From a544146ff0cb10818d1779c4f12240e4b567ed39 Mon Sep 17 00:00:00 2001 From: St John Grimbly Date: Wed, 19 May 2021 10:49:50 +0200 Subject: [PATCH 001/188] feature: basic wrapper outline for PySC2/SMAC. --- mava/wrappers/starcraft.py | 137 +++++++++++++++++++++++++++++++++++++ 1 file changed, 137 insertions(+) create mode 100644 mava/wrappers/starcraft.py diff --git a/mava/wrappers/starcraft.py b/mava/wrappers/starcraft.py new file mode 100644 index 000000000..2917316dc --- /dev/null +++ b/mava/wrappers/starcraft.py @@ -0,0 +1,137 @@ +# python3 +# Copyright 2021 [...placeholder...]. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# See SMAC here: https://github.com/oxwhirl/smac + +"""Wraps a StarCraft II MARL environment (SMAC) as a dm_env environment.""" +import random +from typing import Dict, List, Optional, Tuple, Type + +import numpy as np +from gym.spaces import Box, Discrete +from smac.env import StarCraft2Env # type:ignore + +from mava.wrappers.env_wrappers import ParallelEnvWrapper # , SequentialEnvWrapper + + +# Is it ParallelEnvWrapper or SequentialEnvWrapper +class SMACEnvWrapper(ParallelEnvWrapper): + """ + Wraps a StarCraft II MARL environment (SMAC) as a Mava Parallel environment. + Based on RLlib wrapper provided by SMAC. + """ + + def __init__(self, **smac_args: Optional[Tuple]) -> None: + """Create a new multi-agent StarCraft env compatible with RLlib. + Arguments: + smac_args (dict): Arguments to pass to the underlying + smac.env.starcraft.StarCraft2Env instance. + """ + + self._environment = StarCraft2Env(**smac_args) + self._ready_agents: List = [] + self.observation_space = Dict( + { + "obs": Box(-1, 1, shape=(self._env.get_obs_size(),)), + "action_mask": Box(0, 1, shape=(self._env.get_total_actions(),)), + } + ) + self.action_space: Type[Discrete] = Discrete(self._env.get_total_actions()) + + def reset(self) -> Dict: + """Resets the env and returns observations from ready agents. + Returns: + obs (dict): New observations for each ready agent. + """ + obs_list, state_list = self._env.reset() + return_obs = {} + for i, obs in enumerate(obs_list): + return_obs[i] = { + "action_mask": np.array(self._env.get_avail_agent_actions(i)), + "obs": obs, + } + + self._ready_agents = list(range(len(obs_list))) + return return_obs # TODO return global state for Mixers + + def step(self, action_dict: Dict) -> Tuple: + """Returns observations from ready agents. + The returns are dicts mapping from agent_id strings to values. The + number of agents in the env can vary over time. + Returns + ------- + obs (dict): New observations for each ready agent. + rewards (dict): Reward values for each ready agent. If the + episode is just started, the value will be None. + dones (dict): Done values for each ready agent. The special key + "__all__" (required) is used to indicate env termination. + infos (dict): Optional info values for each agent id. + """ + + actions = [] + for i in self._ready_agents: + if i not in action_dict: + raise ValueError(f"You must supply an action for agent: {i}") + actions.append(action_dict[i]) + + if len(actions) != len(self._ready_agents): + raise ValueError( + f"Number of actions ({len(actions)}) does not match number \ + of ready agents (len(self._ready_agents))." + ) + + rew, done, info = self._env.step(actions) + obs_list = self._env.get_obs() + return_obs = {} + for i, obs in enumerate(obs_list): + return_obs[i] = { + "action_mask": self._env.get_avail_agent_actions(i), + "obs": obs, + } + rews = {i: rew / len(obs_list) for i in range(len(obs_list))} + dones = {i: done for i in range(len(obs_list))} + infos = {i: info for i in range(len(obs_list))} + + self._ready_agents = list(range(len(obs_list))) + return return_obs, rews, dones, infos + + def env_done(self) -> bool: + """ + Returns a bool indicating if all agents in env are done. + """ + return self._environment.env_done # TODO Check SMAC has this function + + @property + def agents(self) -> List: + """ + Returns the active agents in the env. + """ + return NotImplementedError # type:ignore + + @property + def possible_agents(self) -> List: + """ + Returns all the possible agents in the env. + """ + return NotImplementedError # type:ignore + + # Note sure we need these next methods. Comes from RLlib wrapper. + def close(self) -> None: + """Close the environment""" + self._env.close() + + def seed(self, seed: int) -> None: + random.seed(seed) + np.random.seed(seed) From 075dc6d835080986745efaef768901f09f7225fe Mon Sep 17 00:00:00 2001 From: St John Grimbly Date: Wed, 19 May 2021 13:15:47 +0200 Subject: [PATCH 002/188] fix: return timesteps in `reset()` and `step()`. --- mava/wrappers/__init__.py | 1 + mava/wrappers/starcraft.py | 154 ++++++++++++++++++++++++++++++++----- 2 files changed, 134 insertions(+), 21 deletions(-) diff --git a/mava/wrappers/__init__.py b/mava/wrappers/__init__.py index c8c31c85c..b6f9cd8e6 100644 --- a/mava/wrappers/__init__.py +++ b/mava/wrappers/__init__.py @@ -24,6 +24,7 @@ PettingZooAECEnvWrapper, PettingZooParallelEnvWrapper, ) +from mava.wrappers.starcraft import SMACEnvWrapper from mava.wrappers.system_trainer_statistics import ( DetailedTrainerStatistics, NetworkStatisticsActorCritic, diff --git a/mava/wrappers/starcraft.py b/mava/wrappers/starcraft.py index 2917316dc..a01ab1246 100644 --- a/mava/wrappers/starcraft.py +++ b/mava/wrappers/starcraft.py @@ -17,12 +17,18 @@ """Wraps a StarCraft II MARL environment (SMAC) as a dm_env environment.""" import random -from typing import Dict, List, Optional, Tuple, Type +from typing import Any, Dict, List, Optional, Tuple, Type +import dm_env import numpy as np +from acme import specs +from acme.wrappers.gym_wrapper import _convert_to_spec from gym.spaces import Box, Discrete +from pettingzoo.utils.env import ParallelEnv from smac.env import StarCraft2Env # type:ignore +from mava import types +from mava.utils.wrapper_utils import convert_np_type, parameterized_restart from mava.wrappers.env_wrappers import ParallelEnvWrapper # , SequentialEnvWrapper @@ -39,8 +45,9 @@ def __init__(self, **smac_args: Optional[Tuple]) -> None: smac_args (dict): Arguments to pass to the underlying smac.env.starcraft.StarCraft2Env instance. """ - self._environment = StarCraft2Env(**smac_args) + + self._reset_next_step = True self._ready_agents: List = [] self.observation_space = Dict( { @@ -50,23 +57,50 @@ def __init__(self, **smac_args: Optional[Tuple]) -> None: ) self.action_space: Type[Discrete] = Discrete(self._env.get_total_actions()) - def reset(self) -> Dict: + def reset(self) -> Tuple[dm_env.TimeStep, np.array]: """Resets the env and returns observations from ready agents. Returns: obs (dict): New observations for each ready agent. """ + self._reset_next_step = False + self._step_type = dm_env.StepType.FIRST + + # TODO Check the form of this state list and convert for return. obs_list, state_list = self._env.reset() - return_obs = {} + observe: Dict[str, np.ndarray] = {} for i, obs in enumerate(obs_list): - return_obs[i] = { + agent = f"agent_{i}" + observe[agent] = { # TODO Only obs in this Dict or mask too? "action_mask": np.array(self._env.get_avail_agent_actions(i)), "obs": obs, } self._ready_agents = list(range(len(obs_list))) - return return_obs # TODO return global state for Mixers - def step(self, action_dict: Dict) -> Tuple: + discount_spec = self.discount_spec() + self._discounts = { + agent: convert_np_type(discount_spec[agent].dtype, 1) + for agent in self._environment.possible_agents + } + observations = self._convert_observations( + observe, {agent: False for agent in self.possible_agents} + ) + rewards_spec = self.reward_spec() + rewards = { + agent: convert_np_type(rewards_spec[agent].dtype, 0) + for agent in self.possible_agents + } + discount_spec = self.discount_spec() + self._discounts = { + agent: convert_np_type(discount_spec[agent].dtype, 1) + for agent in self.possible_agents + } + + timestep = parameterized_restart(rewards, self._discounts, observations) + + return timestep, {"s_t": state_list} # TODO Convert this to correct form + + def step(self, action_dict: Dict) -> Tuple[dm_env.TimeStep, np.array]: """Returns observations from ready agents. The returns are dicts mapping from agent_id strings to values. The number of agents in the env can vary over time. @@ -79,6 +113,8 @@ def step(self, action_dict: Dict) -> Tuple: "__all__" (required) is used to indicate env termination. infos (dict): Optional info values for each agent id. """ + if self._reset_next_step: + return self.reset() actions = [] for i in self._ready_agents: @@ -92,20 +128,51 @@ def step(self, action_dict: Dict) -> Tuple: of ready agents (len(self._ready_agents))." ) - rew, done, info = self._env.step(actions) + rewards, dones, infos = self._env.step(actions) obs_list = self._env.get_obs() + return_obs = {} for i, obs in enumerate(obs_list): return_obs[i] = { "action_mask": self._env.get_avail_agent_actions(i), "obs": obs, } - rews = {i: rew / len(obs_list) for i in range(len(obs_list))} - dones = {i: done for i in range(len(obs_list))} - infos = {i: info for i in range(len(obs_list))} - + rewards = {i: rewards / len(obs_list) for i in range(len(obs_list))} + dones = {i: dones for i in range(len(obs_list))} + infos = {i: infos for i in range(len(obs_list))} self._ready_agents = list(range(len(obs_list))) - return return_obs, rews, dones, infos + + rewards_spec = self.reward_spec() + + # Handle empty rewards + if not rewards: + rewards = { + agent: convert_np_type(rewards_spec[agent].dtype, 0) + for agent in self.possible_agents + } + else: + rewards = { + agent: convert_np_type(rewards_spec[agent].dtype, reward) + for agent, reward in rewards.items() + } + + if return_obs: + return_obs = self._convert_observations(return_obs, dones) + + if self.env_done(): + self._step_type = dm_env.StepType.LAST + self._reset_next_step = True + else: + self._step_type = dm_env.StepType.MID + + timestep = dm_env.TimeStep( + observation=return_obs, + reward=rewards, + discount=self._discounts, + step_type=self._step_type, + ) + + return timestep # TODO return global state as well def env_done(self) -> bool: """ @@ -113,19 +180,64 @@ def env_done(self) -> bool: """ return self._environment.env_done # TODO Check SMAC has this function + def observation_spec(self) -> types.Observation: + observation_specs = {} + for agent in self._environment.possible_agents: + observation_specs[agent] = types.OLT( + observation=_convert_to_spec( + self._environment.observation_spaces[agent] + ), + legal_actions=_convert_to_spec(self._environment.action_spaces[agent]), + terminal=specs.Array((1,), np.float32), + ) + return observation_specs + + def action_spec(self) -> Dict[str, specs.DiscreteArray]: + action_specs = {} + for agent in self._environment.possible_agents: + action_specs[agent] = _convert_to_spec( + self._environment.action_spaces[agent] + ) + return action_specs + + def reward_spec(self) -> Dict[str, specs.Array]: + reward_specs = {} + for agent in self._environment.possible_agents: + reward_specs[agent] = specs.Array((), np.float32) + + return reward_specs + + def discount_spec(self) -> Dict[str, specs.BoundedArray]: + discount_specs = {} + for agent in self._environment.possible_agents: + discount_specs[agent] = specs.BoundedArray( + (), np.float32, minimum=0, maximum=1.0 + ) + return discount_specs + + def extra_spec(self) -> Dict[str, specs.BoundedArray]: + return {} + @property def agents(self) -> List: - """ - Returns the active agents in the env. - """ - return NotImplementedError # type:ignore + return self._environment.agents @property def possible_agents(self) -> List: - """ - Returns all the possible agents in the env. - """ - return NotImplementedError # type:ignore + return self._environment.possible_agents + + @property + def environment(self) -> ParallelEnv: + """Returns the wrapped environment.""" + return self._environment + + @property + def current_agent(self) -> Any: + return self._environment.agent_selection + + def __getattr__(self, name: str) -> Any: + """Expose any other attributes of the underlying environment.""" + return getattr(self._environment, name) # Note sure we need these next methods. Comes from RLlib wrapper. def close(self) -> None: From 873929b0499ee99837e93f3d9445dc43072a22ec Mon Sep 17 00:00:00 2001 From: St John Grimbly Date: Wed, 19 May 2021 13:18:59 +0200 Subject: [PATCH 003/188] Add documentation comment. --- mava/wrappers/starcraft.py | 1 + 1 file changed, 1 insertion(+) diff --git a/mava/wrappers/starcraft.py b/mava/wrappers/starcraft.py index a01ab1246..6f7cda7d5 100644 --- a/mava/wrappers/starcraft.py +++ b/mava/wrappers/starcraft.py @@ -14,6 +14,7 @@ # limitations under the License. # See SMAC here: https://github.com/oxwhirl/smac +# Documentation available at smac/blob/master/docs/smac.md """Wraps a StarCraft II MARL environment (SMAC) as a dm_env environment.""" import random From e148db49609cb664542b5f93a5026ff920c92ed0 Mon Sep 17 00:00:00 2001 From: St John Grimbly Date: Thu, 20 May 2021 09:41:11 +0200 Subject: [PATCH 004/188] documentation: add instructions for SCII env. --- README.md | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/README.md b/README.md index 67587e211..1f3ae3a7b 100644 --- a/README.md +++ b/README.md @@ -124,6 +124,15 @@ We have tested `mava` on Python 3.6, 3.7 and 3.8. pip install .[flatland] ``` + NB: For StarCraft II installation, this must be installed separately according to your operating system. + To install the StarCraft II ML environment and associated packages, please follow the instructions on [PySC2](https://github.com/deepmind/pysc2) to install the StarCraft II game files. + Please ensure you have the required game maps (for both PySC2 and SMAC) extracted in the StarCraft II maps directory. + Once this is done you can install the packages for the single agent case (PySC2) and the multi-agent case (SMAC). + ```bash + pip install pysc2 + pip install git+https://github.com/oxwhirl/smac.git + ``` + We also have a list of [optional installs](OPTIONAL_INSTALL.md) for extra functionality such as the use of Atari environments, environment wrappers, gpu support and agent episode recording. ## Contributing From 8287e30046ebb75bf06ac21f5491097622779652 Mon Sep 17 00:00:00 2001 From: Kevin Eloff Date: Thu, 20 May 2021 12:04:22 +0200 Subject: [PATCH 005/188] feat: update dial trainer --- mava/components/tf/networks/dial.py | 2 +- mava/systems/tf/dial/training.py | 176 ++++++++++++++++------------ 2 files changed, 99 insertions(+), 79 deletions(-) diff --git a/mava/components/tf/networks/dial.py b/mava/components/tf/networks/dial.py index 21193c9fc..71b2d79ff 100644 --- a/mava/components/tf/networks/dial.py +++ b/mava/components/tf/networks/dial.py @@ -91,7 +91,7 @@ def __call__( if state is None: state = self.initial_state() if message is None: - message = tf.zeros((1, self._message_dim), dtype=tf.float32) + message = tf.zeros((x.shape[0], self._message_dim), dtype=tf.float32) x_task = self.task_mlp(x) x_message = self.message_in_mlp(message) diff --git a/mava/systems/tf/dial/training.py b/mava/systems/tf/dial/training.py index ed987b02d..fb65f2823 100644 --- a/mava/systems/tf/dial/training.py +++ b/mava/systems/tf/dial/training.py @@ -233,42 +233,67 @@ def _forward(self, inputs: Any) -> None: core_states = extra["core_states"] - bs = actions[self._agents[0]].shape[1] - - self.bs = bs - - T = actions[self._agents[0]].shape[0] + self.bs = actions[self._agents[0]].shape[1] # Batch Size + T = actions[self._agents[0]].shape[0] # Episode Length with tf.GradientTape(persistent=True) as tape: - policy_losses = {agent_id: tf.zeros(bs) for agent_id in self._agents} - # for each batch - # for b in range(bs): - # Unroll episode and store states, messages + policy_losses = {agent_id: tf.zeros(self.bs) for agent_id in self._agents} - messages = { - 0: { + # Unroll episode and store states, messages, action values + # for policy and target policy + policy = {} + target_policy = {} + + policy["messages"] = { + -1: { agent_id: core_states[agent_id]["message"][0] for agent_id in self._agents } - } # index of time t - - channel = {} + } + policy["states"] = { + -1: { + agent_id: core_states[agent_id]["state"][0] + for agent_id in self._agents + } + } + policy["actions"] = {} + policy["channel"] = {} - states = { - 0: { + target_policy["messages"] = { + -1: { + agent_id: core_states[agent_id]["message"][0] + for agent_id in self._agents + } + } + target_policy["states"] = { + -1: { agent_id: core_states[agent_id]["state"][0] for agent_id in self._agents } - } # index of time t + } + target_policy["actions"] = {} + target_policy["channel"] = {} # For all time-steps for t in range(0, T, 1): - channel[t] = self._communication_module.process_messages(messages[t])[ + policy["channel"][t - 1] = self._communication_module.process_messages( + policy["messages"][t - 1] + )[self._agents[0]] + target_policy["channel"][ + t - 1 + ] = self._communication_module.process_messages( + target_policy["messages"][t - 1] + )[ self._agents[0] ] - messages[t + 1] = {} - states[t + 1] = {} + policy["messages"][t] = {} + policy["states"][t] = {} + policy["actions"][t] = {} + + target_policy["messages"][t] = {} + target_policy["states"][t] = {} + target_policy["actions"][t] = {} # For each agent for agent_id in self._agents: @@ -276,90 +301,83 @@ def _forward(self, inputs: Any) -> None: # Agent input at time t obs_in = observations[agent_id].observation[t] - state_in = states[t][agent_id] - message_in = channel[t] + # Policy + state_in = policy["states"][t - 1][agent_id] + message_in = policy["channel"][t - 1] - batched_observation = obs_in - batched_state = state_in - batched_message = message_in + (action, message), state = self._policy_networks[agent_type]( + obs_in, state_in, message_in + ) + + # Target policy + target_state_in = policy["states"][t - 1][agent_id] + target_message_in = policy["channel"][t - 1] - (q_t, m_t), s_t = self._policy_networks[agent_type]( - batched_observation, batched_state, batched_message + ( + target_action, + target_message, + ), target_state = self._target_policy_networks[agent_type]( + obs_in, target_state_in, target_message_in ) - # TODO (dries): Why was this here? Is it still needed? - # if obs_in[0] == 0: - # m_t = tf.zeros_like(m_t) + policy["messages"][t][agent_id] = message + policy["states"][t][agent_id] = state + policy["actions"][t][agent_id] = action + + target_policy["messages"][t][agent_id] = target_message + target_policy["states"][t][agent_id] = target_state + target_policy["actions"][t][agent_id] = target_action - messages[t + 1][agent_id] = m_t - states[t + 1][agent_id] = s_t + # policy['channel'][t] = self._communication_module.process_messages( + # policy['messages'][t] + # )[self._agents[0]] - channel[t + 1] = self._communication_module.process_messages( - messages[t + 1] - )[self._agents[0]] + # target_policy['channel'][t] = self._communication_module.process_messages( + # target_policy['messages'][t] + # )[self._agents[0]] - # Backtrack episode + # Backtrack episode and calculate loss # For t=T to 1, -1 do for t in range(T - 1, 0, -1): # For each agent a do for agent_id in self._agents: - agent_type = self.agent_net_keys[agent_id] - - # All at timestep t - agent_input = observations[agent_id].observation - # (sequence,batch,1) - next_message = channel[t] - message = channel[t - 1] - next_state = states[t][agent_id] - state = states[t - 1][agent_id] - - action = actions[agent_id] - - reward = rewards[agent_id] + # agent_type = self.agent_net_keys[agent_id] + # All at timestep t - 1 + action = actions[agent_id][t - 1] + message = core_states[agent_id]["message"][t - 1] + reward = rewards[agent_id][t - 1] + terminal = t == T - 1 discount = tf.cast( self._discount, dtype=discounts[agent_id][t, 0].dtype ) - terminal = t == T - 1 - # y_t_a = r_t - y_action = reward[t - 1] - y_message = reward[t - 1] + y_action = reward + y_message = reward # y_t_a = r_t + discount * max_u Q(t) if not terminal: - batched_observation = agent_input[t] - batched_state = next_state - batched_message = next_message - - (q_t, m_t), s = self._target_policy_networks[agent_type]( - batched_observation, batched_state, batched_message + y_action += discount * tf.reduce_max( + target_policy["actions"][t][agent_id] + ) + y_message += discount * tf.reduce_max( + target_policy["messages"][t][agent_id] ) - - y_action += discount * tf.reduce_max(q_t) - y_message += discount * tf.reduce_max(m_t) - # y_message += discount * m_t # d_Q_t_a = y_t_a - Q(t-1) - batched_observation = agent_input[t - 1] - batched_state = state - batched_message = message - - # TODO (dries): Why is the policy values calculated - # again? Was this not done in the first loop? - (q_t1, m_t1), s = self._policy_networks[agent_type]( - batched_observation, batched_state, batched_message + td_action = y_action - tf.gather( + policy["actions"][t - 1][agent_id], action, batch_dims=1 ) - td_action = y_action - tf.gather(q_t1, action[t - 1], batch_dims=1) - # d_theta = d_theta + d_Q_t_a ^ 2 policy_losses[agent_id] += td_action ** 2 # Communication grads td_comm = y_message - tf.gather( - m_t1, tf.argmax(next_message, axis=-1), batch_dims=1 + policy["messages"][t - 1][agent_id], + tf.argmax(message, axis=-1), + batch_dims=1, ) policy_losses[agent_id] += td_comm ** 2 @@ -368,6 +386,7 @@ def _forward(self, inputs: Any) -> None: policy_losses[key] = { "policy_loss": tf.reduce_mean(policy_losses[key], axis=0) } + self.policy_losses: Dict[str, Dict[str, tf.Tensor]] = policy_losses self.tape = tape @@ -384,10 +403,11 @@ def _backward(self) -> None: agent_key = self.agent_net_keys[agent] # Get trainable variables. - policy_variables = ( - self._observation_networks[agent_key].trainable_variables - + self._policy_networks[agent_key].trainable_variables - ) + # policy_variables = ( + # self._observation_networks[agent_key].trainable_variables + # + self._policy_networks[agent_key].trainable_variables + # ) + policy_variables = self._policy_networks[agent_key].trainable_variables # Compute gradients. # Note: Warning "WARNING:tensorflow:Calling GradientTape.gradient From af0f46f2d7f9c1bb25455300b84091ddd83f30dc Mon Sep 17 00:00:00 2001 From: Kevin Eloff Date: Thu, 20 May 2021 15:32:59 +0200 Subject: [PATCH 006/188] fix: target network --- mava/systems/tf/dial/training.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/mava/systems/tf/dial/training.py b/mava/systems/tf/dial/training.py index fb65f2823..c7207506c 100644 --- a/mava/systems/tf/dial/training.py +++ b/mava/systems/tf/dial/training.py @@ -310,8 +310,8 @@ def _forward(self, inputs: Any) -> None: ) # Target policy - target_state_in = policy["states"][t - 1][agent_id] - target_message_in = policy["channel"][t - 1] + target_state_in = target_policy["states"][t - 1][agent_id] + target_message_in = target_policy["channel"][t - 1] ( target_action, @@ -328,13 +328,13 @@ def _forward(self, inputs: Any) -> None: target_policy["states"][t][agent_id] = target_state target_policy["actions"][t][agent_id] = target_action - # policy['channel'][t] = self._communication_module.process_messages( - # policy['messages'][t] - # )[self._agents[0]] + policy["channel"][t] = self._communication_module.process_messages( + policy["messages"][t] + )[self._agents[0]] - # target_policy['channel'][t] = self._communication_module.process_messages( - # target_policy['messages'][t] - # )[self._agents[0]] + target_policy["channel"][t] = self._communication_module.process_messages( + target_policy["messages"][t] + )[self._agents[0]] # Backtrack episode and calculate loss # For t=T to 1, -1 do From 5d9e0b0c1fdfd279a8b9c0c3cce4aa22024bfde1 Mon Sep 17 00:00:00 2001 From: Dries Date: Fri, 21 May 2021 13:34:51 +0200 Subject: [PATCH 007/188] feature: Save progress. --- ...centralised_feedforward_discrete_maddpg.py | 217 ++++++++++++++++++ mava/systems/tf/maddpg/execution.py | 141 ++++++++++++ mava/systems/tf/maddpg/training.py | 4 +- mava/systems/tf/mappo/execution.py | 3 +- 4 files changed, 362 insertions(+), 3 deletions(-) create mode 100644 examples/debugging_envs/run_decentralised_feedforward_discrete_maddpg.py create mode 100644 mava/systems/tf/maddpg/execution.py diff --git a/examples/debugging_envs/run_decentralised_feedforward_discrete_maddpg.py b/examples/debugging_envs/run_decentralised_feedforward_discrete_maddpg.py new file mode 100644 index 000000000..c2eb6b582 --- /dev/null +++ b/examples/debugging_envs/run_decentralised_feedforward_discrete_maddpg.py @@ -0,0 +1,217 @@ +# python3 +# Copyright 2021 [...placeholder...]. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Example running feedforward MADDPG on debug MPE environments.""" + +import functools +from datetime import datetime +from typing import Any, Dict, Mapping, Sequence, Union + +from dm_env import specs + +Array = specs.Array +BoundedArray = specs.BoundedArray +DiscreteArray = specs.DiscreteArray +import launchpad as lp +import numpy as np +import sonnet as snt +import tensorflow as tf +from absl import app, flags +from acme import types +from acme.tf import networks +from acme.tf import utils as tf2_utils +from launchpad.nodes.python.local_multi_processing import PythonProcess + +from mava import specs as mava_specs +from mava.systems.tf import maddpg +from mava.systems.tf.maddpg.execution import MADDPGDiscreteFeedForwardExecutor +from mava.utils import lp_utils +from mava.utils.environments import debugging_utils +from mava.utils.loggers import logger_utils + +FLAGS = flags.FLAGS +flags.DEFINE_string( + "env_name", + "simple_spread", + "Debugging environment name (str).", +) +flags.DEFINE_string( + "action_space", + "discrete", + "Environment action space type (str).", +) +flags.DEFINE_string( + "mava_id", + str(datetime.now()), + "Experiment identifier that can be used to continue experiments.", +) +flags.DEFINE_string("base_dir", "~/mava/", "Base dir to store experiments.") + + +def make_networks( + environment_spec: mava_specs.MAEnvironmentSpec, + policy_networks_layer_sizes: Union[Dict[str, Sequence], Sequence] = ( + 256, + 256, + 256, + ), + critic_networks_layer_sizes: Union[Dict[str, Sequence], Sequence] = (512, 512, 256), + shared_weights: bool = True, + sigma: float = 0.3, +) -> Mapping[str, types.TensorTransformation]: + """Creates networks used by the agents.""" + specs = environment_spec.get_agent_specs() + + # Create agent_type specs + if shared_weights: + type_specs = {key.split("_")[0]: specs[key] for key in specs.keys()} + specs = type_specs + + if isinstance(policy_networks_layer_sizes, Sequence): + policy_networks_layer_sizes = { + key: policy_networks_layer_sizes for key in specs.keys() + } + if isinstance(critic_networks_layer_sizes, Sequence): + critic_networks_layer_sizes = { + key: critic_networks_layer_sizes for key in specs.keys() + } + + observation_networks = {} + policy_networks = {} + critic_networks = {} + for key in specs.keys(): + + # TODO (dries): Make specs[key].actions return a list of specs for hybrid action space + + # Get total number of action dimensions from action spec. + if type(specs[key].actions) == DiscreteArray: + num_act_dimensions = specs[key].actions.num_values + elif type(specs[key].actions) == BoundedArray: + num_act_dimensions = np.prod(specs[key].actions.shape, dtype=int) + else: + raise NotImplementedError + + # Create the shared observation network; here simply a state-less operation. + observation_network = tf2_utils.to_sonnet_module(tf.identity) + + # Create the policy network. + policy_network = snt.Sequential( + [ + networks.LayerNormMLP( + policy_networks_layer_sizes[key], activate_final=True + ), + networks.NearZeroInitializedLinear(num_act_dimensions), + networks.TanhToSpec(specs[key].actions), + networks.ClippedGaussian(sigma), + networks.ClipToSpec(specs[key].actions), + ] + ) + + # Create the critic network. + critic_network = snt.Sequential( + [ + # The multiplexer concatenates the observations/actions. + networks.CriticMultiplexer(), + networks.LayerNormMLP( + critic_networks_layer_sizes[key], activate_final=False + ), + snt.Linear(1), + ] + ) + observation_networks[key] = observation_network + policy_networks[key] = policy_network + critic_networks[key] = critic_network + + return { + "policies": policy_networks, + "critics": critic_networks, + "observations": observation_networks, + } + + +def main(_: Any) -> None: + # environment + environment_factory = functools.partial( + debugging_utils.make_environment, + env_name=FLAGS.env_name, + action_space=FLAGS.action_space, + ) + + # networks + network_factory = lp_utils.partial_kwargs(make_networks) + + # loggers + # set custom logger config for each process + # -- log trainer,executor and evaluator to TF + # -- log only evaluator to terminal + log_every = 10 + logger_config = { + "trainer": { + "directory": FLAGS.base_dir, + "to_terminal": False, + "to_tensorboard": True, + "time_stamp": FLAGS.mava_id, + "time_delta": log_every, + }, + "executor": { + "directory": FLAGS.base_dir, + "to_terminal": False, + "to_tensorboard": True, + "time_stamp": FLAGS.mava_id, + "time_delta": log_every, + }, + "evaluator": { + "directory": FLAGS.base_dir, + "to_terminal": True, + "to_tensorboard": True, + "time_stamp": FLAGS.mava_id, + "time_delta": log_every, + }, + } + + # Checkpointer appends "Checkpoints" to checkpoint_dir + checkpoint_dir = f"{FLAGS.base_dir}/{FLAGS.mava_id}" + + # distributed program + program = maddpg.MADDPG( + environment_factory=environment_factory, + network_factory=network_factory, + logger_config=logger_config, + num_executors=2, + executor_fn=MADDPGDiscreteFeedForwardExecutor, + policy_optimizer=snt.optimizers.Adam(learning_rate=1e-4), + critic_optimizer=snt.optimizers.Adam(learning_rate=1e-4), + checkpoint_subpath=checkpoint_dir, + max_gradient_norm=40.0, + ).build() + + # launch + gpu_id = -1 + env_vars = {"CUDA_VISIBLE_DEVICES": str(gpu_id)} + local_resources = { + "trainer": [], + "evaluator": PythonProcess(env=env_vars), + "executor": PythonProcess(env=env_vars), + } + lp.launch( + program, + lp.LaunchType.LOCAL_MULTI_PROCESSING, + terminal="current_terminal", + local_resources=local_resources, + ) + + +if __name__ == "__main__": + app.run(main) diff --git a/mava/systems/tf/maddpg/execution.py b/mava/systems/tf/maddpg/execution.py new file mode 100644 index 000000000..0c43708ff --- /dev/null +++ b/mava/systems/tf/maddpg/execution.py @@ -0,0 +1,141 @@ +# python3 +# Copyright 2021 [...placeholder...]. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Any, Dict, Optional, Tuple + +import dm_env +import numpy as np +import sonnet as snt +import tensorflow as tf +import tensorflow_probability as tfp +from acme import types + +# Internal imports. +from acme.tf import utils as tf2_utils +from acme.tf import variable_utils as tf2_variable_utils + +from mava import adders, core +from mava.systems import executors + +tfd = tfp.distributions + + +class MADDPGDiscreteFeedForwardExecutor(executors.FeedForwardExecutor): + """A feed-forward executor for discrete actions in MADDPG. + An executor based on a feed-forward policy for each agent in the system + which takes non-batched observations and outputs non-batched actions. + It also allows adding experiences to replay and updating the weights + from the policy on the learner. + """ + + def __init__( + self, + policy_networks: Dict[str, snt.Module], + adder: Optional[adders.ParallelAdder] = None, + variable_client: Optional[tf2_variable_utils.VariableClient] = None, + shared_weights: bool = True, + ): + + """Initializes the executor. + Args: + networks: the (recurrent) policy to run for each agent in the system. + shared_weights: specify if weights are shared between agent networks. + adder: the adder object to which allows to add experiences to a + dataset/replay buffer. + variable_client: object which allows to copy weights from the trainer copy + of the policies to the executor copy (in case they are separate). + """ + + # Store these for later use. + self._adder = adder + self._variable_client = variable_client + self._policy_networks = policy_networks + self._shared_weights = shared_weights + self.self._policy_outputs: Dict[str, Any] = {} + + @tf.function + def _policy( + self, agent: str, observation: types.NestedTensor + ) -> types.NestedTensor: + + # Add a dummy batch dimension and as a side effect convert numpy to TF. + batched_observation = tf2_utils.add_batch_dim(observation) + + # index network either on agent type or on agent id + agent_key = agent.split("_")[0] if self._shared_weights else agent + + # Compute the policy, conditioned on the observation. + policy = self._policy_networks[agent_key](batched_observation) + + # Sample from the policy if it is stochastic. + action = tf.math.argmax(policy) + + return action, policy + + def select_action( + self, agent: str, observation: types.NestedArray + ) -> types.NestedArray: + + # Step the recurrent policy/value network forward + # given the current observation and state. + action, self._policy_outputs[agent] = self._policy(agent, observation) + + # Return a numpy array with squeezed out batch dimension. + action = tf2_utils.to_numpy_squeeze(action) + return action + + def select_actions( + self, observations: Dict[str, types.NestedArray] + ) -> Dict[str, types.NestedArray]: + + actions = {} + for agent, observation in observations.items(): + action = self.select_action(agent, observation) + actions[agent] = action + + return actions + + def observe_first( + self, + timestep: dm_env.TimeStep, + extras: Dict[str, types.NestedArray] = {}, + ) -> None: + + if self._adder: + + # Generate dummy policy values to send through + self.select_actions(timestep.observation) + extras.update({"policy_out": self._policy_outputs}) + self._adder.add_first(timestep) + + def observe( + self, + actions: Dict[str, types.NestedArray], + next_timestep: dm_env.TimeStep, + next_extras: Dict[str, types.NestedArray] = {}, + ) -> None: + + if not self._adder: + return + + next_extras.update({"policy_out": self._policy_outputs}) + + next_extras = tf2_utils.to_numpy_squeeze(next_extras) + + self._adder.add(actions, next_timestep, next_extras) + + def update(self, wait: bool = False) -> None: + if self._variable_client: + self._variable_client.update(wait) diff --git a/mava/systems/tf/maddpg/training.py b/mava/systems/tf/maddpg/training.py index 63057fd23..2bfe259c9 100644 --- a/mava/systems/tf/maddpg/training.py +++ b/mava/systems/tf/maddpg/training.py @@ -309,7 +309,9 @@ def _forward(self, inputs: Any) -> None: # This discount is applied to future rewards after r_t. # o_t = dictionary of next observations or next observation sequences # e_t [Optional] = extra data for timestep t that the agents persist in replay. - o_tm1, a_tm1, e_tm1, r_t, d_t, o_t, e_t = inputs.data + o_tm1, _, e_tm1, r_t, d_t, o_t, e_t = inputs.data + + a_tm1 = e_tm1["policy_outputs"] self.policy_losses = {} self.critic_losses = {} diff --git a/mava/systems/tf/mappo/execution.py b/mava/systems/tf/mappo/execution.py index 38ac4f3f9..eb65b00b0 100644 --- a/mava/systems/tf/mappo/execution.py +++ b/mava/systems/tf/mappo/execution.py @@ -62,7 +62,6 @@ def __init__( self._variable_client = variable_client self._policy_networks = policy_networks self._shared_weights = shared_weights - self._prev_log_probs: Dict[str, Any] = {} @tf.function @@ -85,7 +84,7 @@ def _policy( log_prob = policy.log_prob(action) # Cast for compatibility with reverb. - # smaple() returns a 'int32', which is a problem. + # sample() returns a 'int32', which is a problem. if isinstance(policy, tfp.distributions.Categorical): action = tf.cast(action, "int64") From 518b5bea71cb477bcef49a94f8259713b2e096de Mon Sep 17 00:00:00 2001 From: Dries Date: Fri, 21 May 2021 16:22:14 +0200 Subject: [PATCH 008/188] feature: Save changes. --- mava/environment_loop.py | 9 ++++++++- mava/systems/tf/maddpg/execution.py | 19 ++++++++----------- 2 files changed, 16 insertions(+), 12 deletions(-) diff --git a/mava/environment_loop.py b/mava/environment_loop.py index 65b4860e9..3e9e7e755 100644 --- a/mava/environment_loop.py +++ b/mava/environment_loop.py @@ -280,7 +280,14 @@ def run_episode(self) -> loggers.LoggingData: # Generate an action from the agent's policy and step the environment. actions = self._get_actions(timestep) - timestep = self._environment.step(actions) + + if type(actions) == tuple: + # Return other extra action information like the policy information as well + env_actions, _ = actions + else: + env_actions = actions + + timestep = self._environment.step(env_actions) if type(timestep) == tuple: timestep, env_extras = timestep diff --git a/mava/systems/tf/maddpg/execution.py b/mava/systems/tf/maddpg/execution.py index 0c43708ff..c0dd02097 100644 --- a/mava/systems/tf/maddpg/execution.py +++ b/mava/systems/tf/maddpg/execution.py @@ -63,8 +63,6 @@ def __init__( self._variable_client = variable_client self._policy_networks = policy_networks self._shared_weights = shared_weights - self.self._policy_outputs: Dict[str, Any] = {} - @tf.function def _policy( self, agent: str, observation: types.NestedTensor @@ -90,22 +88,24 @@ def select_action( # Step the recurrent policy/value network forward # given the current observation and state. - action, self._policy_outputs[agent] = self._policy(agent, observation) + action, policy = self._policy(agent, observation) # Return a numpy array with squeezed out batch dimension. action = tf2_utils.to_numpy_squeeze(action) - return action + return [action, policy] def select_actions( self, observations: Dict[str, types.NestedArray] ) -> Dict[str, types.NestedArray]: actions = {} + policies = {} for agent, observation in observations.items(): - action = self.select_action(agent, observation) - actions[agent] = action + action, policy = self.select_action(agent, observation) + actions[agent] = action[0] + policies[agent] = policy - return actions + return [actions, policies] def observe_first( self, @@ -116,8 +116,6 @@ def observe_first( if self._adder: # Generate dummy policy values to send through - self.select_actions(timestep.observation) - extras.update({"policy_out": self._policy_outputs}) self._adder.add_first(timestep) def observe( @@ -130,8 +128,7 @@ def observe( if not self._adder: return - next_extras.update({"policy_out": self._policy_outputs}) - + self.adder.next_extras.update({"policy_out": self._policy_outputs}) next_extras = tf2_utils.to_numpy_squeeze(next_extras) self._adder.add(actions, next_timestep, next_extras) From 9113abf19297eac1858a424c9f7319e8a3d04dec Mon Sep 17 00:00:00 2001 From: St John Grimbly Date: Fri, 21 May 2021 21:18:22 +0200 Subject: [PATCH 009/188] fix: add SCII envs to tests. --- mava/wrappers/starcraft.py | 40 ++++++++++++++++---------------------- setup.py | 13 ++++++++++++- 2 files changed, 29 insertions(+), 24 deletions(-) diff --git a/mava/wrappers/starcraft.py b/mava/wrappers/starcraft.py index 6f7cda7d5..b838bcbe1 100644 --- a/mava/wrappers/starcraft.py +++ b/mava/wrappers/starcraft.py @@ -30,10 +30,9 @@ from mava import types from mava.utils.wrapper_utils import convert_np_type, parameterized_restart -from mava.wrappers.env_wrappers import ParallelEnvWrapper # , SequentialEnvWrapper +from mava.wrappers.env_wrappers import ParallelEnvWrapper -# Is it ParallelEnvWrapper or SequentialEnvWrapper class SMACEnvWrapper(ParallelEnvWrapper): """ Wraps a StarCraft II MARL environment (SMAC) as a Mava Parallel environment. @@ -182,39 +181,34 @@ def env_done(self) -> bool: return self._environment.env_done # TODO Check SMAC has this function def observation_spec(self) -> types.Observation: - observation_specs = {} - for agent in self._environment.possible_agents: - observation_specs[agent] = types.OLT( + return { + agent: types.OLT( observation=_convert_to_spec( self._environment.observation_spaces[agent] ), legal_actions=_convert_to_spec(self._environment.action_spaces[agent]), terminal=specs.Array((1,), np.float32), ) - return observation_specs + for agent in self._environment.possible_agents + } def action_spec(self) -> Dict[str, specs.DiscreteArray]: - action_specs = {} - for agent in self._environment.possible_agents: - action_specs[agent] = _convert_to_spec( - self._environment.action_spaces[agent] - ) - return action_specs + return { + agent: _convert_to_spec(self._environment.action_spaces[agent]) + for agent in self._environment.possible_agents + } def reward_spec(self) -> Dict[str, specs.Array]: - reward_specs = {} - for agent in self._environment.possible_agents: - reward_specs[agent] = specs.Array((), np.float32) - - return reward_specs + return { + agent: specs.Array((), np.float32) + for agent in self._environment.possible_agents + } def discount_spec(self) -> Dict[str, specs.BoundedArray]: - discount_specs = {} - for agent in self._environment.possible_agents: - discount_specs[agent] = specs.BoundedArray( - (), np.float32, minimum=0, maximum=1.0 - ) - return discount_specs + return { + agent: specs.BoundedArray((), np.float32, minimum=0, maximum=1.0) + for agent in self._environment.possible_agents + } def extra_spec(self) -> Dict[str, specs.BoundedArray]: return {} diff --git a/setup.py b/setup.py index a01f5ac8b..12099540b 100644 --- a/setup.py +++ b/setup.py @@ -39,7 +39,18 @@ "trfl", ] -env_requirements = ["pettingzoo>=1.8.2", "multi_agent_ale_py", "supersuit", "pygame"] +# NOTE (St John, for Kale-ab) The SMAC installed when callling `pip install +# smac` seems to be a different package. We actually need to install SMAC +# via pip `install git+https://github.com/oxwhirl/smac.git`. PySC2 is the +# single agent env for StarCraft II, required by SMAC. +env_requirements = [ + "pettingzoo>=1.8.2", + "multi_agent_ale_py", + "supersuit", + "pygame", + "pysc2", + "smac", +] launchpad_requirements = [ "dm-launchpad", From 33798716ba8e7551e47546251e5086ffdea828b1 Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Sat, 22 May 2021 10:19:02 +0200 Subject: [PATCH 010/188] update ci to run on dev branch --- .github/workflows/ci.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index fcd48ea70..8abedcba0 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -2,9 +2,9 @@ name: format_and_test on: push: - branches: [main] + branches: [develop] pull_request: - branches: [main] + branches: [develop] jobs: test-ubuntu: From 54241e2fc41798923f87f56265cb376a62f43338 Mon Sep 17 00:00:00 2001 From: Dries Date: Sat, 22 May 2021 11:42:18 +0200 Subject: [PATCH 011/188] feature: Add working discrete action space examples of MA-DDPG and MA-D4PG. --- ...centralised_feedforward_discrete_mad4pg.py | 210 ++++++++++++++++++ ...centralised_feedforward_discrete_maddpg.py | 42 ++-- mava/core.py | 7 +- mava/systems/tf/executors.py | 9 +- mava/systems/tf/mad4pg/execution.py | 45 ++++ mava/systems/tf/maddpg/builder.py | 39 +++- mava/systems/tf/maddpg/execution.py | 50 ++--- mava/systems/tf/maddpg/system.py | 5 +- mava/systems/tf/maddpg/training.py | 4 +- 9 files changed, 352 insertions(+), 59 deletions(-) create mode 100644 examples/debugging_envs/run_decentralised_feedforward_discrete_mad4pg.py create mode 100644 mava/systems/tf/mad4pg/execution.py diff --git a/examples/debugging_envs/run_decentralised_feedforward_discrete_mad4pg.py b/examples/debugging_envs/run_decentralised_feedforward_discrete_mad4pg.py new file mode 100644 index 000000000..3473c4ac3 --- /dev/null +++ b/examples/debugging_envs/run_decentralised_feedforward_discrete_mad4pg.py @@ -0,0 +1,210 @@ +# python3 +# Copyright 2021 [...placeholder...]. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Example running MAD4PG on debug MPE environments.""" +import functools +from datetime import datetime +from typing import Any, Dict, Mapping, Sequence, Union + +import launchpad as lp +import numpy as np +import sonnet as snt +from absl import app, flags +from acme import types +from acme.tf import networks +from acme.tf import utils as tf2_utils +from dm_env import specs +from launchpad.nodes.python.local_multi_processing import PythonProcess + +from mava import specs as mava_specs +from mava.components.tf.networks.mad4pg import DiscreteValuedHead +from mava.systems.tf import mad4pg +from mava.systems.tf.mad4pg.execution import MAD4PGDiscreteFeedForwardExecutor +from mava.utils import lp_utils +from mava.utils.environments import debugging_utils +from mava.utils.loggers import logger_utils + +Array = specs.Array +BoundedArray = specs.BoundedArray +DiscreteArray = specs.DiscreteArray + +FLAGS = flags.FLAGS +flags.DEFINE_string( + "env_name", + "simple_spread", + "Debugging environment name (str).", +) +flags.DEFINE_string( + "action_space", + "discrete", + "Environment action space type (str).", +) +flags.DEFINE_string( + "mava_id", + str(datetime.now()), + "Experiment identifier that can be used to continue experiments.", +) +flags.DEFINE_string("base_dir", "~/mava/", "Base dir to store experiments.") + + +def make_networks( + environment_spec: mava_specs.MAEnvironmentSpec, + policy_networks_layer_sizes: Union[Dict[str, Sequence], Sequence] = ( + 256, + 256, + 256, + ), + critic_networks_layer_sizes: Union[Dict[str, Sequence], Sequence] = (512, 512, 256), + shared_weights: bool = True, + sigma: float = 0.3, + vmin: float = -150.0, + vmax: float = 150.0, + num_atoms: int = 51, +) -> Mapping[str, types.TensorTransformation]: + """Creates networks used by the agents.""" + specs = environment_spec.get_agent_specs() + + # Create agent_type specs + if shared_weights: + type_specs = {key.split("_")[0]: specs[key] for key in specs.keys()} + specs = type_specs + + if isinstance(policy_networks_layer_sizes, Sequence): + policy_networks_layer_sizes = { + key: policy_networks_layer_sizes for key in specs.keys() + } + if isinstance(critic_networks_layer_sizes, Sequence): + critic_networks_layer_sizes = { + key: critic_networks_layer_sizes for key in specs.keys() + } + + observation_networks = {} + policy_networks = {} + critic_networks = {} + for key in specs.keys(): + # TODO (dries): Make specs[key].actions + # return a list of specs for hybrid action space + # Get total number of action dimensions from action spec. + agent_act_spec = specs[key].actions + if type(specs[key].actions) == DiscreteArray: + num_actions = agent_act_spec.num_values + minimum = [-1.0] * num_actions + maximum = [1.0] * num_actions + agent_act_spec = BoundedArray( + shape=(num_actions,), + minimum=minimum, + maximum=maximum, + dtype="float32", + name="actions", + ) + + # Get total number of action dimensions from action spec. + num_dimensions = np.prod(agent_act_spec.shape, dtype=int) + + # Create the shared observation network; here simply a state-less operation. + observation_network = tf2_utils.to_sonnet_module(tf2_utils.batch_concat) + + # Create the policy network. + policy_network = snt.Sequential( + [ + networks.LayerNormMLP( + policy_networks_layer_sizes[key], activate_final=True + ), + networks.NearZeroInitializedLinear(num_dimensions), + networks.TanhToSpec(agent_act_spec), + networks.ClippedGaussian(sigma), + networks.ClipToSpec(agent_act_spec), + ] + ) + + # Create the critic network. + critic_network = snt.Sequential( + [ + # The multiplexer concatenates the observations/actions. + networks.CriticMultiplexer(), + networks.LayerNormMLP( + critic_networks_layer_sizes[key], activate_final=False + ), + DiscreteValuedHead(vmin, vmax, num_atoms), + ] + ) + observation_networks[key] = observation_network + policy_networks[key] = policy_network + critic_networks[key] = critic_network + + return { + "policies": policy_networks, + "critics": critic_networks, + "observations": observation_networks, + } + + +def main(_: Any) -> None: + + # environment + environment_factory = lp_utils.partial_kwargs( + debugging_utils.make_environment, + env_name=FLAGS.env_name, + action_space=FLAGS.action_space, + ) + + # networks + network_factory = lp_utils.partial_kwargs(make_networks) + + # Checkpointer appends "Checkpoints" to checkpoint_dir + checkpoint_dir = f"{FLAGS.base_dir}/{FLAGS.mava_id}" + + # loggers + log_every = 10 + logger_factory = functools.partial( + logger_utils.make_logger, + directory=FLAGS.base_dir, + to_terminal=True, + to_tensorboard=True, + time_stamp=FLAGS.mava_id, + time_delta=log_every, + ) + + # distributed program + program = mad4pg.MAD4PG( + environment_factory=environment_factory, + network_factory=network_factory, + logger_factory=logger_factory, + num_executors=2, + executor_fn=MAD4PGDiscreteFeedForwardExecutor, + policy_optimizer=snt.optimizers.Adam(learning_rate=1e-4), + critic_optimizer=snt.optimizers.Adam(learning_rate=1e-4), + checkpoint_subpath=checkpoint_dir, + max_gradient_norm=40.0, + ).build() + + # launch + gpu_id = -1 + env_vars = {"CUDA_VISIBLE_DEVICES": str(gpu_id)} + local_resources = { + "trainer": [], + "evaluator": PythonProcess(env=env_vars), + "executor": PythonProcess(env=env_vars), + } + lp.launch( + program, + lp.LaunchType.LOCAL_MULTI_PROCESSING, + terminal="current_terminal", + local_resources=local_resources, + ) + + +if __name__ == "__main__": + app.run(main) diff --git a/examples/debugging_envs/run_decentralised_feedforward_discrete_maddpg.py b/examples/debugging_envs/run_decentralised_feedforward_discrete_maddpg.py index c2eb6b582..7acb89533 100644 --- a/examples/debugging_envs/run_decentralised_feedforward_discrete_maddpg.py +++ b/examples/debugging_envs/run_decentralised_feedforward_discrete_maddpg.py @@ -19,11 +19,6 @@ from datetime import datetime from typing import Any, Dict, Mapping, Sequence, Union -from dm_env import specs - -Array = specs.Array -BoundedArray = specs.BoundedArray -DiscreteArray = specs.DiscreteArray import launchpad as lp import numpy as np import sonnet as snt @@ -32,6 +27,7 @@ from acme import types from acme.tf import networks from acme.tf import utils as tf2_utils +from dm_env import specs from launchpad.nodes.python.local_multi_processing import PythonProcess from mava import specs as mava_specs @@ -39,7 +35,10 @@ from mava.systems.tf.maddpg.execution import MADDPGDiscreteFeedForwardExecutor from mava.utils import lp_utils from mava.utils.environments import debugging_utils -from mava.utils.loggers import logger_utils + +Array = specs.Array +BoundedArray = specs.BoundedArray +DiscreteArray = specs.DiscreteArray FLAGS = flags.FLAGS flags.DEFINE_string( @@ -92,30 +91,35 @@ def make_networks( policy_networks = {} critic_networks = {} for key in specs.keys(): - - # TODO (dries): Make specs[key].actions return a list of specs for hybrid action space - + # TODO (dries): Make specs[key].actions + # return a list of specs for hybrid action space # Get total number of action dimensions from action spec. + agent_act_spec = specs[key].actions if type(specs[key].actions) == DiscreteArray: - num_act_dimensions = specs[key].actions.num_values - elif type(specs[key].actions) == BoundedArray: - num_act_dimensions = np.prod(specs[key].actions.shape, dtype=int) - else: - raise NotImplementedError - + num_actions = agent_act_spec.num_values + minimum = [-1.0] * num_actions + maximum = [1.0] * num_actions + agent_act_spec = BoundedArray( + shape=(num_actions,), + minimum=minimum, + maximum=maximum, + dtype="float32", + name="actions", + ) + + num_dimensions = np.prod(agent_act_spec.shape, dtype=int) # Create the shared observation network; here simply a state-less operation. observation_network = tf2_utils.to_sonnet_module(tf.identity) - # Create the policy network. policy_network = snt.Sequential( [ networks.LayerNormMLP( policy_networks_layer_sizes[key], activate_final=True ), - networks.NearZeroInitializedLinear(num_act_dimensions), - networks.TanhToSpec(specs[key].actions), + networks.NearZeroInitializedLinear(num_dimensions), + networks.TanhToSpec(agent_act_spec), networks.ClippedGaussian(sigma), - networks.ClipToSpec(specs[key].actions), + networks.ClipToSpec(agent_act_spec), ] ) diff --git a/mava/core.py b/mava/core.py index 5d7b77d75..dc3caa70f 100644 --- a/mava/core.py +++ b/mava/core.py @@ -21,7 +21,7 @@ import abc import itertools -from typing import Dict, Generic, Optional, Sequence, TypeVar +from typing import Dict, Generic, Optional, Sequence, Tuple, TypeVar, Union import acme import dm_env @@ -55,7 +55,10 @@ def select_action( @abc.abstractmethod def select_actions( self, observations: Dict[str, types.NestedArray] - ) -> Dict[str, types.NestedArray]: + ) -> Union[ + Dict[str, types.NestedArray], + Tuple[Dict[str, types.NestedArray], Dict[str, types.NestedArray]], + ]: """Samples from the policy and returns an action for each agent.""" @abc.abstractmethod diff --git a/mava/systems/tf/executors.py b/mava/systems/tf/executors.py index d726c72dd..a6da36015 100644 --- a/mava/systems/tf/executors.py +++ b/mava/systems/tf/executors.py @@ -1,5 +1,5 @@ """Generic executor implementations, using TensorFlow and Sonnet.""" -from typing import Any, Dict, Optional, Tuple +from typing import Any, Dict, Optional, Tuple, Union import dm_env import sonnet as snt @@ -69,7 +69,7 @@ def _policy( def select_action( self, agent: str, observation: types.NestedArray - ) -> types.NestedArray: + ) -> Union[types.NestedArray, Tuple[types.NestedArray, types.NestedArray]]: # Pass the observation through the policy network. action = self._policy(agent, observation.observation) @@ -99,7 +99,10 @@ def observe( def select_actions( self, observations: Dict[str, types.NestedArray] - ) -> Dict[str, types.NestedArray]: + ) -> Union[ + Dict[str, types.NestedArray], + Tuple[Dict[str, types.NestedArray], Dict[str, types.NestedArray]], + ]: actions = {} for agent, observation in observations.items(): # Pass the observation through the policy network. diff --git a/mava/systems/tf/mad4pg/execution.py b/mava/systems/tf/mad4pg/execution.py new file mode 100644 index 000000000..336fc251b --- /dev/null +++ b/mava/systems/tf/mad4pg/execution.py @@ -0,0 +1,45 @@ +# python3 +# Copyright 2021 [...placeholder...]. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Dict, Optional + +import sonnet as snt +from acme.tf import variable_utils as tf2_variable_utils + +from mava import adders +from mava.systems.tf.maddpg.execution import MADDPGDiscreteFeedForwardExecutor + + +class MAD4PGDiscreteFeedForwardExecutor(MADDPGDiscreteFeedForwardExecutor): + """A feed-forward executor for discrete actions in MAD4PG. + An executor based on a feed-forward policy for each agent in the system + which takes non-batched observations and outputs non-batched actions. + It also allows adding experiences to replay and updating the weights + from the policy on the learner. + """ + + def __init__( + self, + policy_networks: Dict[str, snt.Module], + adder: Optional[adders.ParallelAdder] = None, + variable_client: Optional[tf2_variable_utils.VariableClient] = None, + shared_weights: bool = True, + ): + super().__init__( + policy_networks=policy_networks, + adder=adder, + variable_client=variable_client, + shared_weights=shared_weights, + ) diff --git a/mava/systems/tf/maddpg/builder.py b/mava/systems/tf/maddpg/builder.py index 32a2008a3..3f659b221 100644 --- a/mava/systems/tf/maddpg/builder.py +++ b/mava/systems/tf/maddpg/builder.py @@ -14,14 +14,17 @@ # limitations under the License. """MADDPG system implementation.""" +import copy import dataclasses from typing import Any, Dict, Iterator, List, Optional, Type, Union import reverb import sonnet as snt from acme import datasets +from acme.specs import EnvironmentSpec from acme.tf import variable_utils from acme.utils import counting, loggers +from dm_env import specs as dm_specs from mava import adders, core, specs, types from mava.adders import reverb as reverb_adders @@ -30,6 +33,9 @@ from mava.systems.tf.maddpg import training from mava.wrappers import DetailedTrainerStatistics, NetworkStatisticsActorCritic +BoundedArray = dm_specs.BoundedArray +DiscreteArray = dm_specs.DiscreteArray + @dataclasses.dataclass class MADDPGConfig: @@ -118,20 +124,49 @@ def __init__( self._trainer_fn = trainer_fn self._executor_fn = executor_fn + def convert_discrete_to_bounded( + self, environment_spec: specs.MAEnvironmentSpec + ) -> specs.MAEnvironmentSpec: + env_adder_spec: specs.MAEnvironmentSpec = copy.deepcopy(environment_spec) + keys = env_adder_spec._keys + for key in keys: + agent_spec = env_adder_spec._specs[key] + if type(agent_spec.actions) == DiscreteArray: + num_actions = agent_spec.actions.num_values + minimum = [float("-inf")] * num_actions + maximum = [float("inf")] * num_actions + new_act_spec = BoundedArray( + shape=(num_actions,), + minimum=minimum, + maximum=maximum, + dtype="float32", + name="actions", + ) + + env_adder_spec._specs[key] = EnvironmentSpec( + observations=agent_spec.observations, + actions=new_act_spec, + rewards=agent_spec.rewards, + discounts=agent_spec.discounts, + ) + return env_adder_spec + def make_replay_tables( self, environment_spec: specs.MAEnvironmentSpec, ) -> List[reverb.Table]: """Create tables to insert data into.""" + env_adder_spec = self.convert_discrete_to_bounded(environment_spec) + # Select adder if issubclass(self._executor_fn, executors.FeedForwardExecutor): adder_sig = reverb_adders.ParallelNStepTransitionAdder.signature( - environment_spec, self._extra_specs + env_adder_spec, self._extra_specs ) elif issubclass(self._executor_fn, executors.RecurrentExecutor): adder_sig = reverb_adders.ParallelSequenceAdder.signature( - environment_spec, self._extra_specs + env_adder_spec, self._extra_specs ) else: raise NotImplementedError("Unknown executor type: ", self._executor_fn) diff --git a/mava/systems/tf/maddpg/execution.py b/mava/systems/tf/maddpg/execution.py index c0dd02097..c440ad235 100644 --- a/mava/systems/tf/maddpg/execution.py +++ b/mava/systems/tf/maddpg/execution.py @@ -13,10 +13,9 @@ # See the License for the specific language governing permissions and # limitations under the License. -from typing import Any, Dict, Optional, Tuple +from typing import Dict, List, Optional, Tuple, Union import dm_env -import numpy as np import sonnet as snt import tensorflow as tf import tensorflow_probability as tfp @@ -26,8 +25,8 @@ from acme.tf import utils as tf2_utils from acme.tf import variable_utils as tf2_variable_utils -from mava import adders, core -from mava.systems import executors +from mava import adders +from mava.systems.tf import executors tfd = tfp.distributions @@ -63,7 +62,9 @@ def __init__( self._variable_client = variable_client self._policy_networks = policy_networks self._shared_weights = shared_weights - @tf.function + + # TODO: Add this back in + # @tf.function def _policy( self, agent: str, observation: types.NestedTensor ) -> types.NestedTensor: @@ -78,34 +79,34 @@ def _policy( policy = self._policy_networks[agent_key](batched_observation) # Sample from the policy if it is stochastic. - action = tf.math.argmax(policy) + action = tf.math.argmax(policy, axis=1) return action, policy def select_action( self, agent: str, observation: types.NestedArray - ) -> types.NestedArray: + ) -> Tuple[types.NestedArray, types.NestedArray]: # Step the recurrent policy/value network forward # given the current observation and state. - action, policy = self._policy(agent, observation) + action, policy = self._policy(agent, observation.observation) # Return a numpy array with squeezed out batch dimension. action = tf2_utils.to_numpy_squeeze(action) - return [action, policy] + policy = tf2_utils.to_numpy_squeeze(policy) + return action, policy def select_actions( self, observations: Dict[str, types.NestedArray] - ) -> Dict[str, types.NestedArray]: + ) -> Tuple[Dict[str, types.NestedArray], Dict[str, types.NestedArray]]: actions = {} policies = {} for agent, observation in observations.items(): action, policy = self.select_action(agent, observation) - actions[agent] = action[0] + actions[agent] = action policies[agent] = policy - - return [actions, policies] + return actions, policies def observe_first( self, @@ -114,25 +115,16 @@ def observe_first( ) -> None: if self._adder: - - # Generate dummy policy values to send through - self._adder.add_first(timestep) + self._adder.add_first(timestep, extras) def observe( self, - actions: Dict[str, types.NestedArray], + actions: Union[ + Dict[str, types.NestedArray], List[Dict[str, types.NestedArray]] + ], next_timestep: dm_env.TimeStep, next_extras: Dict[str, types.NestedArray] = {}, ) -> None: - - if not self._adder: - return - - self.adder.next_extras.update({"policy_out": self._policy_outputs}) - next_extras = tf2_utils.to_numpy_squeeze(next_extras) - - self._adder.add(actions, next_timestep, next_extras) - - def update(self, wait: bool = False) -> None: - if self._variable_client: - self._variable_client.update(wait) + if self._adder: + env_actions, policy = actions + self._adder.add(policy, next_timestep, next_extras) # type: ignore diff --git a/mava/systems/tf/maddpg/system.py b/mava/systems/tf/maddpg/system.py index 37131d51c..b7e2f46c6 100644 --- a/mava/systems/tf/maddpg/system.py +++ b/mava/systems/tf/maddpg/system.py @@ -247,8 +247,11 @@ def trainer( ) # Create system architecture with target networks. + adder_env_spec = self._builder.convert_discrete_to_bounded( + self._environment_spec + ) system_networks = self._architecture( - environment_spec=self._environment_spec, + environment_spec=adder_env_spec, observation_networks=networks["observations"], policy_networks=networks["policies"], critic_networks=networks["critics"], diff --git a/mava/systems/tf/maddpg/training.py b/mava/systems/tf/maddpg/training.py index 2bfe259c9..63057fd23 100644 --- a/mava/systems/tf/maddpg/training.py +++ b/mava/systems/tf/maddpg/training.py @@ -309,9 +309,7 @@ def _forward(self, inputs: Any) -> None: # This discount is applied to future rewards after r_t. # o_t = dictionary of next observations or next observation sequences # e_t [Optional] = extra data for timestep t that the agents persist in replay. - o_tm1, _, e_tm1, r_t, d_t, o_t, e_t = inputs.data - - a_tm1 = e_tm1["policy_outputs"] + o_tm1, a_tm1, e_tm1, r_t, d_t, o_t, e_t = inputs.data self.policy_losses = {} self.critic_losses = {} From 787f5716a961f23fc3e53785ed2af1bca02f186c Mon Sep 17 00:00:00 2001 From: Dries Date: Sat, 22 May 2021 11:42:51 +0200 Subject: [PATCH 012/188] feature: Add comment. --- mava/systems/tf/maddpg/execution.py | 1 + 1 file changed, 1 insertion(+) diff --git a/mava/systems/tf/maddpg/execution.py b/mava/systems/tf/maddpg/execution.py index c440ad235..279e77a02 100644 --- a/mava/systems/tf/maddpg/execution.py +++ b/mava/systems/tf/maddpg/execution.py @@ -127,4 +127,5 @@ def observe( ) -> None: if self._adder: env_actions, policy = actions + # TODO (dries): Sort out this mypy issue. self._adder.add(policy, next_timestep, next_extras) # type: ignore From 5a27a672cfe480701c7004ad5009239f296630f7 Mon Sep 17 00:00:00 2001 From: Kevin Eloff Date: Sat, 22 May 2021 13:01:44 +0200 Subject: [PATCH 013/188] feat: comm madqn --- .../run_recurrent_comm_madqn.py | 221 ++++++++++++++++++ .../tf/modules/communication/base.py | 22 +- mava/systems/tf/executors.py | 41 ++-- mava/systems/tf/madqn/builder.py | 5 + mava/systems/tf/madqn/execution.py | 120 +++++++++- mava/systems/tf/madqn/system.py | 68 +++++- mava/systems/tf/madqn/training.py | 136 ++++++++++- 7 files changed, 590 insertions(+), 23 deletions(-) create mode 100644 examples/debugging_envs/run_recurrent_comm_madqn.py diff --git a/examples/debugging_envs/run_recurrent_comm_madqn.py b/examples/debugging_envs/run_recurrent_comm_madqn.py new file mode 100644 index 000000000..057394654 --- /dev/null +++ b/examples/debugging_envs/run_recurrent_comm_madqn.py @@ -0,0 +1,221 @@ +# python3 +# Copyright 2021 [...placeholder...]. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import functools +from datetime import datetime +from typing import Any, Dict, Mapping, Optional, Sequence, Union + +import launchpad as lp +import numpy as np +import sonnet as snt +import tensorflow as tf +from absl import app, flags +from acme import types +from acme.wrappers.gym_wrapper import _convert_to_spec +from gym import spaces +from launchpad.nodes.python.local_multi_processing import PythonProcess + +from mava import specs as mava_specs +from mava.components.tf.modules.communication.broadcasted import ( + BroadcastedCommunication, +) +from mava.components.tf.modules.exploration import LinearExplorationScheduler +from mava.components.tf.networks import DIALPolicy, epsilon_greedy_action_selector +from mava.systems.tf import madqn +from mava.systems.tf.madqn.execution import MADQNRecurrentCommExecutor +from mava.systems.tf.madqn.training import RecurrentCommMADQNTrainer +from mava.utils import lp_utils +from mava.utils.environments import debugging_utils +from mava.utils.loggers import Logger + +FLAGS = flags.FLAGS +flags.DEFINE_string( + "env_name", + "simple_spread", + "Debugging environment name (str).", +) +flags.DEFINE_string( + "action_space", + "discrete", + "Environment action space type (str).", +) + +flags.DEFINE_string( + "mava_id", + str(datetime.now()), + "Experiment identifier that can be used to continue experiments.", +) +flags.DEFINE_string("base_dir", "~/mava/", "Base dir to store experiments.") + + +def make_networks( + environment_spec: mava_specs.MAEnvironmentSpec, + q_networks_layer_sizes: Union[Dict[str, Sequence], Sequence] = ( + 512, + 512, + 256, + ), + shared_weights: bool = True, +) -> Mapping[str, types.TensorTransformation]: + """Creates networks used by the agents.""" + + specs = environment_spec.get_agent_specs() + + # Create agent_type specs + if shared_weights: + type_specs = {key.split("_")[0]: specs[key] for key in specs.keys()} + specs = type_specs + + if isinstance(q_networks_layer_sizes, Sequence): + q_networks_layer_sizes = {key: q_networks_layer_sizes for key in specs.keys()} + + def action_selector_fn( + q_values: types.NestedTensor, + legal_actions: types.NestedTensor, + epsilon: Optional[tf.Variable] = None, + ) -> types.NestedTensor: + return epsilon_greedy_action_selector( + action_values=q_values, legal_actions_mask=legal_actions, epsilon=epsilon + ) + + q_networks = {} + action_selectors = {} + for key in specs.keys(): + + # Get total number of action dimensions from action spec. + # num_dimensions = specs[key].actions.num_values + + # Create the policy network. + # q_network = snt.DeepRNN( + # [ + # networks.LayerNormMLP( + # q_networks_layer_sizes[key], activate_final=False + # ), + # snt.LSTM(25), + # snt.nets.MLP([128]), + # networks.NearZeroInitializedLinear(num_dimensions), + # ] + # ) + + q_network = DIALPolicy( + action_spec=specs[key].actions, + message_spec=_convert_to_spec( + spaces.Box(-np.inf, np.inf, (10,), dtype=np.float32) + ), + gru_hidden_size=256, + gru_layers=2, + task_mlp_size=( + 256, + 256, + ), + message_in_mlp_size=(256,), + message_out_mlp_size=(256,), + output_mlp_size=(256,), + ) + + # epsilon greedy action selector + action_selector = action_selector_fn + + q_networks[key] = q_network + action_selectors[key] = action_selector + + return { + "q_networks": q_networks, + "action_selectors": action_selectors, + } + + +def main(_: Any) -> None: + + # environment + environment_factory = functools.partial( + debugging_utils.make_environment, + env_name=FLAGS.env_name, + action_space=FLAGS.action_space, + ) + + # networks + network_factory = lp_utils.partial_kwargs(make_networks) + + # Checkpointer appends "Checkpoints" to checkpoint_dir + checkpoint_dir = f"{FLAGS.base_dir}/{FLAGS.mava_id}" + + log_every = 10 + trainer_logger = Logger( + label="system_trainer", + directory=FLAGS.base_dir, + to_terminal=True, + to_tensorboard=True, + time_stamp=FLAGS.mava_id, + time_delta=log_every, + ) + + exec_logger = Logger( + # _{executor_id} gets appended to label in system. + label="train_loop_executor", + directory=FLAGS.base_dir, + to_terminal=True, + to_tensorboard=True, + time_stamp=FLAGS.mava_id, + time_delta=log_every, + ) + + eval_logger = Logger( + label="eval_loop", + directory=FLAGS.base_dir, + to_terminal=True, + to_tensorboard=True, + time_stamp=FLAGS.mava_id, + time_delta=log_every, + ) + + # distributed program + program = madqn.MADQN( + environment_factory=environment_factory, + network_factory=network_factory, + num_executors=2, + trainer_fn=RecurrentCommMADQNTrainer, + executor_fn=MADQNRecurrentCommExecutor, + exploration_scheduler_fn=LinearExplorationScheduler, + communication_module=BroadcastedCommunication, + epsilon_min=0.05, + epsilon_decay=5e-4, + optimizer=snt.optimizers.Adam(learning_rate=1e-4), + checkpoint_subpath=checkpoint_dir, + trainer_logger=trainer_logger, + exec_logger=exec_logger, + eval_logger=eval_logger, + n_step=1, + batch_size=32, + ).build() + + # launch + gpu_id = -1 + env_vars = {"CUDA_VISIBLE_DEVICES": str(gpu_id)} + local_resources = { + "trainer": [], + "evaluator": PythonProcess(env=env_vars), + "executor": PythonProcess(env=env_vars), + } + lp.launch( + program, + lp.LaunchType.LOCAL_MULTI_PROCESSING, + terminal="current_terminal", + local_resources=local_resources, + ) + + +if __name__ == "__main__": + app.run(main) diff --git a/mava/components/tf/modules/communication/base.py b/mava/components/tf/modules/communication/base.py index 572f1a43c..3eb71d861 100644 --- a/mava/components/tf/modules/communication/base.py +++ b/mava/components/tf/modules/communication/base.py @@ -16,10 +16,12 @@ # TODO (Kevin): complete base class for communication import abc -from typing import Dict +from typing import Dict, Union import sonnet as snt +from mava.components.tf.architectures import BaseArchitecture, BasePolicyArchitecture + """Base communication interface for multi-agent RL systems""" @@ -30,6 +32,24 @@ class BaseCommunicationModule: agents in a multi-agent RL system. """ + @abc.abstractmethod + def __init__( + self, + architecture: Union[ + BaseArchitecture, + BasePolicyArchitecture, + ], + shared: bool, + channel_size: int, + channel_noise: float, + ) -> None: + """Initializes the broadcaster communicator. + Args: + architecture: the BaseArchitecture used. + shared: if a shared communication channel is used. + channel_noise: stddev of normal noise in channel. + """ + @abc.abstractmethod def process_messages( self, messages: Dict[str, snt.Module] diff --git a/mava/systems/tf/executors.py b/mava/systems/tf/executors.py index d726c72dd..3dc7ef19e 100644 --- a/mava/systems/tf/executors.py +++ b/mava/systems/tf/executors.py @@ -308,8 +308,6 @@ def __init__( self._policy_networks = policy_networks self._states: Dict[str, Any] = {} self._messages: Dict[str, Any] = {} - self._prev_states: Dict[str, Any] = {} - self._prev_messages: Dict[str, Any] = {} self._store_recurrent_state = store_recurrent_state self._communication_module = communication_module self._shared_weights = shared_weights @@ -371,14 +369,20 @@ def observe_first( if self._adder is not None: numpy_states = { - agent: { - "state": tf2_utils.to_numpy_squeeze(_state), - "message": tf2_utils.to_numpy_squeeze(self._messages[agent]), - } + agent: tf2_utils.to_numpy_squeeze(_state) for agent, _state in self._states.items() } + numpy_messages = { + agent: tf2_utils.to_numpy_squeeze(_message) + for agent, _message in self._messages.items() + } if extras is not None: - extras.update({"core_states": numpy_states}) + extras.update( + { + "core_states": numpy_states, + "core_messages": numpy_messages, + } + ) self._adder.add_first(timestep, extras) else: raise NotImplementedError("Why is extras None?") @@ -400,17 +404,28 @@ def observe( return numpy_states = { - agent: { - "state": tf2_utils.to_numpy_squeeze(_state), - "message": tf2_utils.to_numpy_squeeze(self._messages[agent]), - } + agent: tf2_utils.to_numpy_squeeze(_state) for agent, _state in self._states.items() } + numpy_messages = { + agent: tf2_utils.to_numpy_squeeze(_message) + for agent, _message in self._messages.items() + } + if next_extras: - next_extras.update({"core_states": numpy_states}) + next_extras.update( + { + "core_states": numpy_states, + "core_messages": numpy_messages, + } + ) self._adder.add(actions, next_timestep, next_extras) else: - self._adder.add(actions, next_timestep, numpy_states) + next_extras = { + "core_states": numpy_states, + "core_messages": numpy_messages, + } + self._adder.add(actions, next_timestep, next_extras) def select_action( self, diff --git a/mava/systems/tf/madqn/builder.py b/mava/systems/tf/madqn/builder.py index f439a6373..6edc8b14e 100644 --- a/mava/systems/tf/madqn/builder.py +++ b/mava/systems/tf/madqn/builder.py @@ -25,6 +25,7 @@ from mava import adders, core, specs, types from mava.adders import reverb as reverb_adders +from mava.components.tf.modules.communication import BaseCommunicationModule from mava.components.tf.modules.exploration.exploration_scheduling import ( LinearExplorationScheduler, ) @@ -257,6 +258,7 @@ def make_executor( adder: Optional[adders.ParallelAdder] = None, variable_source: Optional[core.VariableSource] = None, trainer: Optional[training.MADQNTrainer] = None, + communication_module: Optional[BaseCommunicationModule] = None, ) -> core.Executor: """Create an executor instance. Args: @@ -297,6 +299,7 @@ def make_executor( variable_client=variable_client, adder=adder, trainer=trainer, + communication_module=communication_module, ) def make_trainer( @@ -305,6 +308,7 @@ def make_trainer( dataset: Iterator[reverb.ReplaySample], counter: Optional[counting.Counter] = None, logger: Optional[types.NestedLogger] = None, + communication_module: Optional[BaseCommunicationModule] = None, ) -> core.Trainer: """Creates an instance of the trainer. Args: @@ -340,6 +344,7 @@ def make_trainer( target_update_period=self._config.target_update_period, clipping=self._config.clipping, exploration_scheduler=exploration_scheduler, + communication_module=communication_module, dataset=dataset, counter=counter, logger=logger, diff --git a/mava/systems/tf/madqn/execution.py b/mava/systems/tf/madqn/execution.py index b870b0d3d..92bd72da8 100644 --- a/mava/systems/tf/madqn/execution.py +++ b/mava/systems/tf/madqn/execution.py @@ -27,7 +27,12 @@ from acme.tf import variable_utils as tf2_variable_utils from mava import adders -from mava.systems.tf.executors import FeedForwardExecutor, RecurrentExecutor +from mava.components.tf.modules.communication import BaseCommunicationModule +from mava.systems.tf.executors import ( + FeedForwardExecutor, + RecurrentExecutor, + RecurrentExecutorWithComms, +) from mava.systems.tf.madqn.training import MADQNTrainer from mava.types import OLT @@ -235,3 +240,116 @@ def select_actions( # Return a numpy array with squeezed out batch dimension. return actions + + +class MADQNRecurrentCommExecutor(RecurrentExecutorWithComms): + """A feed-forward executor. + An executor based on a feed-forward policy for each agent in the system + which takes non-batched observations and outputs non-batched actions. + It also allows adding experiences to replay and updating the weights + from the policy on the learner. + """ + + def __init__( + self, + q_networks: Dict[str, snt.Module], + action_selectors: Dict[str, snt.Module], + communication_module: BaseCommunicationModule, + shared_weights: bool = True, + adder: Optional[adders.ParallelAdder] = None, + variable_client: Optional[tf2_variable_utils.VariableClient] = None, + store_recurrent_state: bool = True, + trainer: MADQNTrainer = None, + ): + """Initializes the executor. + Args: + policy_network: the policy to run for each agent in the system. + shared_weights: specify if weights are shared between agent networks. + adder: the adder object to which allows to add experiences to a + dataset/replay buffer. + variable_client: object which allows to copy weights from the trainer copy + of the policies to the executor copy (in case they are separate). + """ + + # Store these for later use. + self._adder = adder + self._variable_client = variable_client + self._q_networks = q_networks + self._policy_networks = q_networks + self._communication_module = communication_module + self._action_selectors = action_selectors + self._store_recurrent_state = store_recurrent_state + self._trainer = trainer + self._shared_weights = shared_weights + + self._states: Dict[str, Any] = {} + self._messages: Dict[str, Any] = {} + + @tf.function + def _policy( + self, + agent: str, + observation: types.NestedTensor, + state: types.NestedTensor, + message: types.NestedTensor, + legal_actions: types.NestedTensor, + epsilon: tf.Tensor, + ) -> types.NestedTensor: + + # Add a dummy batch dimension and as a side effect convert numpy to TF. + batched_observation = tf2_utils.add_batch_dim(observation) + batched_legals = tf2_utils.add_batch_dim(legal_actions) + + # index network either on agent type or on agent id + agent_key = agent.split("_")[0] if self._shared_weights else agent + + # Compute the policy, conditioned on the observation. + (q_values, m_values), new_state = self._q_networks[agent_key]( + batched_observation, state, message + ) + + # select legal action + action = self._action_selectors[agent_key]( + q_values, batched_legals, epsilon=epsilon + ) + + return (action, m_values), new_state + + def select_action( + self, agent: str, observation: types.NestedArray + ) -> types.NestedArray: + raise NotImplementedError + + def select_actions( + self, observations: Dict[str, OLT] + ) -> Dict[str, types.NestedArray]: + actions = {} + + message_inputs = self._communication_module.process_messages(self._messages) + + for agent, observation in observations.items(): + + # Pass the observation through the policy network. + if self._trainer is not None: + epsilon = self._trainer.get_epsilon() + else: + epsilon = 0.0 + + epsilon = tf.convert_to_tensor(epsilon) + + (policy_output, new_message), new_state = self._policy( + agent, + observation.observation, + self._states[agent], + message_inputs[agent], + observation.legal_actions, + epsilon, + ) + + self._states[agent] = new_state + self._messages[agent] = new_message + + actions[agent] = tf2_utils.to_numpy_squeeze(policy_output) + + # Return a numpy array with squeezed out batch dimension. + return actions diff --git a/mava/systems/tf/madqn/system.py b/mava/systems/tf/madqn/system.py index 7f952fd62..d7f319d16 100644 --- a/mava/systems/tf/madqn/system.py +++ b/mava/systems/tf/madqn/system.py @@ -31,6 +31,7 @@ from mava import core from mava import specs as mava_specs from mava.components.tf.architectures import DecentralisedValueActor +from mava.components.tf.modules.communication import BaseCommunicationModule from mava.components.tf.modules.exploration import LinearExplorationScheduler from mava.environment_loop import ParallelEnvironmentLoop from mava.systems.tf import executors @@ -52,6 +53,7 @@ def __init__( trainer_fn: Union[ Type[training.MADQNTrainer], Type[training.RecurrentMADQNTrainer] ] = training.MADQNTrainer, + communication_module: Type[BaseCommunicationModule] = None, executor_fn: Type[core.Executor] = execution.MADQNFeedForwardExecutor, exploration_scheduler_fn: Type[ LinearExplorationScheduler @@ -93,6 +95,7 @@ def __init__( ) self._architecture = architecture + self._communication_module_fn = communication_module self._environment_factory = environment_factory self._network_factory = network_factory self._environment_spec = environment_spec @@ -146,6 +149,8 @@ def __init__( def _get_extra_specs(self) -> Any: agents = self._environment_spec.get_agent_ids() core_state_specs = {} + core_message_specs = {} + networks = self._network_factory( # type: ignore environment_spec=self._environment_spec ) @@ -156,7 +161,17 @@ def _get_extra_specs(self) -> Any: networks["q_networks"][agent_type].initial_state(1) ), ) - extras = {"core_states": core_state_specs} + if self._communication_module_fn is not None: + core_message_specs[agent] = ( + tf2_utils.squeeze_batch_dim( + networks["q_networks"][agent_type].initial_message(1) + ), + ) + + extras = { + "core_states": core_state_specs, + "core_messages": core_message_specs, + } return extras def replay(self) -> Any: @@ -187,11 +202,23 @@ def trainer( ) # Create system architecture with target networks. - system_networks = self._architecture( + architecture = self._architecture( environment_spec=self._environment_spec, value_networks=networks["q_networks"], shared_weights=self._shared_weights, - ).create_system() + ) + + communication_module = None + if self._communication_module_fn is not None: + communication_module = self._communication_module_fn( + architecture=architecture, + shared=True, + channel_size=1, + channel_noise=0, + ) + system_networks = communication_module.create_system() + else: + system_networks = architecture.create_system() dataset = self._builder.make_dataset_iterator(replay) counter = counting.Counter(counter, "trainer") @@ -201,6 +228,7 @@ def trainer( dataset=dataset, counter=counter, logger=self._trainer_logger, + communication_module=communication_module, ) def executor( @@ -219,16 +247,29 @@ def executor( ) # Create system architecture with target networks. - system_networks = self._architecture( + architecture = self._architecture( environment_spec=self._environment_spec, value_networks=networks["q_networks"], shared_weights=self._shared_weights, - ).create_system() + ) + + communication_module = None + if self._communication_module_fn is not None: + communication_module = self._communication_module_fn( + architecture=architecture, + shared=True, + channel_size=1, + channel_noise=0, + ) + system_networks = communication_module.create_system() + else: + system_networks = architecture.create_system() # Create the executor. executor = self._builder.make_executor( q_networks=system_networks["values"], action_selectors=networks["action_selectors"], + communication_module=communication_module, adder=self._builder.make_adder(replay), variable_source=variable_source, trainer=trainer, @@ -274,17 +315,30 @@ def evaluator( ) # Create system architecture with target networks. - system_networks = self._architecture( + architecture = self._architecture( environment_spec=self._environment_spec, value_networks=networks["q_networks"], shared_weights=self._shared_weights, - ).create_system() + ) + + communication_module = None + if self._communication_module_fn is not None: + communication_module = self._communication_module_fn( + architecture=architecture, + shared=True, + channel_size=1, + channel_noise=0, + ) + system_networks = communication_module.create_system() + else: + system_networks = architecture.create_system() # Create the agent. executor = self._builder.make_executor( q_networks=system_networks["values"], action_selectors=networks["action_selectors"], variable_source=variable_source, + communication_module=communication_module, ) # Make the environment. diff --git a/mava/systems/tf/madqn/training.py b/mava/systems/tf/madqn/training.py index 0b0e25a04..99322861a 100644 --- a/mava/systems/tf/madqn/training.py +++ b/mava/systems/tf/madqn/training.py @@ -14,7 +14,7 @@ # limitations under the License. import time -from typing import Any, Dict, List, Sequence, Tuple +from typing import Any, Dict, List, Optional, Sequence, Tuple import numpy as np import sonnet as snt @@ -26,6 +26,7 @@ from acme.utils import counting, loggers import mava +from mava.components.tf.modules.communication import BaseCommunicationModule from mava.components.tf.modules.exploration.exploration_scheduling import ( LinearExplorationScheduler, ) @@ -56,6 +57,7 @@ def __init__( logger: loggers.Logger = None, checkpoint: bool = True, checkpoint_subpath: str = "~/mava/", + communication_module: Optional[BaseCommunicationModule] = None, ): self._agents = agents @@ -316,6 +318,7 @@ def __init__( logger: loggers.Logger = None, checkpoint: bool = True, checkpoint_subpath: str = "~/mava/", + communication_module: Optional[BaseCommunicationModule] = None, ): super().__init__( agents=agents, @@ -385,3 +388,134 @@ def _forward(self, inputs: Any) -> None: self._q_network_losses = q_network_losses self.tape = tape + + +class RecurrentCommMADQNTrainer(MADQNTrainer): + """Recurrent MADQN trainer. + This is the trainer component of a MADQN system. IE it takes a dataset as input + and implements update functionality to learn from this dataset. + """ + + def __init__( + self, + agents: List[str], + agent_types: List[str], + q_networks: Dict[str, snt.Module], + target_q_networks: Dict[str, snt.Module], + target_update_period: int, + dataset: tf.data.Dataset, + optimizer: snt.Optimizer, + discount: float, + shared_weights: bool, + exploration_scheduler: LinearExplorationScheduler, + communication_module: BaseCommunicationModule, + clipping: bool = True, + counter: counting.Counter = None, + logger: loggers.Logger = None, + checkpoint: bool = True, + checkpoint_subpath: str = "~/mava/", + ): + super().__init__( + agents=agents, + agent_types=agent_types, + q_networks=q_networks, + target_q_networks=target_q_networks, + target_update_period=target_update_period, + dataset=dataset, + optimizer=optimizer, + discount=discount, + shared_weights=shared_weights, + exploration_scheduler=exploration_scheduler, + clipping=clipping, + counter=counter, + logger=logger, + checkpoint=checkpoint, + checkpoint_subpath=checkpoint_subpath, + ) + + self._communication_module = communication_module + + def _forward(self, inputs: Any) -> None: + data = tree.map_structure( + lambda v: tf.expand_dims(v, axis=0) if len(v.shape) <= 1 else v, inputs.data + ) + data = tf2_utils.batch_to_sequence(data) + + observations, actions, rewards, discounts, _, extra = data + + # core_states = extra["core_states"] + core_state = tree.map_structure( + lambda s: s[:, 0, :], inputs.data.extras["core_states"] + ) + core_message = tree.map_structure( + lambda s: s[:, 0, :], inputs.data.extras["core_messages"] + ) + + with tf.GradientTape(persistent=True) as tape: + q_network_losses: Dict[str, NestedArray] = { + agent: {"q_value_loss": tf.zeros(())} for agent in self._agents + } + + T = actions[self._agents[0]].shape[0] + + state = {agent: core_state[agent][0] for agent in self._agents} + target_state = {agent: core_state[agent][0] for agent in self._agents} + + message = {agent: core_message[agent][0] for agent in self._agents} + target_message = {agent: core_message[agent][0] for agent in self._agents} + + # _target_q_networks must be 1 step ahead + target_channel = self._communication_module.process_messages(target_message) + for agent in self._agents: + agent_key = self.agent_net_keys[agent] + (q_targ, m), s = self._target_q_networks[agent_key]( + observations[agent].observation[0], + target_state[agent], + target_channel[agent], + ) + target_state[agent] = s + target_message[agent] = m + + for t in range(1, T, 1): + channel = self._communication_module.process_messages(message) + target_channel = self._communication_module.process_messages( + target_message + ) + + for agent in self._agents: + agent_key = self.agent_net_keys[agent] + + # Cast the additional discount + # to match the environment discount dtype. + + discount = tf.cast(self._discount, dtype=discounts[agent][0].dtype) + + (q_targ, m), s = self._target_q_networks[agent_key]( + observations[agent].observation[t], + target_state[agent], + target_channel[agent], + ) + target_state[agent] = s + target_message[agent] = m + + (q, m), s = self._q_networks[agent_key]( + observations[agent].observation[t - 1], + state[agent], + channel[agent], + ) + state[agent] = s + message[agent] = m + + loss, _ = trfl.qlearning( + q, + actions[agent][t - 1], + rewards[agent][t - 1], + discount * discounts[agent][t], + q_targ, + ) + + loss = tf.reduce_mean(loss) + q_network_losses[agent]["q_value_loss"] += loss + + self._q_network_losses = q_network_losses + self.tape = tape From 6a624deba57c5620c5acbfbe360a3843074e45f8 Mon Sep 17 00:00:00 2001 From: Kevin Eloff Date: Sat, 22 May 2021 13:04:25 +0200 Subject: [PATCH 014/188] fix: comm module args --- mava/systems/tf/madqn/execution.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/mava/systems/tf/madqn/execution.py b/mava/systems/tf/madqn/execution.py index 92bd72da8..5732b6ab6 100644 --- a/mava/systems/tf/madqn/execution.py +++ b/mava/systems/tf/madqn/execution.py @@ -53,6 +53,7 @@ def __init__( adder: Optional[adders.ParallelAdder] = None, variable_client: Optional[tf2_variable_utils.VariableClient] = None, trainer: MADQNTrainer = None, + communication_module: Optional[BaseCommunicationModule] = None, ): """Initializes the executor. Args: @@ -156,6 +157,7 @@ def __init__( variable_client: Optional[tf2_variable_utils.VariableClient] = None, store_recurrent_state: bool = True, trainer: MADQNTrainer = None, + communication_module: Optional[BaseCommunicationModule] = None, ): """Initializes the executor. Args: From 13bea907d2f4b4105b6cbf7792f9f6743e7d794e Mon Sep 17 00:00:00 2001 From: Kevin Eloff Date: Sat, 22 May 2021 13:06:55 +0200 Subject: [PATCH 015/188] feat: update dial trainer --- mava/systems/tf/dial/system.py | 9 +++--- mava/systems/tf/dial/training.py | 47 ++++++-------------------------- 2 files changed, 13 insertions(+), 43 deletions(-) diff --git a/mava/systems/tf/dial/system.py b/mava/systems/tf/dial/system.py index 99f5442d8..f6b7f8a25 100644 --- a/mava/systems/tf/dial/system.py +++ b/mava/systems/tf/dial/system.py @@ -61,15 +61,16 @@ def __init__( num_caches: int = 0, environment_spec: mava_specs.MAEnvironmentSpec = None, shared_weights: bool = True, - discount: float = 1.0, - batch_size: int = 32, + discount: float = 0.99, + batch_size: int = 256, prefetch_size: int = 4, target_update_period: int = 100, min_replay_size: int = 100, max_replay_size: int = 1000000, samples_per_insert: float = 32.0, - policy_optimizer: snt.Optimizer = snt.optimizers.RMSProp(5e-4, momentum=0.95), - n_step: int = 1, + # policy_optimizer: snt.Optimizer = snt.optimizers.RMSProp(5e-4, momentum=0.95), + policy_optimizer: snt.Optimizer = snt.optimizers.Adam(learning_rate=1e-4), + n_step: int = 5, sequence_length: int = 20, period: int = 20, importance_sampling_exponent: float = 0.2, diff --git a/mava/systems/tf/dial/training.py b/mava/systems/tf/dial/training.py index c7207506c..c967a5762 100644 --- a/mava/systems/tf/dial/training.py +++ b/mava/systems/tf/dial/training.py @@ -176,38 +176,7 @@ def _update_target_networks(self) -> None: if tf.math.mod(self._num_steps, self._target_update_period) == 0: for src, dest in zip(online_variables, target_variables): dest.assign(src) - self._num_steps.assign_add(1) - - @tf.function - def _policy_actions_messages( - self, - target_obs_trans: Dict[str, np.ndarray], - target_core_state: Dict[str, np.ndarray], - target_core_message: Dict[str, np.ndarray], - ) -> Any: - actions = {} - messages = {} - - for agent in self._agents: - time.time() - agent_key = self.agent_net_keys[agent] - target_trans_obs = target_obs_trans[agent] - # TODO (dries): Why is there an extra tuple - # wrapping that needs to be removed? - agent_core_state = target_core_state[agent][0] - agent_core_message = target_core_message[agent][0] - - transposed_obs = tf2_utils.batch_to_sequence(target_trans_obs) - - (output_actions, output_messages), _ = snt.static_unroll( - self._target_policy_networks[agent_key], - transposed_obs, - agent_core_state, - agent_core_message, - ) - actions[agent] = tf2_utils.batch_to_sequence(output_actions) - messages[agent] = tf2_utils.batch_to_sequence(output_messages) - return actions, messages + self._num_steps.assign_add(1) def _step(self) -> Dict[str, Dict[str, Any]]: # Update the target networks @@ -373,13 +342,13 @@ def _forward(self, inputs: Any) -> None: # d_theta = d_theta + d_Q_t_a ^ 2 policy_losses[agent_id] += td_action ** 2 - # Communication grads - td_comm = y_message - tf.gather( - policy["messages"][t - 1][agent_id], - tf.argmax(message, axis=-1), - batch_dims=1, - ) - policy_losses[agent_id] += td_comm ** 2 + # Communication grads for RIAL + # td_comm = y_message - tf.gather( + # policy["messages"][t - 1][agent_id], + # tf.argmax(message, axis=-1), + # batch_dims=1, + # ) + # policy_losses[agent_id] += td_comm ** 2 # Average over batches for key in policy_losses.keys(): From ffa59f0cc7df840bdf11d02d04206b11a4289e0d Mon Sep 17 00:00:00 2001 From: Kevin Eloff Date: Sat, 22 May 2021 13:20:08 +0200 Subject: [PATCH 016/188] fix: update fingerprint --- .../run_recurrent_comm_madqn.py | 29 +++---------------- mava/systems/tf/madqn/training.py | 2 ++ 2 files changed, 6 insertions(+), 25 deletions(-) diff --git a/examples/debugging_envs/run_recurrent_comm_madqn.py b/examples/debugging_envs/run_recurrent_comm_madqn.py index 057394654..904748bf5 100644 --- a/examples/debugging_envs/run_recurrent_comm_madqn.py +++ b/examples/debugging_envs/run_recurrent_comm_madqn.py @@ -38,7 +38,7 @@ from mava.systems.tf.madqn.training import RecurrentCommMADQNTrainer from mava.utils import lp_utils from mava.utils.environments import debugging_utils -from mava.utils.loggers import Logger +from mava.utils.loggers import logger_utils FLAGS = flags.FLAGS flags.DEFINE_string( @@ -153,27 +153,8 @@ def main(_: Any) -> None: checkpoint_dir = f"{FLAGS.base_dir}/{FLAGS.mava_id}" log_every = 10 - trainer_logger = Logger( - label="system_trainer", - directory=FLAGS.base_dir, - to_terminal=True, - to_tensorboard=True, - time_stamp=FLAGS.mava_id, - time_delta=log_every, - ) - - exec_logger = Logger( - # _{executor_id} gets appended to label in system. - label="train_loop_executor", - directory=FLAGS.base_dir, - to_terminal=True, - to_tensorboard=True, - time_stamp=FLAGS.mava_id, - time_delta=log_every, - ) - - eval_logger = Logger( - label="eval_loop", + logger_factory = functools.partial( + logger_utils.make_logger, directory=FLAGS.base_dir, to_terminal=True, to_tensorboard=True, @@ -185,6 +166,7 @@ def main(_: Any) -> None: program = madqn.MADQN( environment_factory=environment_factory, network_factory=network_factory, + logger_factory=logger_factory, num_executors=2, trainer_fn=RecurrentCommMADQNTrainer, executor_fn=MADQNRecurrentCommExecutor, @@ -194,9 +176,6 @@ def main(_: Any) -> None: epsilon_decay=5e-4, optimizer=snt.optimizers.Adam(learning_rate=1e-4), checkpoint_subpath=checkpoint_dir, - trainer_logger=trainer_logger, - exec_logger=exec_logger, - eval_logger=eval_logger, n_step=1, batch_size=32, ).build() diff --git a/mava/systems/tf/madqn/training.py b/mava/systems/tf/madqn/training.py index 19224215c..c2c7e2bdb 100644 --- a/mava/systems/tf/madqn/training.py +++ b/mava/systems/tf/madqn/training.py @@ -431,6 +431,7 @@ def __init__( exploration_scheduler: LinearExplorationScheduler, communication_module: BaseCommunicationModule, clipping: bool = True, + fingerprint: bool = False, counter: counting.Counter = None, logger: loggers.Logger = None, checkpoint: bool = True, @@ -448,6 +449,7 @@ def __init__( shared_weights=shared_weights, exploration_scheduler=exploration_scheduler, clipping=clipping, + fingerprint=fingerprint, counter=counter, logger=logger, checkpoint=checkpoint, From 957fa9a577c0776e9335ae3835b1d167016edb61 Mon Sep 17 00:00:00 2001 From: Kevin Eloff Date: Sat, 22 May 2021 14:28:43 +0200 Subject: [PATCH 017/188] feat: general comm network --- .../run_recurrent_comm_madqn.py | 69 ++++++++---------- mava/components/tf/networks/__init__.py | 1 + mava/components/tf/networks/communication.py | 72 +++++++++++++++++++ 3 files changed, 103 insertions(+), 39 deletions(-) create mode 100644 mava/components/tf/networks/communication.py diff --git a/examples/debugging_envs/run_recurrent_comm_madqn.py b/examples/debugging_envs/run_recurrent_comm_madqn.py index 904748bf5..e8c1169c3 100644 --- a/examples/debugging_envs/run_recurrent_comm_madqn.py +++ b/examples/debugging_envs/run_recurrent_comm_madqn.py @@ -15,16 +15,14 @@ import functools from datetime import datetime -from typing import Any, Dict, Mapping, Optional, Sequence, Union +from typing import Any, Mapping, Optional import launchpad as lp -import numpy as np import sonnet as snt import tensorflow as tf from absl import app, flags from acme import types -from acme.wrappers.gym_wrapper import _convert_to_spec -from gym import spaces +from acme.tf import networks from launchpad.nodes.python.local_multi_processing import PythonProcess from mava import specs as mava_specs @@ -32,7 +30,10 @@ BroadcastedCommunication, ) from mava.components.tf.modules.exploration import LinearExplorationScheduler -from mava.components.tf.networks import DIALPolicy, epsilon_greedy_action_selector +from mava.components.tf.networks import ( + CommunicationNetwork, + epsilon_greedy_action_selector, +) from mava.systems.tf import madqn from mava.systems.tf.madqn.execution import MADQNRecurrentCommExecutor from mava.systems.tf.madqn.training import RecurrentCommMADQNTrainer @@ -62,11 +63,7 @@ def make_networks( environment_spec: mava_specs.MAEnvironmentSpec, - q_networks_layer_sizes: Union[Dict[str, Sequence], Sequence] = ( - 512, - 512, - 256, - ), + message_size: int = 10, shared_weights: bool = True, ) -> Mapping[str, types.TensorTransformation]: """Creates networks used by the agents.""" @@ -78,9 +75,6 @@ def make_networks( type_specs = {key.split("_")[0]: specs[key] for key in specs.keys()} specs = type_specs - if isinstance(q_networks_layer_sizes, Sequence): - q_networks_layer_sizes = {key: q_networks_layer_sizes for key in specs.keys()} - def action_selector_fn( q_values: types.NestedTensor, legal_actions: types.NestedTensor, @@ -95,34 +89,31 @@ def action_selector_fn( for key in specs.keys(): # Get total number of action dimensions from action spec. - # num_dimensions = specs[key].actions.num_values - - # Create the policy network. - # q_network = snt.DeepRNN( - # [ - # networks.LayerNormMLP( - # q_networks_layer_sizes[key], activate_final=False - # ), - # snt.LSTM(25), - # snt.nets.MLP([128]), - # networks.NearZeroInitializedLinear(num_dimensions), - # ] - # ) - - q_network = DIALPolicy( - action_spec=specs[key].actions, - message_spec=_convert_to_spec( - spaces.Box(-np.inf, np.inf, (10,), dtype=np.float32) + num_dimensions = specs[key].actions.num_values + + q_network = CommunicationNetwork( + networks.LayerNormMLP( + (128,), + activate_final=True, + ), + networks.LayerNormMLP( + (128,), + activate_final=True, + ), + snt.LSTM(128), + snt.Sequential( + [ + networks.LayerNormMLP((128,), activate_final=True), + networks.NearZeroInitializedLinear(num_dimensions), + networks.TanhToSpec(specs[key].actions), + ] ), - gru_hidden_size=256, - gru_layers=2, - task_mlp_size=( - 256, - 256, + snt.Sequential( + [ + networks.LayerNormMLP((128, message_size), activate_final=True), + ] ), - message_in_mlp_size=(256,), - message_out_mlp_size=(256,), - output_mlp_size=(256,), + message_size=message_size, ) # epsilon greedy action selector diff --git a/mava/components/tf/networks/__init__.py b/mava/components/tf/networks/__init__.py index 01f27652b..fcb25d850 100644 --- a/mava/components/tf/networks/__init__.py +++ b/mava/components/tf/networks/__init__.py @@ -15,6 +15,7 @@ from mava.components.tf.networks.additive import AdditiveMixingNetwork +from mava.components.tf.networks.communication import CommunicationNetwork from mava.components.tf.networks.dial import DIALPolicy from mava.components.tf.networks.epsilon_greedy import epsilon_greedy_action_selector from mava.components.tf.networks.fingerprints import ObservationNetworkWithFingerprint diff --git a/mava/components/tf/networks/communication.py b/mava/components/tf/networks/communication.py new file mode 100644 index 000000000..faeead858 --- /dev/null +++ b/mava/components/tf/networks/communication.py @@ -0,0 +1,72 @@ +# Copyright 2021 [...placeholder...]. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Optional + +import sonnet as snt +import tensorflow as tf +from acme.tf import utils as tf2_utils + + +class CommunicationNetwork(snt.Module): + def __init__( + self, + obs_in_network: snt.Module, + comm_in_network: snt.Module, + core_network: snt.RNNCore, + action_head: snt.Module, + comm_head: snt.Module, + message_size: int, + name: str = None, + ): + super(CommunicationNetwork, self).__init__(name=name) + + self._obs_in_network = obs_in_network + self._comm_in_network = comm_in_network + self._core_network = core_network + self._action_head = action_head + self._comm_head = comm_head + + self._message_size = message_size + + def initial_state(self, batch_size: int = 1) -> snt.Module: + # Requires core_network to be of type snt.RNNCore + return self._core_network.initial_state(batch_size) + + def initial_message(self, batch_size: int = 1) -> snt.Module: + # TODO Kevin: get output dim of comm_head + return tf.zeros([batch_size, self._message_size], dtype=tf.float32) + + def __call__( + self, + x: snt.Module, + state: Optional[snt.Module] = None, + message: Optional[snt.Module] = None, + ) -> snt.Module: + if state is None: + state = self.initial_state(x.shape[0]) + if message is None: + message = self.initial_message(x.shape[0]) + + obs_in = self._obs_in_network(x) + comm_in = self._comm_in_network(message) + + core_in = tf2_utils.batch_concat([obs_in, comm_in]) + + core_out, state = self._core_network(core_in, state) + + action = self._action_head(core_out) + message = self._comm_head(core_out) + + return (action, message), state From 5fc632b903bb5960fddf20f6981ee283e2a99a68 Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Sat, 22 May 2021 16:49:09 +0200 Subject: [PATCH 018/188] update dockerfile to install sc2 --- Dockerfile | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/Dockerfile b/Dockerfile index 584f4b603..8e2b6edda 100755 --- a/Dockerfile +++ b/Dockerfile @@ -32,4 +32,9 @@ RUN python -m pip install --upgrade pip RUN python -m pip install -e .[flatland] RUN python -m pip install -e .[tf,envs,reverb,launchpad,testing_formatting,record_episode] +# install starcraft 2 environment +RUN apt-get -y install git +RUN python -m pip install git+https://github.com/oxwhirl/smac.git +ENV SC2PATH /home/app/mava/3rdparty/StarCraftII + EXPOSE 6006 From 77cb03736c81850d0055e357e387061bb302d499 Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Sat, 22 May 2021 16:49:30 +0200 Subject: [PATCH 019/188] add 3rd party libraries to be ignored --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 3b997dbf1..af142c8a2 100644 --- a/.gitignore +++ b/.gitignore @@ -147,3 +147,4 @@ mava/workspace.code-workspace .DS_Store mava_testing .devcontainer/devcontainer.json +3rdparty From 70b1e9142674a44d1515196a11339a537726839c Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Sat, 22 May 2021 16:49:51 +0200 Subject: [PATCH 020/188] remove smac from setup --- setup.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/setup.py b/setup.py index 12099540b..d24212070 100644 --- a/setup.py +++ b/setup.py @@ -39,17 +39,12 @@ "trfl", ] -# NOTE (St John, for Kale-ab) The SMAC installed when callling `pip install -# smac` seems to be a different package. We actually need to install SMAC -# via pip `install git+https://github.com/oxwhirl/smac.git`. PySC2 is the -# single agent env for StarCraft II, required by SMAC. env_requirements = [ "pettingzoo>=1.8.2", "multi_agent_ale_py", "supersuit", "pygame", "pysc2", - "smac", ] launchpad_requirements = [ From 4555a5cde984494d623c84e9ceb9b04298d92b23 Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Sat, 22 May 2021 16:51:11 +0200 Subject: [PATCH 021/188] add mac stuff to be ignored --- .gitignore | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.gitignore b/.gitignore index af142c8a2..ff53a4e0a 100644 --- a/.gitignore +++ b/.gitignore @@ -27,6 +27,9 @@ share/python-wheels/ *.egg MANIFEST +# mac +__MACOSX + # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. From 0aeceba6e9dcfa969009b37784b1752767858a80 Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Sat, 22 May 2021 16:51:43 +0200 Subject: [PATCH 022/188] Minor changes to pz wrapper --- mava/wrappers/pettingzoo.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mava/wrappers/pettingzoo.py b/mava/wrappers/pettingzoo.py index 1671fd3ea..1c9a64164 100644 --- a/mava/wrappers/pettingzoo.py +++ b/mava/wrappers/pettingzoo.py @@ -218,7 +218,7 @@ def __init__( self._environment, env_preprocess_wrappers ) - def reset(self) -> dm_env.TimeStep: + def reset(self) -> Tuple[dm_env.TimeStep, np.array]: """Resets the episode.""" self._reset_next_step = False self._step_type = dm_env.StepType.FIRST From 0902e39412c8deb2ec9cc9f1ee3cec433a55e655 Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Sat, 22 May 2021 16:52:34 +0200 Subject: [PATCH 023/188] feat: environment wrapper for sc2 --- install_sc2.sh | 33 ++++ mava/wrappers/{starcraft.py => starcraft2.py} | 149 +++++++++--------- 2 files changed, 110 insertions(+), 72 deletions(-) create mode 100755 install_sc2.sh rename mava/wrappers/{starcraft.py => starcraft2.py} (67%) diff --git a/install_sc2.sh b/install_sc2.sh new file mode 100755 index 000000000..bff0d8b54 --- /dev/null +++ b/install_sc2.sh @@ -0,0 +1,33 @@ +#!/bin/bash +# Install SC2 and add the custom maps + +mkdir 3rdparty +cd 3rdparty + +export SC2PATH=`pwd`'/StarCraftII' +echo 'SC2PATH is set to '$SC2PATH + +if [ ! -d $SC2PATH ]; then + echo 'StarCraftII is not installed. Installing now ...'; + wget http://blzdistsc2-a.akamaihd.net/Linux/SC2.4.10.zip + unzip -P iagreetotheeula SC2.4.10.zip + rm -rf SC2.4.10.zip +else + echo 'StarCraftII is already installed.' +fi + +echo 'Adding SMAC maps.' +MAP_DIR="$SC2PATH/Maps/" +echo 'MAP_DIR is set to '$MAP_DIR + +if [ ! -d $MAP_DIR ]; then + mkdir -p $MAP_DIR +fi + +cd .. +wget https://github.com/oxwhirl/smac/releases/download/v0.1-beta1/SMAC_Maps.zip +unzip SMAC_Maps.zip +mv SMAC_Maps $MAP_DIR +rm -rf SMAC_Maps.zip + +echo 'StarCraft II and SMAC are installed.' \ No newline at end of file diff --git a/mava/wrappers/starcraft.py b/mava/wrappers/starcraft2.py similarity index 67% rename from mava/wrappers/starcraft.py rename to mava/wrappers/starcraft2.py index b838bcbe1..2e86a3ee3 100644 --- a/mava/wrappers/starcraft.py +++ b/mava/wrappers/starcraft2.py @@ -51,7 +51,7 @@ def __init__(self, **smac_args: Optional[Tuple]) -> None: self._ready_agents: List = [] self.observation_space = Dict( { - "obs": Box(-1, 1, shape=(self._env.get_obs_size(),)), + "observation": Box(-1, 1, shape=(self._env.get_obs_size(),)), "action_mask": Box(0, 1, shape=(self._env.get_total_actions(),)), } ) @@ -62,45 +62,49 @@ def reset(self) -> Tuple[dm_env.TimeStep, np.array]: Returns: obs (dict): New observations for each ready agent. """ + self._env_done = False self._reset_next_step = False self._step_type = dm_env.StepType.FIRST - # TODO Check the form of this state list and convert for return. - obs_list, state_list = self._env.reset() + # reset internal SC2 env + obs_list, state = self._env.reset() + + # Convert observations observe: Dict[str, np.ndarray] = {} + self.possible_agents = [] for i, obs in enumerate(obs_list): agent = f"agent_{i}" - observe[agent] = { # TODO Only obs in this Dict or mask too? + observe[agent] = { "action_mask": np.array(self._env.get_avail_agent_actions(i)), - "obs": obs, + "observation": obs, } + self.possible_agents.append(agent) + observations = self._convert_observations( + observe, {agent: False for agent in self.possible_agents} + ) self._ready_agents = list(range(len(obs_list))) + # create discount spec discount_spec = self.discount_spec() self._discounts = { agent: convert_np_type(discount_spec[agent].dtype, 1) for agent in self._environment.possible_agents } - observations = self._convert_observations( - observe, {agent: False for agent in self.possible_agents} - ) + + # create rewards spec rewards_spec = self.reward_spec() rewards = { agent: convert_np_type(rewards_spec[agent].dtype, 0) for agent in self.possible_agents } - discount_spec = self.discount_spec() - self._discounts = { - agent: convert_np_type(discount_spec[agent].dtype, 1) - for agent in self.possible_agents - } + # dm_env timestep timestep = parameterized_restart(rewards, self._discounts, observations) - return timestep, {"s_t": state_list} # TODO Convert this to correct form + return timestep, {"s_t": state} - def step(self, action_dict: Dict) -> Tuple[dm_env.TimeStep, np.array]: + def step(self, actions: Dict[str, np.ndarray]) -> Tuple[dm_env.TimeStep, np.array]: """Returns observations from ready agents. The returns are dicts mapping from agent_id strings to values. The number of agents in the env can vary over time. @@ -116,34 +120,36 @@ def step(self, action_dict: Dict) -> Tuple[dm_env.TimeStep, np.array]: if self._reset_next_step: return self.reset() - actions = [] - for i in self._ready_agents: - if i not in action_dict: - raise ValueError(f"You must supply an action for agent: {i}") - actions.append(action_dict[i]) - - if len(actions) != len(self._ready_agents): - raise ValueError( - f"Number of actions ({len(actions)}) does not match number \ - of ready agents (len(self._ready_agents))." - ) - - rewards, dones, infos = self._env.step(actions) - obs_list = self._env.get_obs() - - return_obs = {} + # actions = [] + # for i in self._ready_agents: + # if i not in action_dict: + # raise ValueError(f"You must supply an action for agent: {i}") + # actions.append(action_dict[i]) + + # if len(actions) != len(self._ready_agents): + # raise ValueError( + # f"Number of actions ({len(actions)}) does not match number \ + # of ready agents (len(self._ready_agents))." + # ) + + actions_feed = list(actions.values()) + reward, terminated, info = self._env.step(actions_feed) + obs_list = self._environment.get_obs() + state = self._environment.get_state() + self._env_done = terminated + + observations = {} for i, obs in enumerate(obs_list): - return_obs[i] = { + observations[f"agent_{i}"] = { "action_mask": self._env.get_avail_agent_actions(i), - "obs": obs, + "observation": obs, } - rewards = {i: rewards / len(obs_list) for i in range(len(obs_list))} - dones = {i: dones for i in range(len(obs_list))} - infos = {i: infos for i in range(len(obs_list))} - self._ready_agents = list(range(len(obs_list))) + rewards = {agent: reward for agent in actions.keys()} + dones = {agent: terminated for agent in actions.keys()} - rewards_spec = self.reward_spec() + # self._ready_agents = list(range(len(obs_list))) + rewards_spec = self.reward_spec() # Handle empty rewards if not rewards: rewards = { @@ -156,8 +162,8 @@ def step(self, action_dict: Dict) -> Tuple[dm_env.TimeStep, np.array]: for agent, reward in rewards.items() } - if return_obs: - return_obs = self._convert_observations(return_obs, dones) + if observations: + observations = self._convert_observations(observations, dones) if self.env_done(): self._step_type = dm_env.StepType.LAST @@ -166,48 +172,64 @@ def step(self, action_dict: Dict) -> Tuple[dm_env.TimeStep, np.array]: self._step_type = dm_env.StepType.MID timestep = dm_env.TimeStep( - observation=return_obs, + observation=observations, reward=rewards, discount=self._discounts, step_type=self._step_type, ) - return timestep # TODO return global state as well + return timestep, state def env_done(self) -> bool: """ Returns a bool indicating if all agents in env are done. """ - return self._environment.env_done # TODO Check SMAC has this function + return self._env_done + + def _convert_observations( + self, observes: Dict[str, np.ndarray], dones: Dict[str, bool] + ) -> types.Observation: + observations: Dict[str, types.OLT] = {} + for agent, observation in observes.items(): + if isinstance(observation, dict) and "action_mask" in observation: + legals = observation["action_mask"] + observation = observation["observation"] + else: + legals = np.ones( + _convert_to_spec(self.action_space).shape, + dtype=self.action_space.dtype, + ) + observations[agent] = types.OLT( + observation=observation, + legal_actions=legals, + terminal=np.asarray([dones[agent]], dtype=np.float32), + ) + + return observations def observation_spec(self) -> types.Observation: return { agent: types.OLT( - observation=_convert_to_spec( - self._environment.observation_spaces[agent] - ), - legal_actions=_convert_to_spec(self._environment.action_spaces[agent]), + observation=_convert_to_spec(self.observation_space["observation"]), + legal_actions=_convert_to_spec(self.observation_space["action_mask"]), terminal=specs.Array((1,), np.float32), ) - for agent in self._environment.possible_agents + for agent in self.possible_agents } def action_spec(self) -> Dict[str, specs.DiscreteArray]: return { - agent: _convert_to_spec(self._environment.action_spaces[agent]) - for agent in self._environment.possible_agents + agent: _convert_to_spec(self._environment.action_space) + for agent in self.possible_agents } def reward_spec(self) -> Dict[str, specs.Array]: - return { - agent: specs.Array((), np.float32) - for agent in self._environment.possible_agents - } + return {agent: specs.Array((), np.float32) for agent in self.possible_agents} def discount_spec(self) -> Dict[str, specs.BoundedArray]: return { agent: specs.BoundedArray((), np.float32, minimum=0, maximum=1.0) - for agent in self._environment.possible_agents + for agent in self.possible_agents } def extra_spec(self) -> Dict[str, specs.BoundedArray]: @@ -217,28 +239,11 @@ def extra_spec(self) -> Dict[str, specs.BoundedArray]: def agents(self) -> List: return self._environment.agents - @property - def possible_agents(self) -> List: - return self._environment.possible_agents - @property def environment(self) -> ParallelEnv: """Returns the wrapped environment.""" return self._environment - @property - def current_agent(self) -> Any: - return self._environment.agent_selection - def __getattr__(self, name: str) -> Any: """Expose any other attributes of the underlying environment.""" return getattr(self._environment, name) - - # Note sure we need these next methods. Comes from RLlib wrapper. - def close(self) -> None: - """Close the environment""" - self._env.close() - - def seed(self, seed: int) -> None: - random.seed(seed) - np.random.seed(seed) From 894320d17880519fd6f7a0111eab7055835a9fbe Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Sat, 22 May 2021 17:01:38 +0200 Subject: [PATCH 024/188] env factory for sc2 --- mava/utils/environments/starcraft2_utils.py | 54 +++++++++++++++++++++ 1 file changed, 54 insertions(+) create mode 100644 mava/utils/environments/starcraft2_utils.py diff --git a/mava/utils/environments/starcraft2_utils.py b/mava/utils/environments/starcraft2_utils.py new file mode 100644 index 000000000..5af3a9f2e --- /dev/null +++ b/mava/utils/environments/starcraft2_utils.py @@ -0,0 +1,54 @@ +# python3 +# Copyright 2021 [...placeholder...]. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Starcraft 2 environment factory.""" + +import importlib +from typing import Any, List, Optional, Union + +import dm_env +import numpy as np + +from smac.env import StarCraft2Env +from mava.wrappers import ParallelEnvWrapper, SMACEnvWrapper + + +def make_environment( + evaluation: bool = False, + map_name="3m", + random_seed: Optional[int] = None, + **kwargs: Any, +) -> dm_env.Environment: + """Wraps an Pettingzoo environment. + + Args: + env_class: str, class of the environment, e.g. MPE or Atari. + env_name: str, name of environment, .e.g simple_spread or Pong. + evaluation: bool, to change the behaviour during evaluation. + + Returns: + A Pettingzoo environment wrapped as a DeepMind environment. + """ + del evaluation + + env = StarCraft2Env(map_name=map_name, **kwargs) + + # wrap starcraft 2 environment + environment = SMACEnvWrapper(env) + + if random_seed and hasattr(environment, "seed"): + environment.seed(random_seed) + + return environment \ No newline at end of file From 8714f339dc89d92bd02494f6b6bbbdfc7df28cb1 Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Sat, 22 May 2021 17:13:57 +0200 Subject: [PATCH 025/188] have mypy ignore smace --- mypy.ini | 3 +++ 1 file changed, 3 insertions(+) diff --git a/mypy.ini b/mypy.ini index e24c26a89..e9b090c04 100644 --- a/mypy.ini +++ b/mypy.ini @@ -75,3 +75,6 @@ ignore_missing_imports = True [mypy-matplotlib.*] ignore_missing_imports = True + +[mypy-smac.*] +ignore_missing_imports = True From 97b6e2878cc0b77c22994ce9c7980f30816f912c Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Sat, 22 May 2021 17:14:18 +0200 Subject: [PATCH 026/188] add install script for sc2 --- install_sc2.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/install_sc2.sh b/install_sc2.sh index bff0d8b54..b7fa1ff2d 100755 --- a/install_sc2.sh +++ b/install_sc2.sh @@ -30,4 +30,4 @@ unzip SMAC_Maps.zip mv SMAC_Maps $MAP_DIR rm -rf SMAC_Maps.zip -echo 'StarCraft II and SMAC are installed.' \ No newline at end of file +echo 'StarCraft II and SMAC are installed.' From 14c5c33634b61880a0fd118904dc52b2281493f8 Mon Sep 17 00:00:00 2001 From: Dries Date: Sat, 22 May 2021 17:19:12 +0200 Subject: [PATCH 027/188] feature: Update discrete and continuous MA-DDPG and MA-D4PG to each use their default systems. --- ...centralised_feedforward_discrete_mad4pg.py | 2 -- ...centralised_feedforward_discrete_maddpg.py | 2 -- .../run_decentralised_recurrent_mad4pg.py | 5 +-- .../run_decentralised_recurrent_maddpg.py | 5 +-- .../run_decentralised_recurrent_maddpg.py | 5 +-- mava/systems/tf/mad4pg/__init__.py | 3 +- mava/systems/tf/mad4pg/execution.py | 27 +++++++++++++-- mava/systems/tf/mad4pg/system.py | 4 +-- mava/systems/tf/maddpg/__init__.py | 3 ++ mava/systems/tf/maddpg/builder.py | 4 ++- mava/systems/tf/maddpg/execution.py | 33 +++++++++++++++---- mava/systems/tf/maddpg/system.py | 4 +-- 12 files changed, 72 insertions(+), 25 deletions(-) diff --git a/examples/debugging_envs/run_decentralised_feedforward_discrete_mad4pg.py b/examples/debugging_envs/run_decentralised_feedforward_discrete_mad4pg.py index 3473c4ac3..a1d568905 100644 --- a/examples/debugging_envs/run_decentralised_feedforward_discrete_mad4pg.py +++ b/examples/debugging_envs/run_decentralised_feedforward_discrete_mad4pg.py @@ -31,7 +31,6 @@ from mava import specs as mava_specs from mava.components.tf.networks.mad4pg import DiscreteValuedHead from mava.systems.tf import mad4pg -from mava.systems.tf.mad4pg.execution import MAD4PGDiscreteFeedForwardExecutor from mava.utils import lp_utils from mava.utils.environments import debugging_utils from mava.utils.loggers import logger_utils @@ -183,7 +182,6 @@ def main(_: Any) -> None: network_factory=network_factory, logger_factory=logger_factory, num_executors=2, - executor_fn=MAD4PGDiscreteFeedForwardExecutor, policy_optimizer=snt.optimizers.Adam(learning_rate=1e-4), critic_optimizer=snt.optimizers.Adam(learning_rate=1e-4), checkpoint_subpath=checkpoint_dir, diff --git a/examples/debugging_envs/run_decentralised_feedforward_discrete_maddpg.py b/examples/debugging_envs/run_decentralised_feedforward_discrete_maddpg.py index 7acb89533..4ee00b3d3 100644 --- a/examples/debugging_envs/run_decentralised_feedforward_discrete_maddpg.py +++ b/examples/debugging_envs/run_decentralised_feedforward_discrete_maddpg.py @@ -32,7 +32,6 @@ from mava import specs as mava_specs from mava.systems.tf import maddpg -from mava.systems.tf.maddpg.execution import MADDPGDiscreteFeedForwardExecutor from mava.utils import lp_utils from mava.utils.environments import debugging_utils @@ -194,7 +193,6 @@ def main(_: Any) -> None: network_factory=network_factory, logger_config=logger_config, num_executors=2, - executor_fn=MADDPGDiscreteFeedForwardExecutor, policy_optimizer=snt.optimizers.Adam(learning_rate=1e-4), critic_optimizer=snt.optimizers.Adam(learning_rate=1e-4), checkpoint_subpath=checkpoint_dir, diff --git a/examples/debugging_envs/run_decentralised_recurrent_mad4pg.py b/examples/debugging_envs/run_decentralised_recurrent_mad4pg.py index 4f3e26c68..d90e3dc2b 100644 --- a/examples/debugging_envs/run_decentralised_recurrent_mad4pg.py +++ b/examples/debugging_envs/run_decentralised_recurrent_mad4pg.py @@ -30,7 +30,8 @@ from mava import specs as mava_specs from mava.components.tf.networks.mad4pg import DiscreteValuedHead -from mava.systems.tf import executors, mad4pg +from mava.systems.tf import mad4pg +from mava.systems.tf.mad4pg.execution import MAD4PGRecurrentExecutor from mava.systems.tf.mad4pg.training import DecentralisedRecurrentMAD4PGTrainer from mava.utils import lp_utils from mava.utils.environments import debugging_utils @@ -166,7 +167,7 @@ def main(_: Any) -> None: logger_factory=logger_factory, num_executors=2, trainer_fn=DecentralisedRecurrentMAD4PGTrainer, - executor_fn=executors.RecurrentExecutor, + executor_fn=MAD4PGRecurrentExecutor, checkpoint_subpath=checkpoint_dir, ).build() diff --git a/examples/debugging_envs/run_decentralised_recurrent_maddpg.py b/examples/debugging_envs/run_decentralised_recurrent_maddpg.py index a8acc7360..44c5308f6 100644 --- a/examples/debugging_envs/run_decentralised_recurrent_maddpg.py +++ b/examples/debugging_envs/run_decentralised_recurrent_maddpg.py @@ -28,7 +28,8 @@ from launchpad.nodes.python.local_multi_processing import PythonProcess from mava import specs as mava_specs -from mava.systems.tf import executors, maddpg +from mava.systems.tf import maddpg +from mava.systems.tf.mad4pg.execution import MADDPGRecurrentExecutor from mava.systems.tf.maddpg.training import DecentralisedRecurrentMADDPGTrainer from mava.utils import lp_utils from mava.utils.environments import debugging_utils @@ -161,7 +162,7 @@ def main(_: Any) -> None: logger_factory=logger_factory, num_executors=2, trainer_fn=DecentralisedRecurrentMADDPGTrainer, - executor_fn=executors.RecurrentExecutor, + executor_fn=MADDPGRecurrentExecutor, checkpoint_subpath=checkpoint_dir, ).build() diff --git a/examples/petting_zoo/run_decentralised_recurrent_maddpg.py b/examples/petting_zoo/run_decentralised_recurrent_maddpg.py index fa34801cc..2f6e7f046 100644 --- a/examples/petting_zoo/run_decentralised_recurrent_maddpg.py +++ b/examples/petting_zoo/run_decentralised_recurrent_maddpg.py @@ -29,7 +29,8 @@ from launchpad.nodes.python.local_multi_processing import PythonProcess from mava import specs as mava_specs -from mava.systems.tf import executors, maddpg +from mava.systems.tf import maddpg +from mava.systems.tf.maddpg.execution import MADDPGRecurrentExecutor from mava.systems.tf.maddpg.training import DecentralisedRecurrentMADDPGTrainer from mava.utils import lp_utils from mava.utils.environments import pettingzoo_utils @@ -163,7 +164,7 @@ def main(_: Any) -> None: logger_factory=logger_factory, num_executors=2, trainer_fn=DecentralisedRecurrentMADDPGTrainer, - executor_fn=executors.RecurrentExecutor, + executor_fn=MADDPGRecurrentExecutor, policy_optimizer=snt.optimizers.Adam(learning_rate=1e-4), critic_optimizer=snt.optimizers.Adam(learning_rate=1e-4), checkpoint_subpath=checkpoint_dir, diff --git a/mava/systems/tf/mad4pg/__init__.py b/mava/systems/tf/mad4pg/__init__.py index 1adbe4d14..5b3235d5f 100644 --- a/mava/systems/tf/mad4pg/__init__.py +++ b/mava/systems/tf/mad4pg/__init__.py @@ -14,7 +14,6 @@ # limitations under the License. """Implementations of a MAD4PG agent.""" - from mava.systems.tf.mad4pg.system import MAD4PG from mava.systems.tf.mad4pg.training import ( BaseMAD4PGTrainer, @@ -22,3 +21,5 @@ DecentralisedMAD4PGTrainer, StateBasedMAD4PGTrainer, ) +from mava.systems.tf.mad4pg.execution import (MAD4PGFeedForwardExecutor, + MAD4PGRecurrentExecutor) diff --git a/mava/systems/tf/mad4pg/execution.py b/mava/systems/tf/mad4pg/execution.py index 336fc251b..30d4700eb 100644 --- a/mava/systems/tf/mad4pg/execution.py +++ b/mava/systems/tf/mad4pg/execution.py @@ -19,17 +19,38 @@ from acme.tf import variable_utils as tf2_variable_utils from mava import adders -from mava.systems.tf.maddpg.execution import MADDPGDiscreteFeedForwardExecutor +from mava.systems.tf.maddpg.execution import MADDPGFeedForwardExecutor, MADDPGRecurrentExecutor -class MAD4PGDiscreteFeedForwardExecutor(MADDPGDiscreteFeedForwardExecutor): - """A feed-forward executor for discrete actions in MAD4PG. +class MAD4PGFeedForwardExecutor(MADDPGFeedForwardExecutor): + """A feed-forward executor for MAD4PG. An executor based on a feed-forward policy for each agent in the system which takes non-batched observations and outputs non-batched actions. It also allows adding experiences to replay and updating the weights from the policy on the learner. """ + def __init__( + self, + policy_networks: Dict[str, snt.Module], + adder: Optional[adders.ParallelAdder] = None, + variable_client: Optional[tf2_variable_utils.VariableClient] = None, + shared_weights: bool = True, + ): + super().__init__( + policy_networks=policy_networks, + adder=adder, + variable_client=variable_client, + shared_weights=shared_weights, + ) +class MAD4PGRecurrentExecutor(MADDPGRecurrentExecutor): + """A recurrent executor for MAD4PG. + An executor based on a recurrent policy for each agent in the system + which takes non-batched observations and outputs non-batched actions. + It also allows adding experiences to replay and updating the weights + from the policy on the learner. + """ + def __init__( self, policy_networks: Dict[str, snt.Module], diff --git a/mava/systems/tf/mad4pg/system.py b/mava/systems/tf/mad4pg/system.py index c19131338..f89a09668 100644 --- a/mava/systems/tf/mad4pg/system.py +++ b/mava/systems/tf/mad4pg/system.py @@ -24,7 +24,7 @@ from mava import specs as mava_specs from mava.components.tf.architectures import DecentralisedQValueActorCritic from mava.environment_loop import ParallelEnvironmentLoop -from mava.systems.tf import executors +from mava.systems.tf.maddpg.execution import MADDPGFeedForwardExecutor from mava.systems.tf.mad4pg import training from mava.systems.tf.maddpg.system import MADDPG from mava.utils.loggers import MavaLogger @@ -50,7 +50,7 @@ def __init__( Type[training.BaseMAD4PGTrainer], Type[training.BaseRecurrentMAD4PGTrainer], ] = training.DecentralisedMAD4PGTrainer, - executor_fn: Type[core.Executor] = executors.FeedForwardExecutor, + executor_fn: Type[core.Executor] = MADDPGFeedForwardExecutor, num_executors: int = 1, num_caches: int = 0, environment_spec: mava_specs.MAEnvironmentSpec = None, diff --git a/mava/systems/tf/maddpg/__init__.py b/mava/systems/tf/maddpg/__init__.py index e2213e2b1..bdb6dd1eb 100644 --- a/mava/systems/tf/maddpg/__init__.py +++ b/mava/systems/tf/maddpg/__init__.py @@ -23,3 +23,6 @@ NetworkedMADDPGTrainer, StateBasedMADDPGTrainer, ) + +from mava.systems.tf.mad4pg.execution import (MADDPGFeedForwardExecutor, + MADDPGRecurrentExecutor) diff --git a/mava/systems/tf/maddpg/builder.py b/mava/systems/tf/maddpg/builder.py index 2c8684054..42fea1d5c 100644 --- a/mava/systems/tf/maddpg/builder.py +++ b/mava/systems/tf/maddpg/builder.py @@ -31,6 +31,7 @@ from mava.systems.builders import SystemBuilder from mava.systems.tf import executors from mava.systems.tf.maddpg import training +from mava.systems.tf.maddpg.execution import MADDPGFeedForwardExecutor from mava.wrappers import DetailedTrainerStatistics, NetworkStatisticsActorCritic BoundedArray = dm_specs.BoundedArray @@ -107,7 +108,7 @@ def __init__( Type[training.BaseMADDPGTrainer], Type[training.BaseRecurrentMADDPGTrainer], ] = training.DecentralisedMADDPGTrainer, - executor_fn: Type[core.Executor] = executors.FeedForwardExecutor, + executor_fn: Type[core.Executor] = MADDPGFeedForwardExecutor, extra_specs: Dict[str, Any] = {}, ): """Args: @@ -287,6 +288,7 @@ def make_executor( # Create the actor which defines how we take actions. return self._executor_fn( policy_networks=policy_networks, + agent_specs=self._config.environment_spec.get_agent_specs(), shared_weights=shared_weights, variable_client=variable_client, adder=adder, diff --git a/mava/systems/tf/maddpg/execution.py b/mava/systems/tf/maddpg/execution.py index 279e77a02..6ac12e005 100644 --- a/mava/systems/tf/maddpg/execution.py +++ b/mava/systems/tf/maddpg/execution.py @@ -20,6 +20,10 @@ import tensorflow as tf import tensorflow_probability as tfp from acme import types +from dm_env import specs +Array = specs.Array +BoundedArray = specs.BoundedArray +DiscreteArray = specs.DiscreteArray # Internal imports. from acme.tf import utils as tf2_utils @@ -27,11 +31,11 @@ from mava import adders from mava.systems.tf import executors - +from acme.specs import EnvironmentSpec tfd = tfp.distributions -class MADDPGDiscreteFeedForwardExecutor(executors.FeedForwardExecutor): +class MADDPGFeedForwardExecutor(executors.FeedForwardExecutor): """A feed-forward executor for discrete actions in MADDPG. An executor based on a feed-forward policy for each agent in the system which takes non-batched observations and outputs non-batched actions. @@ -42,6 +46,7 @@ class MADDPGDiscreteFeedForwardExecutor(executors.FeedForwardExecutor): def __init__( self, policy_networks: Dict[str, snt.Module], + agent_specs: Dict[str, EnvironmentSpec], adder: Optional[adders.ParallelAdder] = None, variable_client: Optional[tf2_variable_utils.VariableClient] = None, shared_weights: bool = True, @@ -61,10 +66,10 @@ def __init__( self._adder = adder self._variable_client = variable_client self._policy_networks = policy_networks + self._agent_specs = agent_specs self._shared_weights = shared_weights - # TODO: Add this back in - # @tf.function + @tf.function def _policy( self, agent: str, observation: types.NestedTensor ) -> types.NestedTensor: @@ -78,8 +83,14 @@ def _policy( # Compute the policy, conditioned on the observation. policy = self._policy_networks[agent_key](batched_observation) - # Sample from the policy if it is stochastic. - action = tf.math.argmax(policy, axis=1) + # TODO (dries): Make this support hybrid action spaces. + if type(self._agent_specs[agent].actions) == BoundedArray: + # Continuous action + action = policy + elif type(self._agent_specs[agent].actions) == DiscreteArray: + action = tf.math.argmax(policy, axis=1) + else: + raise NotImplementedError return action, policy @@ -129,3 +140,13 @@ def observe( env_actions, policy = actions # TODO (dries): Sort out this mypy issue. self._adder.add(policy, next_timestep, next_extras) # type: ignore + +class MADDPGRecurrentExecutor(executors.FeedForwardExecutor): + def __init__( + self, + policy_networks: Dict[str, snt.Module], + adder: Optional[adders.ParallelAdder] = None, + variable_client: Optional[tf2_variable_utils.VariableClient] = None, + shared_weights: bool = True, + ): + raise NotImplementedError \ No newline at end of file diff --git a/mava/systems/tf/maddpg/system.py b/mava/systems/tf/maddpg/system.py index e8a39a0d1..19a4b6498 100644 --- a/mava/systems/tf/maddpg/system.py +++ b/mava/systems/tf/maddpg/system.py @@ -32,13 +32,13 @@ from mava.components.tf.architectures import DecentralisedQValueActorCritic from mava.environment_loop import ParallelEnvironmentLoop from mava.systems.tf import executors +from mava.systems.tf.maddpg.execution import MADDPGFeedForwardExecutor from mava.systems.tf import savers as tf2_savers from mava.systems.tf.maddpg import builder, training from mava.utils import lp_utils from mava.utils.loggers import MavaLogger, logger_utils from mava.wrappers import DetailedPerAgentStatistics - class MADDPG: """MADDPG system. This implements a single-process DDPG system. This is an actor-critic based @@ -59,7 +59,7 @@ def __init__( Type[training.BaseMADDPGTrainer], Type[training.BaseRecurrentMADDPGTrainer], ] = training.DecentralisedMADDPGTrainer, - executor_fn: Type[core.Executor] = executors.FeedForwardExecutor, + executor_fn: Type[core.Executor] = MADDPGFeedForwardExecutor, num_executors: int = 1, num_caches: int = 0, environment_spec: mava_specs.MAEnvironmentSpec = None, From 0bcd3326ab29fecde65746beb0406e79e0491dd5 Mon Sep 17 00:00:00 2001 From: Kevin Eloff Date: Sat, 22 May 2021 17:22:47 +0200 Subject: [PATCH 028/188] feat: dial prison learning --- examples/debugging_envs/run_recurrent_dial.py | 147 +++--- mava/systems/tf/dial/builder.py | 329 ++++++++------ mava/systems/tf/dial/execution.py | 88 ++-- mava/systems/tf/dial/system.py | 360 ++++++++------- mava/systems/tf/dial/training.py | 424 ++---------------- 5 files changed, 524 insertions(+), 824 deletions(-) diff --git a/examples/debugging_envs/run_recurrent_dial.py b/examples/debugging_envs/run_recurrent_dial.py index b4da0d726..57671b930 100644 --- a/examples/debugging_envs/run_recurrent_dial.py +++ b/examples/debugging_envs/run_recurrent_dial.py @@ -13,30 +13,35 @@ # See the License for the specific language governing permissions and # limitations under the License. -"""Example running DIAL""" import functools from datetime import datetime -from typing import Any, Dict, Mapping, Sequence, Union +from typing import Any, Mapping, Optional import launchpad as lp -import numpy as np +import sonnet as snt import tensorflow as tf from absl import app, flags from acme import types -from acme.tf import utils as tf2_utils -from acme.wrappers.gym_wrapper import _convert_to_spec -from gym import spaces +from acme.tf import networks from launchpad.nodes.python.local_multi_processing import PythonProcess from mava import specs as mava_specs -from mava.components.tf.networks import DIALPolicy +from mava.components.tf.modules.communication.broadcasted import ( + BroadcastedCommunication, +) +from mava.components.tf.modules.exploration import LinearExplorationScheduler +from mava.components.tf.networks import ( + CommunicationNetwork, + epsilon_greedy_action_selector, +) from mava.systems.tf import dial +from mava.systems.tf.madqn.execution import MADQNRecurrentCommExecutor +from mava.systems.tf.madqn.training import RecurrentCommMADQNTrainer from mava.utils import lp_utils from mava.utils.environments import debugging_utils from mava.utils.loggers import logger_utils FLAGS = flags.FLAGS -flags.DEFINE_integer("num_episodes", 30000, "Number of training episodes to run for.") flags.DEFINE_string( "env_name", "switch", @@ -47,11 +52,6 @@ "discrete", "Environment action space type (str).", ) -flags.DEFINE_integer( - "num_episodes_per_eval", - 100, - "Number of training episodes to run between evaluation " "episodes.", -) flags.DEFINE_string( "mava_id", @@ -61,103 +61,88 @@ flags.DEFINE_string("base_dir", "~/mava/", "Base dir to store experiments.") -# TODO Kevin: Define message head node correctly def make_networks( environment_spec: mava_specs.MAEnvironmentSpec, - policy_network_gru_hidden_sizes: Union[Dict[str, int], int] = 128, - policy_network_gru_layers: Union[Dict[str, int], int] = 2, - policy_network_task_mlp_sizes: Union[Dict[str, Sequence], Sequence] = (128,), - policy_network_message_in_mlp_sizes: Union[Dict[str, Sequence], Sequence] = (128,), - policy_network_message_out_mlp_sizes: Union[Dict[str, Sequence], Sequence] = (128,), - policy_network_output_mlp_sizes: Union[Dict[str, Sequence], Sequence] = (128,), - message_size: int = 1, + message_size: int = 10, shared_weights: bool = True, - sigma: float = 0.3, ) -> Mapping[str, types.TensorTransformation]: """Creates networks used by the agents.""" + specs = environment_spec.get_agent_specs() - # extra_specs = environment_spec.get_extra_specs() # Create agent_type specs if shared_weights: type_specs = {key.split("_")[0]: specs[key] for key in specs.keys()} specs = type_specs - if isinstance(policy_network_gru_hidden_sizes, int): - policy_network_gru_hidden_sizes = { - key: policy_network_gru_hidden_sizes for key in specs.keys() - } - - if isinstance(policy_network_gru_layers, int): - policy_network_gru_layers = { - key: policy_network_gru_layers for key in specs.keys() - } - - if isinstance(policy_network_task_mlp_sizes, Sequence): - policy_network_task_mlp_sizes = { - key: policy_network_task_mlp_sizes for key in specs.keys() - } - - if isinstance(policy_network_message_in_mlp_sizes, Sequence): - policy_network_message_in_mlp_sizes = { - key: policy_network_message_in_mlp_sizes for key in specs.keys() - } - - if isinstance(policy_network_message_out_mlp_sizes, Sequence): - policy_network_message_out_mlp_sizes = { - key: policy_network_message_out_mlp_sizes for key in specs.keys() - } + def action_selector_fn( + q_values: types.NestedTensor, + legal_actions: types.NestedTensor, + epsilon: Optional[tf.Variable] = None, + ) -> types.NestedTensor: + return epsilon_greedy_action_selector( + action_values=q_values, legal_actions_mask=legal_actions, epsilon=epsilon + ) - if isinstance(policy_network_output_mlp_sizes, Sequence): - policy_network_output_mlp_sizes = { - key: policy_network_output_mlp_sizes for key in specs.keys() - } + q_networks = {} + action_selectors = {} + for key in specs.keys(): - observation_networks = {} - policy_networks = {} + # Get total number of action dimensions from action spec. + num_dimensions = specs[key].actions.num_values - for key in specs.keys(): - # Create the shared observation network - observation_network = tf2_utils.to_sonnet_module(tf.identity) - - # Create the policy network. - policy_network = DIALPolicy( - action_spec=specs[key].actions, - message_spec=_convert_to_spec( - spaces.Box(-np.inf, np.inf, (message_size,), dtype=np.float32) + q_network = CommunicationNetwork( + networks.LayerNormMLP( + (128,), + activate_final=True, + ), + networks.LayerNormMLP( + (128,), + activate_final=True, ), - gru_hidden_size=policy_network_gru_hidden_sizes[key], - gru_layers=policy_network_gru_layers[key], - task_mlp_size=policy_network_task_mlp_sizes[key], - message_in_mlp_size=policy_network_message_in_mlp_sizes[key], - message_out_mlp_size=policy_network_message_out_mlp_sizes[key], - output_mlp_size=policy_network_output_mlp_sizes[key], + snt.GRU(128), + snt.Sequential( + [ + networks.LayerNormMLP((128,), activate_final=True), + networks.NearZeroInitializedLinear(num_dimensions), + networks.TanhToSpec(specs[key].actions), + ] + ), + snt.Sequential( + [ + networks.LayerNormMLP((128, message_size), activate_final=True), + ] + ), + message_size=message_size, ) - observation_networks[key] = observation_network - policy_networks[key] = policy_network + # epsilon greedy action selector + action_selector = action_selector_fn + + q_networks[key] = q_network + action_selectors[key] = action_selector return { - "policies": policy_networks, - "observations": observation_networks, + "q_networks": q_networks, + "action_selectors": action_selectors, } def main(_: Any) -> None: # environment - environment_factory = lp_utils.partial_kwargs( + environment_factory = functools.partial( debugging_utils.make_environment, env_name=FLAGS.env_name, action_space=FLAGS.action_space, ) + # networks network_factory = lp_utils.partial_kwargs(make_networks) # Checkpointer appends "Checkpoints" to checkpoint_dir checkpoint_dir = f"{FLAGS.base_dir}/{FLAGS.mava_id}" - # loggers log_every = 10 logger_factory = functools.partial( logger_utils.make_logger, @@ -168,13 +153,23 @@ def main(_: Any) -> None: time_delta=log_every, ) + # distributed program program = dial.DIAL( environment_factory=environment_factory, network_factory=network_factory, logger_factory=logger_factory, - num_executors=5, - batch_size=1, + num_executors=2, + trainer_fn=RecurrentCommMADQNTrainer, + executor_fn=MADQNRecurrentCommExecutor, + exploration_scheduler_fn=LinearExplorationScheduler, + communication_module=BroadcastedCommunication, + sequence_length=6, + epsilon_min=0.05, + epsilon_decay=5e-4, + optimizer=snt.optimizers.Adam(learning_rate=1e-4), checkpoint_subpath=checkpoint_dir, + n_step=1, + batch_size=32, ).build() # launch diff --git a/mava/systems/tf/dial/builder.py b/mava/systems/tf/dial/builder.py index a54a619f6..6e4af83e4 100644 --- a/mava/systems/tf/dial/builder.py +++ b/mava/systems/tf/dial/builder.py @@ -1,110 +1,114 @@ +# python3 +# Copyright 2021 [...placeholder...]. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import dataclasses -from typing import Any, Dict, Iterator, List, Optional, Type # , Union +from typing import Any, Dict, Iterator, List, Optional, Type +import numpy as np import reverb import sonnet as snt -import tensorflow as tf +from acme import datasets from acme.tf import variable_utils -from acme.utils import counting, loggers +from acme.utils import counting from mava import adders, core, specs, types from mava.adders import reverb as reverb_adders -from mava.systems.builders import SystemBuilder +from mava.components.tf.modules.communication import BaseCommunicationModule +from mava.components.tf.modules.exploration.exploration_scheduling import ( + LinearExplorationScheduler, +) +from mava.components.tf.modules.stabilising import FingerPrintStabalisation from mava.systems.tf import executors -from mava.systems.tf.dial.execution import DIALExecutor -from mava.systems.tf.dial.training import DIALTrainer +from mava.systems.tf.dial import training +from mava.systems.tf.madqn import execution @dataclasses.dataclass class DIALConfig: - """Configuration options for the DIAL system + """Configuration options for the MADDPG system. Args: - environment_spec: description of the actions, observations, etc. - networks: the online Q network (the one being optimized) - batch_size: batch size for updates. - prefetch_size: size to prefetch from replay. - target_update_period: number of learner steps to perform before updating - the target networks. - samples_per_insert: number of samples to take from replay for every insert - that is made. - min_replay_size: minimum replay size before updating. This and all - following arguments are related to dataset construction and will be - ignored if a dataset argument is passed. - max_replay_size: maximum replay size. - importance_sampling_exponent: power to which importance weights are raised - before normalizing. - priority_exponent: exponent used in prioritized sampling. - n_step: number of steps to squash into a single transition. - epsilon: probability of taking a random action; ignored if a policy - network is given. - discount: discount to use for TD updates. - logger: logger object to be used by learner. - checkpoint: boolean indicating whether to checkpoint the learner. - checkpoint_subpath: directory for the checkpoint. - policy_networks: if given, this will be used as the policy network. - Otherwise, an epsilon greedy policy using the online Q network will be - created. Policy network is used in the actor to sample actions. - max_gradient_norm: used for gradient clipping. - replay_table_name: string indicating what name to give the replay table. - """ + environment_spec: description of the actions, observations, etc. + discount: discount to use for TD updates. + batch_size: batch size for updates. + prefetch_size: size to prefetch from replay. + target_update_period: number of learner steps to perform before updating + the target networks. + min_replay_size: minimum replay size before updating. + max_replay_size: maximum replay size. + samples_per_insert: number of samples to take from replay for every insert + that is made. + n_step: number of steps to squash into a single transition. + sigma: standard deviation of zero-mean, Gaussian exploration noise. + clipping: whether to clip gradients by global norm. + replay_table_name: string indicating what name to give the replay table.""" environment_spec: specs.MAEnvironmentSpec - # networks: Dict[str, snt.Module] - policy_optimizer: snt.Optimizer - shared_weights: bool = True - batch_size: int = 1 - prefetch_size: int = 4 - target_update_period: int = 100 - samples_per_insert: float = 32.0 - min_replay_size: int = 1 - max_replay_size: int = 1000000 - importance_sampling_exponent: float = 0.2 - priority_exponent: float = 0.6 - n_step: int = 1 - epsilon: Optional[tf.Tensor] = None - discount: float = 1.00 - logger: loggers.Logger = None - checkpoint: bool = True - checkpoint_subpath: str = "~/mava/" - # policy_networks: Optional[Dict[str, snt.Module]] = None - max_gradient_norm: Optional[float] = None + epsilon_min: float + epsilon_decay: float + shared_weights: bool + target_update_period: int + executor_variable_update_period: int + clipping: bool + min_replay_size: int + max_replay_size: int + samples_per_insert: Optional[float] + prefetch_size: int + batch_size: int + n_step: int + sequence_length: int + period: int + discount: float + checkpoint: bool + optimizer: snt.Optimizer replay_table_name: str = reverb_adders.DEFAULT_PRIORITY_TABLE - counter: counting.Counter = None - huber_loss_parameter: float = 1.0 - clipping: bool = False - # communication_module: BaseCommunicationModule = BroadcastedCommunication - sequence_length: int = 10 - period: int = 1 + checkpoint_subpath: str = "~/mava/" -class DIALBuilder(SystemBuilder): - """Builder for DIAL which constructs individual components of the system.""" +class DIALBuilder: + """Builder for MADQN which constructs individual components of the system.""" - """Defines an interface for defining the components of an MARL system. + """Defines an interface for defining the components of an RL system. Implementations of this interface contain a complete specification of a - concrete MARL system. An instance of this class can be used to build an - MARL system which interacts with the environment either locally or in a + concrete RL system. An instance of this class can be used to build an + RL system which interacts with the environment either locally or in a distributed setup. """ def __init__( self, config: DIALConfig, - executor_fn: Type[core.Executor] = DIALExecutor, + trainer_fn: Type[training.DIALTrainer] = training.DIALTrainer, + executor_fn: Type[core.Executor] = execution.MADQNRecurrentCommExecutor, extra_specs: Dict[str, Any] = {}, + exploration_scheduler_fn: Type[ + LinearExplorationScheduler + ] = LinearExplorationScheduler, + replay_stabilisation_fn: Optional[Type[FingerPrintStabalisation]] = None, ): """Args: - config: Configuration options for the DIAL system. - executor_fn: Executor function to use""" - + _config: Configuration options for the MADQN system. + _trainer_fn: Trainer module to use.""" self._config = config self._extra_specs = extra_specs - """ _agents: a list of the agent specs (ids). - _agent_types: a list of the types of agents to be used.""" self._agents = self._config.environment_spec.get_agent_ids() self._agent_types = self._config.environment_spec.get_agent_types() + self._trainer_fn = trainer_fn self._executor_fn = executor_fn + self._exploration_scheduler_fn = exploration_scheduler_fn + self._replay_stabiliser_fn = replay_stabilisation_fn def make_replay_tables( self, @@ -114,64 +118,97 @@ def make_replay_tables( # Select adder if issubclass(self._executor_fn, executors.FeedForwardExecutor): + # Check if we should use fingerprints + if self._replay_stabiliser_fn is not None: + self._extra_specs.update({"fingerprint": np.array([1.0, 1.0])}) adder_sig = reverb_adders.ParallelNStepTransitionAdder.signature( - environment_spec + environment_spec, self._extra_specs ) elif issubclass(self._executor_fn, executors.RecurrentExecutor): - - adder_sig = reverb_adders.ParallelEpisodeAdder.signature( + adder_sig = reverb_adders.ParallelSequenceAdder.signature( environment_spec, self._extra_specs ) + # adder_sig = reverb_adders.ParallelEpisodeAdder.signature( + # environment_spec, self._extra_specs + # ) else: raise NotImplementedError("Unknown executor type: ", self._executor_fn) - return [ - reverb.Table.queue( - name=self._config.replay_table_name, - max_size=self._config.max_replay_size, - signature=adder_sig, + if self._config.samples_per_insert is None: + # We will take a samples_per_insert ratio of None to mean that there is + # no limit, i.e. this only implies a min size limit. + limiter = reverb.rate_limiters.MinSize(self._config.min_replay_size) + + else: + # Create enough of an error buffer to give a 10% tolerance in rate. + samples_per_insert_tolerance = 0.1 * self._config.samples_per_insert + error_buffer = self._config.min_replay_size * samples_per_insert_tolerance + limiter = reverb.rate_limiters.SampleToInsertRatio( + min_size_to_sample=self._config.min_replay_size, + samples_per_insert=self._config.samples_per_insert, + error_buffer=error_buffer, ) - ] + + replay_table = reverb.Table( + name=self._config.replay_table_name, + sampler=reverb.selectors.Uniform(), + remover=reverb.selectors.Fifo(), + max_size=self._config.max_replay_size, + rate_limiter=limiter, + signature=adder_sig, + ) + + return [replay_table] def make_dataset_iterator( - self, - replay_client: reverb.Client, + self, replay_client: reverb.Client ) -> Iterator[reverb.ReplaySample]: - """Create a dataset iterator to use for learning/updating the system.""" - dataset = reverb.ReplayDataset.from_table_signature( - server_address=replay_client.server_address, + """Create a dataset iterator to use for learning/updating the system. + Args: + replay_client: Reverb Client which points to the replay server.""" + + sequence_length = ( + self._config.sequence_length + if issubclass(self._executor_fn, executors.RecurrentExecutor) + else None + ) + + dataset = datasets.make_reverb_dataset( table=self._config.replay_table_name, - max_in_flight_samples_per_worker=1, - # sequence_length=self._config.sequence_length, - emit_timesteps=False, + server_address=replay_client.server_address, + batch_size=self._config.batch_size, + prefetch_size=self._config.prefetch_size, + sequence_length=sequence_length, ) - dataset = dataset.batch(self._config.batch_size, drop_remainder=True) return iter(dataset) def make_adder( - self, - replay_client: reverb.Client, + self, replay_client: reverb.Client ) -> Optional[adders.ParallelAdder]: """Create an adder which records data generated by the executor/environment. Args: - replay_client: Reverb Client which points to the replay server. - """ + replay_client: Reverb Client which points to the replay server.""" # Select adder if issubclass(self._executor_fn, executors.FeedForwardExecutor): adder = reverb_adders.ParallelNStepTransitionAdder( - priority_fns=None, # {self._config.replay_table_name: lambda x: 1.0}, + priority_fns=None, client=replay_client, n_step=self._config.n_step, discount=self._config.discount, ) elif issubclass(self._executor_fn, executors.RecurrentExecutor): - adder = reverb_adders.ParallelEpisodeAdder( - priority_fns=None, # {self._config.replay_table_name: lambda x: 1.0}, + adder = reverb_adders.ParallelSequenceAdder( + priority_fns=None, client=replay_client, - max_sequence_length=self._config.sequence_length, - # period=self._config.period, + sequence_length=self._config.sequence_length, + period=self._config.period, ) + # adder = reverb_adders.ParallelEpisodeAdder( + # priority_fns=None, + # client=replay_client, + # max_sequence_length=self._config.sequence_length, + # ) else: raise NotImplementedError("Unknown executor type: ", self._executor_fn) @@ -179,98 +216,112 @@ def make_adder( def make_executor( self, - policy_networks: Dict[str, snt.Module], + q_networks: Dict[str, snt.Module], + action_selectors: Dict[str, Any], adder: Optional[adders.ParallelAdder] = None, variable_source: Optional[core.VariableSource] = None, + trainer: Optional[training.DIALTrainer] = None, + communication_module: Optional[BaseCommunicationModule] = None, + evaluator: bool = False, ) -> core.Executor: """Create an executor instance. Args: - policy_networks: A struct of instance of all the different policy networks; - this should be a callable - which takes as input observations and returns actions. - adder: How data is recorded (e.g. added to replay). - variable_source: A source providing the necessary executor parameters. + q_networks: A struct of instance of all + the different system q networks, + this should be a callable which takes as input observations + and returns actions. + adder: How data is recorded (e.g. added to replay). + variable_source: collection of (nested) numpy arrays. Contains + source variables as defined in mava.core. """ - shared_weights = self._config.shared_weights - policy_net: Dict[str, snt.Module] = policy_networks["policy_net"] - communication_module: str = policy_networks["communication_module"] + shared_weights = self._config.shared_weights variable_client = None if variable_source: agent_keys = self._agent_types if shared_weights else self._agents # Create policy variables - variables = {} - for agent in agent_keys: - variables[agent] = policy_net[agent].variables - + variables = {agent: q_networks[agent].variables for agent in agent_keys} # Get new policy variables variable_client = variable_utils.VariableClient( client=variable_source, - variables={"policy": variables}, - update_period=10, + variables={"q_network": variables}, + update_period=self._config.executor_variable_update_period, ) # Make sure not to use a random policy after checkpoint restoration by # assigning variables before running the environment loop. variable_client.update_and_wait() - # Create the actor which defines how we take actions. + # Check if we should use fingerprints + fingerprint = True if self._replay_stabiliser_fn is not None else False + + # Create the executor which coordinates the actors. return self._executor_fn( - policy_networks=policy_net, - communication_module=communication_module, + q_networks=q_networks, + action_selectors=action_selectors, shared_weights=shared_weights, variable_client=variable_client, - agent_specs=self._config.environment_spec.get_agent_specs(), adder=adder, + trainer=trainer, + communication_module=communication_module, + evaluator=evaluator, + fingerprint=fingerprint, ) def make_trainer( self, networks: Dict[str, Dict[str, snt.Module]], dataset: Iterator[reverb.ReplaySample], - replay_client: Optional[reverb.Client] = None, counter: Optional[counting.Counter] = None, logger: Optional[types.NestedLogger] = None, + communication_module: Optional[BaseCommunicationModule] = None, ) -> core.Trainer: """Creates an instance of the trainer. Args: networks: struct describing the networks needed by the trainer; this can be specific to the trainer in question. dataset: iterator over samples from replay. - replay_client: client which allows communication with replay, e.g. in - order to update priorities. counter: a Counter which allows for recording of counts (trainer steps, executor steps, etc.) distributed throughout the system. logger: Logger object for logging metadata. + checkpoint: bool controlling whether the trainer checkpoints itself. """ - agents = self._agents - agent_types = self._agent_types - shared_weights = self._config.shared_weights - clipping = self._config.clipping - discount = self._config.discount - target_update_period = self._config.target_update_period - max_gradient_norm = self._config.max_gradient_norm - importance_sampling_exponent = self._config.importance_sampling_exponent + q_networks = networks["values"] + target_q_networks = networks["target_values"] + + agents = self._config.environment_spec.get_agent_ids() + agent_types = self._config.environment_spec.get_agent_types() + + # Make epsilon scheduler + exploration_scheduler = self._exploration_scheduler_fn( + epsilon_min=self._config.epsilon_min, + epsilon_decay=self._config.epsilon_decay, + ) + + # Check if we should use fingerprints + fingerprint = True if self._replay_stabiliser_fn is not None else False # The learner updates the parameters (and initializes them). - trainer = DIALTrainer( + trainer = self._trainer_fn( agents=agents, agent_types=agent_types, - networks=networks, - shared_weights=shared_weights, - discount=discount, - importance_sampling_exponent=importance_sampling_exponent, - policy_optimizer=self._config.policy_optimizer, - target_update_period=target_update_period, + discount=self._config.discount, + q_networks=q_networks, + target_q_networks=target_q_networks, + shared_weights=self._config.shared_weights, + optimizer=self._config.optimizer, + target_update_period=self._config.target_update_period, + clipping=self._config.clipping, + exploration_scheduler=exploration_scheduler, + communication_module=communication_module, dataset=dataset, - huber_loss_parameter=self._config.huber_loss_parameter, - replay_client=replay_client, - clipping=clipping, counter=counter, + fingerprint=fingerprint, logger=logger, checkpoint=self._config.checkpoint, - max_gradient_norm=max_gradient_norm, + checkpoint_subpath=self._config.checkpoint_subpath, ) + return trainer diff --git a/mava/systems/tf/dial/execution.py b/mava/systems/tf/dial/execution.py index afd8750f0..6fc3971d7 100644 --- a/mava/systems/tf/dial/execution.py +++ b/mava/systems/tf/dial/execution.py @@ -22,90 +22,58 @@ # - multi-agent generic executors in mava: mava/systems/tf/executors.py """DIAL executor implementation.""" -from typing import Dict, Optional +from typing import Any, Dict, Optional import sonnet as snt -import tensorflow as tf -import tensorflow_probability as tfp -from acme import types -from acme.specs import EnvironmentSpec from acme.tf import variable_utils as tf2_variable_utils from mava import adders from mava.components.tf.modules.communication import BaseCommunicationModule -from mava.systems.tf.executors import RecurrentExecutorWithComms +from mava.systems.tf.madqn.execution import MADQNRecurrentCommExecutor +from mava.systems.tf.madqn.training import MADQNTrainer -tfd = tfp.distributions - -class DIALExecutor(RecurrentExecutorWithComms): - """DIAL implementation of a recurrent Executor.""" +class DIALExecutor(MADQNRecurrentCommExecutor): + """DIAL executor. + An executor based on a recurrent communicating policy for each agent in the system + which takes non-batched observations and outputs non-batched actions. + It also allows adding experiences to replay and updating the weights + from the policy on the learner. + """ def __init__( self, - policy_networks: Dict[str, snt.RNNCore], + q_networks: Dict[str, snt.Module], + action_selectors: Dict[str, snt.Module], communication_module: BaseCommunicationModule, - agent_specs: Dict[str, EnvironmentSpec], shared_weights: bool = True, adder: Optional[adders.ParallelAdder] = None, variable_client: Optional[tf2_variable_utils.VariableClient] = None, store_recurrent_state: bool = True, - is_eval: bool = False, - epsilon: float = 0.05, + trainer: MADQNTrainer = None, + fingerprint: bool = False, + evaluator: bool = False, ): """Initializes the executor. - TODO: Update docstring Args: - policy_networks: the (recurrent) policy to run for each agent in the system. + policy_network: the policy to run for each agent in the system. shared_weights: specify if weights are shared between agent networks. adder: the adder object to which allows to add experiences to a dataset/replay buffer. variable_client: object which allows to copy weights from the trainer copy of the policies to the executor copy (in case they are separate). - store_recurrent_state: Whether to pass the recurrent state to the adder. """ - # Store these for later use. - self._agent_specs = agent_specs - self._is_eval = is_eval - self._epsilon = epsilon - - super().__init__( - adder=adder, - variable_client=variable_client, - policy_networks=policy_networks, - store_recurrent_state=store_recurrent_state, - communication_module=communication_module, - shared_weights=shared_weights, - ) - def _sample_action( - self, action_policy: types.NestedTensor, agent: str - ) -> types.NestedTensor: - action = tf.argmax(action_policy, axis=1) - if tf.random.uniform([]) < self._epsilon and not self._is_eval: - action_spec = self._agent_specs[agent].actions - action = tf.random.uniform( - action_spec.shape, 0, action_spec.num_values, dtype=tf.dtypes.int64 - ) - - # Hard coded perfect policy: - # if observation[1].item()==5 and observation[0].item()==1: - # action = tf.constant([1], dtype=tf.dtypes.int64) - # else: - # tf.constant([0], dtype=tf.dtypes.int64) - - return action + # Store these for later use. + self._adder = adder + self._variable_client = variable_client + self._q_networks = q_networks + self._policy_networks = q_networks + self._communication_module = communication_module + self._action_selectors = action_selectors + self._store_recurrent_state = store_recurrent_state + self._trainer = trainer + self._shared_weights = shared_weights - def _process_message( - self, observation: types.NestedTensor, message_policy: types.NestedTensor - ) -> types.NestedTensor: - # Only one agent can message at each timestep - if observation[0] == 0: - message = tf.zeros_like(message_policy) - else: - message = ( - message_policy.sample() - if isinstance(message_policy, tfd.Distribution) - else message_policy - ) - return message + self._states: Dict[str, Any] = {} + self._messages: Dict[str, Any] = {} diff --git a/mava/systems/tf/dial/system.py b/mava/systems/tf/dial/system.py index 7990b2647..f6d5ef611 100644 --- a/mava/systems/tf/dial/system.py +++ b/mava/systems/tf/dial/system.py @@ -1,3 +1,4 @@ +# python3 # Copyright 2021 [...placeholder...]. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -12,7 +13,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -"""DIAL system implementation.""" +"""Defines the MADQN system class.""" import functools from typing import Any, Callable, Dict, Optional, Type @@ -21,33 +22,59 @@ import launchpad as lp import reverb import sonnet as snt -import tensorflow as tf from acme import specs as acme_specs from acme.tf import utils as tf2_utils -from acme.utils import counting, loggers +from acme.utils import counting import mava from mava import core from mava import specs as mava_specs -from mava.adders import reverb as reverb_adders -from mava.components.tf.architectures import DecentralisedPolicyActor -from mava.components.tf.modules.communication import BroadcastedCommunication +from mava.components.tf.architectures import DecentralisedValueActor +from mava.components.tf.modules.communication import BaseCommunicationModule +from mava.components.tf.modules.exploration import LinearExplorationScheduler +from mava.components.tf.modules.stabilising import FingerPrintStabalisation from mava.environment_loop import ParallelEnvironmentLoop -from mava.systems import system +from mava.systems.tf import executors from mava.systems.tf import savers as tf2_savers -from mava.systems.tf.dial import builder -from mava.systems.tf.dial.execution import DIALExecutor +from mava.systems.tf.dial import builder, training +from mava.systems.tf.madqn import execution from mava.utils import lp_utils from mava.utils.loggers import MavaLogger, logger_utils from mava.wrappers import DetailedPerAgentStatistics -class DIAL(system.System): +class DIAL: """DIAL system. - This implements a single-process DIAL system. This is an actor-critic based - system that generates data via a behavior policy, inserts N-step transitions into - a replay buffer, and periodically updates the policies of each agent - (and as a result the behavior) by sampling uniformly from this buffer. + This implements a single-process QMIX system. + Args: + environment_factory: Callable to instantiate an environment on a compute node. + network_factory: Callable to instantiate system networks on a compute node. + logger_factory: Callable to instantiate a system logger on a compute node. + architecture: system architecture, e.g. decentralised or centralised. + trainer_fn: training type associated with executor and architecture, + e.g. centralised training. + executor_fn: executor type for example feedforward or recurrent. + num_executors: number of executor processes to run in parallel. + num_caches: number of trainer node caches. + environment_spec: description of the actions, observations, etc. + q_networks: the online Q network (the one being optimized) + epsilon: probability of taking a random action; ignored if a policy + network is given. + trainer_fn: the class used for training the agent and mixing networks. + shared_weights: boolean determining whether shared weights is used. + target_update_period: number of learner steps to perform before updating + the target networks. + clipping: whether to clip gradients by global norm. + replay_table_name: string indicating what name to give the replay table. + max_replay_size: maximum replay size. + samples_per_insert: number of samples to take from replay for every insert + that is made. + prefetch_size: size to prefetch from replay. + batch_size: batch size for updates. + n_step: number of steps to squash into a single transition. + discount: discount to use for TD updates. + counter: counter object used to keep track of steps. + checkpoint: boolean indicating whether to checkpoint the learner. """ def __init__( @@ -55,76 +82,46 @@ def __init__( environment_factory: Callable[[bool], dm_env.Environment], network_factory: Callable[[acme_specs.BoundedArray], Dict[str, snt.Module]], logger_factory: Callable[[str], MavaLogger] = None, - architecture: Type[DecentralisedPolicyActor] = DecentralisedPolicyActor, - executor_fn: Type[core.Executor] = DIALExecutor, + architecture: Type[DecentralisedValueActor] = DecentralisedValueActor, + trainer_fn: Type[training.DIALTrainer] = training.DIALTrainer, + communication_module: Type[BaseCommunicationModule] = None, + executor_fn: Type[core.Executor] = execution.MADQNFeedForwardExecutor, + exploration_scheduler_fn: Type[ + LinearExplorationScheduler + ] = LinearExplorationScheduler, + replay_stabilisation_fn: Optional[Type[FingerPrintStabalisation]] = None, + epsilon_min: float = 0.05, + epsilon_decay: float = 1e-4, num_executors: int = 1, num_caches: int = 0, environment_spec: mava_specs.MAEnvironmentSpec = None, shared_weights: bool = True, - discount: float = 0.99, batch_size: int = 256, prefetch_size: int = 4, - target_update_period: int = 100, - min_replay_size: int = 100, + min_replay_size: int = 1000, max_replay_size: int = 1000000, - samples_per_insert: float = 32.0, - # policy_optimizer: snt.Optimizer = snt.optimizers.RMSProp(5e-4, momentum=0.95), - policy_optimizer: snt.Optimizer = snt.optimizers.Adam(learning_rate=1e-4), + samples_per_insert: Optional[float] = 32.0, n_step: int = 5, sequence_length: int = 20, period: int = 20, - importance_sampling_exponent: float = 0.2, - priority_exponent: float = 0.6, - epsilon: Optional[tf.Tensor] = None, - counter: counting.Counter = None, + clipping: bool = True, + discount: float = 0.99, + optimizer: snt.Optimizer = snt.optimizers.Adam(learning_rate=1e-4), + target_update_period: int = 100, + executor_variable_update_period: int = 1000, max_executor_steps: int = None, - checkpoint: bool = False, + checkpoint: bool = True, checkpoint_subpath: str = "~/mava/", logger_config: Dict = {}, - max_gradient_norm: Optional[float] = None, - replay_table_name: str = reverb_adders.DEFAULT_PRIORITY_TABLE, - communication_module: Type[BroadcastedCommunication] = BroadcastedCommunication, + train_loop_fn: Callable = ParallelEnvironmentLoop, + eval_loop_fn: Callable = ParallelEnvironmentLoop, + train_loop_fn_kwargs: Dict = {}, + eval_loop_fn_kwargs: Dict = {}, ): - # TODO: Update args - """Initialize the system. - Args: - environment_factory: Callable to instantiate an environment - on a compute node. - network_factory: Callable to instantiate system networks on a compute node. - logger_factory: Callable to instantiate a system logger on a compute node. - architecture: system architecture, e.g. decentralised or centralised. - trainer_fn: training type associated with executor and architecture, - e.g. centralised training. - executor_fn: executor type for example feedforward or recurrent. - num_executors: number of executor processes to run in parallel. - num_caches: number of trainer node caches. - environment_spec: description of the actions, observations, etc. - shared_weights: set whether agents should share network weights. - batch_size: batch size for updates. - prefetch_size: size to prefetch from replay. - target_update_period: number of learner steps to perform before updating - the target networks. - samples_per_insert: number of samples to take from replay for every insert - that is made. - min_replay_size: minimum replay size before updating. This and all - following arguments are related to dataset construction and will be - ignored if a dataset argument is passed. - max_replay_size: maximum replay size. - importance_sampling_exponent: power to which importance weights are raised - before normalizing. - priority_exponent: exponent used in prioritized sampling. - n_step: number of steps to squash into a single transition. - epsilon: probability of taking a random action; ignored if a policy - network is given. - discount: discount to use for TD updates. - checkpoint: boolean indicating whether to checkpoint the learner. - checkpoint_subpath: directory for the checkpoint. - max_gradient_norm: used for gradient clipping. - replay_table_name: string indicating what name to give the replay table.""" if not environment_spec: environment_spec = mava_specs.MAEnvironmentSpec( - environment_factory(evaluation=False) # type: ignore + environment_factory(evaluation=False) # type:ignore ) # set default logger if no logger provided @@ -136,7 +133,7 @@ def __init__( time_delta=10, ) - self._architecture_fn = architecture + self._architecture = architecture self._communication_module_fn = communication_module self._environment_factory = environment_factory self._network_factory = network_factory @@ -149,60 +146,71 @@ def __init__( self._checkpoint_subpath = checkpoint_subpath self._checkpoint = checkpoint self._logger_config = logger_config + self._train_loop_fn = train_loop_fn + self._train_loop_fn_kwargs = train_loop_fn_kwargs + self._eval_loop_fn = eval_loop_fn + self._eval_loop_fn_kwargs = eval_loop_fn_kwargs - extra_specs = self._get_extra_specs() + if issubclass(executor_fn, executors.RecurrentExecutor): + extra_specs = self._get_extra_specs() + else: + extra_specs = {} self._builder = builder.DIALBuilder( builder.DIALConfig( environment_spec=environment_spec, + epsilon_min=epsilon_min, + epsilon_decay=epsilon_decay, shared_weights=shared_weights, + discount=discount, batch_size=batch_size, prefetch_size=prefetch_size, target_update_period=target_update_period, - samples_per_insert=samples_per_insert, + executor_variable_update_period=executor_variable_update_period, min_replay_size=min_replay_size, max_replay_size=max_replay_size, - importance_sampling_exponent=importance_sampling_exponent, - priority_exponent=priority_exponent, + samples_per_insert=samples_per_insert, n_step=n_step, - epsilon=epsilon, - discount=discount, - counter=counter, - checkpoint=checkpoint, - checkpoint_subpath=checkpoint_subpath, - max_gradient_norm=max_gradient_norm, - replay_table_name=replay_table_name, - policy_optimizer=policy_optimizer, sequence_length=sequence_length, period=period, + clipping=clipping, + checkpoint=checkpoint, + optimizer=optimizer, + checkpoint_subpath=checkpoint_subpath, ), + trainer_fn=trainer_fn, executor_fn=executor_fn, extra_specs=extra_specs, + exploration_scheduler_fn=exploration_scheduler_fn, + replay_stabilisation_fn=replay_stabilisation_fn, ) - # TODO (Kevin): create decentralised/centralised/networked actor architectures - # see mava/components/tf/architectures - - # TODO (Kevin): create differentiable communication module - # See mava/components/tf/modules/communication - def _get_extra_specs(self) -> Any: agents = self._environment_spec.get_agent_ids() + core_state_specs = {} + core_message_specs = {} + networks = self._network_factory( # type: ignore environment_spec=self._environment_spec ) - core_state_spec = {} for agent in agents: agent_type = agent.split("_")[0] - core_state_spec[agent] = { - "state": tf2_utils.squeeze_batch_dim( - networks["policies"][agent_type].initial_state(1) - ), - "message": tf2_utils.squeeze_batch_dim( - networks["policies"][agent_type].initial_message(1) + core_state_specs[agent] = ( + tf2_utils.squeeze_batch_dim( + networks["q_networks"][agent_type].initial_state(1) ), - } - extras = {"core_states": core_state_spec} + ) + if self._communication_module_fn is not None: + core_message_specs[agent] = ( + tf2_utils.squeeze_batch_dim( + networks["q_networks"][agent_type].initial_message(1) + ), + ) + + extras = { + "core_states": core_state_specs, + "core_messages": core_message_specs, + } return extras def replay(self) -> Any: @@ -217,6 +225,9 @@ def counter(self) -> Any: subdirectory="counter", ) + def coordinator(self, counter: counting.Counter) -> Any: + return lp_utils.StepsLimiter(counter, self._max_executor_steps) # type: ignore + def trainer( self, replay: reverb.Client, @@ -230,27 +241,33 @@ def trainer( ) # Create system architecture with target networks. - architecture = self._architecture_fn( + architecture = self._architecture( environment_spec=self._environment_spec, - observation_networks=networks["observations"], - policy_networks=networks["policies"], + value_networks=networks["q_networks"], shared_weights=self._shared_weights, ) - communication_module = self._communication_module_fn( - architecture=architecture, - shared=True, - channel_size=1, - channel_noise=0, - ) + if self._builder._replay_stabiliser_fn is not None: + architecture = self._builder._replay_stabiliser_fn( # type: ignore + architecture + ) - system_networks = communication_module.create_system() + communication_module = None + if self._communication_module_fn is not None: + communication_module = self._communication_module_fn( + architecture=architecture, + shared=True, + channel_size=1, + channel_noise=0, + ) + system_networks = communication_module.create_system() + else: + system_networks = architecture.create_system() # create logger trainer_logger_config = {} - if self._logger_config: - if "trainer" in self._logger_config: - trainer_logger_config = self._logger_config["trainer"] + if self._logger_config and "trainer" in self._logger_config: + trainer_logger_config = self._logger_config["trainer"] trainer_logger = self._logger_factory( # type: ignore "trainer", **trainer_logger_config ) @@ -258,12 +275,11 @@ def trainer( dataset = self._builder.make_dataset_iterator(replay) counter = counting.Counter(counter, "trainer") - system_networks["communication_module"] = {"all_agents": communication_module} - return self._builder.make_trainer( networks=system_networks, dataset=dataset, counter=counter, + communication_module=communication_module, logger=trainer_logger, ) @@ -273,6 +289,7 @@ def executor( replay: reverb.Client, variable_source: acme.VariableSource, counter: counting.Counter, + trainer: Optional[training.DIALTrainer] = None, ) -> mava.ParallelEnvironmentLoop: """The executor process.""" @@ -282,33 +299,37 @@ def executor( ) # Create system architecture with target networks. - architecture = self._architecture_fn( + architecture = self._architecture( environment_spec=self._environment_spec, - observation_networks=networks["observations"], - policy_networks=networks["policies"], + value_networks=networks["q_networks"], shared_weights=self._shared_weights, ) - communication_module = self._communication_module_fn( - architecture=architecture, - shared=True, - channel_size=1, - channel_noise=0, - ) - - _ = communication_module.create_system() + if self._builder._replay_stabiliser_fn is not None: + architecture = self._builder._replay_stabiliser_fn( # type: ignore + architecture + ) - # behaviour policy networks (obs net + policy head) - behaviour_policy_networks = communication_module.create_behaviour_policy() + communication_module = None + if self._communication_module_fn is not None: + communication_module = self._communication_module_fn( + architecture=architecture, + shared=True, + channel_size=1, + channel_noise=0, + ) + system_networks = communication_module.create_system() + else: + system_networks = architecture.create_system() # Create the executor. executor = self._builder.make_executor( - policy_networks={ - "policy_net": behaviour_policy_networks, - "communication_module": communication_module, - }, + q_networks=system_networks["values"], + action_selectors=networks["action_selectors"], + communication_module=communication_module, adder=self._builder.make_adder(replay), variable_source=variable_source, + trainer=trainer, ) # TODO (Arnu): figure out why factory function are giving type errors @@ -320,16 +341,19 @@ def executor( # Create executor logger executor_logger_config = {} - if self._logger_config: - if "executor" in self._logger_config: - executor_logger_config = self._logger_config["executor"] + if self._logger_config and "executor" in self._logger_config: + executor_logger_config = self._logger_config["executor"] exec_logger = self._logger_factory( # type: ignore f"executor_{executor_id}", **executor_logger_config ) # Create the loop to connect environment and executor. - train_loop = ParallelEnvironmentLoop( - environment, executor, counter=counter, logger=exec_logger + train_loop = self._train_loop_fn( + environment, + executor, + counter=counter, + logger=exec_logger, + **self._train_loop_fn_kwargs, ) train_loop = DetailedPerAgentStatistics(train_loop) @@ -340,7 +364,7 @@ def evaluator( self, variable_source: acme.VariableSource, counter: counting.Counter, - logger: loggers.Logger = None, + trainer: training.DIALTrainer, ) -> Any: """The evaluation process.""" @@ -350,32 +374,37 @@ def evaluator( ) # Create system architecture with target networks. - architecture = self._architecture_fn( + architecture = self._architecture( environment_spec=self._environment_spec, - observation_networks=networks["observations"], - policy_networks=networks["policies"], + value_networks=networks["q_networks"], shared_weights=self._shared_weights, ) - communication_module = self._communication_module_fn( - architecture=architecture, - shared=True, - channel_size=1, - channel_noise=0, - ) - - _ = communication_module.create_system() + if self._builder._replay_stabiliser_fn is not None: + architecture = self._builder._replay_stabiliser_fn( # type: ignore + architecture + ) - # behaviour policy networks (obs net + policy head) - behaviour_policy_networks = communication_module.create_behaviour_policy() + communication_module = None + if self._communication_module_fn is not None: + communication_module = self._communication_module_fn( + architecture=architecture, + shared=True, + channel_size=1, + channel_noise=0, + ) + system_networks = communication_module.create_system() + else: + system_networks = architecture.create_system() - # Create the executor. + # Create the agent. executor = self._builder.make_executor( - policy_networks={ - "policy_net": behaviour_policy_networks, - "communication_module": communication_module, - }, + q_networks=system_networks["values"], + action_selectors=networks["action_selectors"], variable_source=variable_source, + communication_module=communication_module, + trainer=trainer, + evaluator=True, ) # Make the environment. @@ -384,26 +413,26 @@ def evaluator( # Create logger and counter. counter = counting.Counter(counter, "evaluator") evaluator_logger_config = {} - if self._logger_config: - if "evaluator" in self._logger_config: - evaluator_logger_config = self._logger_config["evaluator"] + if self._logger_config and "evaluator" in self._logger_config: + evaluator_logger_config = self._logger_config["evaluator"] eval_logger = self._logger_factory( # type: ignore "evaluator", **evaluator_logger_config ) # Create the run loop and return it. # Create the loop to connect environment and executor. - eval_loop = ParallelEnvironmentLoop( - environment, executor, counter=counter, logger=eval_logger + eval_loop = self._eval_loop_fn( + environment, + executor, + counter=counter, + logger=eval_logger, + **self._eval_loop_fn_kwargs, ) eval_loop = DetailedPerAgentStatistics(eval_loop) return eval_loop - def coordinator(self, counter: counting.Counter) -> Any: - return lp_utils.StepsLimiter(counter, self._max_executor_steps) # type: ignore - - def build(self, name: str = "maddpg") -> Any: + def build(self, name: str = "madqn") -> Any: """Build the distributed system topology.""" program = lp.Program(name=name) @@ -421,7 +450,7 @@ def build(self, name: str = "maddpg") -> Any: trainer = program.add_node(lp.CourierNode(self.trainer, replay, counter)) with program.group("evaluator"): - program.add_node(lp.CourierNode(self.evaluator, trainer, counter)) + program.add_node(lp.CourierNode(self.evaluator, trainer, counter, trainer)) if not self._num_caches: # Use the trainer as a single variable source. @@ -443,7 +472,14 @@ def build(self, name: str = "maddpg") -> Any: for executor_id in range(self._num_exectors): source = sources[executor_id % len(sources)] program.add_node( - lp.CourierNode(self.executor, executor_id, replay, source, counter) + lp.CourierNode( + self.executor, + executor_id, + replay, + source, + counter, + trainer, + ) ) return program diff --git a/mava/systems/tf/dial/training.py b/mava/systems/tf/dial/training.py index 7c1558b33..2803a04d7 100644 --- a/mava/systems/tf/dial/training.py +++ b/mava/systems/tf/dial/training.py @@ -13,36 +13,25 @@ # See the License for the specific language governing permissions and # limitations under the License. -# TODO (Kevin): implement DIAL trainer -# Helper resources -# - single agent dqn learner in acme: -# https://github.com/deepmind/acme/blob/master/acme/agents/tf/dqn/learning.py -# - multi-agent ddpg trainer in mava: mava/systems/tf/maddpg/trainer.py -# - https://github.com/deepmind/acme/agents/tf/r2d2/learning.py """DIAL trainer implementation.""" -import os -import time -from typing import Any, Dict, List, Optional, Sequence -import numpy as np -import reverb +from typing import Dict, List + import sonnet as snt import tensorflow as tf -import tree -from acme.tf import utils as tf2_utils from acme.utils import counting, loggers -import mava - -# from mava.components.tf.modules.communication import BaseCommunicationModule -from mava.systems.tf import savers as tf2_savers -from mava.utils import training_utils as train_utils +from mava.components.tf.modules.communication import BaseCommunicationModule +from mava.components.tf.modules.exploration.exploration_scheduling import ( + LinearExplorationScheduler, +) +from mava.systems.tf.madqn.training import RecurrentCommMADQNTrainer -class DIALTrainer(mava.Trainer): - """DIAL trainer. - This is the trainer component of a DIAL system. IE it takes a dataset as input +class DIALTrainer(RecurrentCommMADQNTrainer): + """Recurrent Comm DIAL trainer. + This is the trainer component of a MADQN system. IE it takes a dataset as input and implements update functionality to learn from this dataset. """ @@ -50,377 +39,38 @@ def __init__( self, agents: List[str], agent_types: List[str], - networks: Dict[str, snt.Module], - discount: float, - huber_loss_parameter: float, + q_networks: Dict[str, snt.Module], + target_q_networks: Dict[str, snt.Module], target_update_period: int, dataset: tf.data.Dataset, - policy_optimizer: snt.Optimizer, - shared_weights: bool = True, - importance_sampling_exponent: float = None, - replay_client: Optional[reverb.Client] = None, + optimizer: snt.Optimizer, + discount: float, + shared_weights: bool, + exploration_scheduler: LinearExplorationScheduler, + communication_module: BaseCommunicationModule, clipping: bool = True, + fingerprint: bool = False, counter: counting.Counter = None, logger: loggers.Logger = None, checkpoint: bool = True, checkpoint_subpath: str = "~/mava/", - max_gradient_norm: Optional[float] = None, ): - """Initializes the learner. - Args: - policy_network: the online (optimized) policy. - target_policy_network: the target policy (which lags behind the online - policy). - discount: discount to use for TD updates. - target_update_period: number of learner steps to perform before updating - the target networks. - dataset: dataset to learn from, whether fixed or from a replay buffer - (see `acme.datasets.reverb.make_dataset` documentation). - observation_network: an optional online network to process observations - before the policy - target_observation_network: the target observation network. - policy_optimizer: the optimizer to be applied to the (policy) loss. - clipping: whether to clip gradients by global norm. - counter: counter object used to keep track of steps. - logger: logger object to be used by learner. - checkpoint: boolean indicating whether to checkpoint the learner. - """ - self._agents = agents - self._agent_types = agent_types - self._shared_weights = shared_weights - self._communication_module = networks["communication_module"]["all_agents"] - - # Store online and target networks. - self._policy_networks = networks["policies"] - self._target_policy_networks = networks["target_policies"] - - # self._observation_networks = observation_networks - self._observation_networks = { - k: tf2_utils.to_sonnet_module(v) - for k, v in networks["observations"].items() - } - - # General learner book-keeping and loggers. - self._counter = counter or counting.Counter() - self._logger = logger or loggers.make_default_logger("trainer") - - # Other learner parameters. - self._discount = discount - self._clipping = clipping - - # Necessary to track when to update target networks. - self._num_steps = tf.Variable(0, dtype=tf.int32) - self._target_update_period = target_update_period - - # Create an iterator to go through the dataset. - # TODO(b/155086959): Fix type stubs and remove. - self._iterator = iter(dataset) # pytype: disable=wrong-arg-types - - # Create optimizers if they aren't given. - self._policy_optimizer = policy_optimizer or snt.optimizers.Adam(1e-4) - - # Dictionary with network keys for each agent. - self.agent_net_keys = {agent: agent for agent in self._agents} - if self._shared_weights: - self.agent_net_keys = {agent: agent.split("_")[0] for agent in self._agents} - - self.unique_net_keys = self._agent_types if shared_weights else self._agents - - policy_networks_to_expose = {} - self._system_network_variables: Dict[str, Dict[str, snt.Module]] = { - "policy": {}, - } - for agent_key in self.unique_net_keys: - policy_network_to_expose = snt.Sequential( - [ - self._observation_networks[agent_key], - self._policy_networks[agent_key], - ] - ) - policy_networks_to_expose[agent_key] = policy_network_to_expose - self._system_network_variables["policy"][ - agent_key - ] = policy_network_to_expose.variables - - self._system_checkpointer = {} - if checkpoint: - for agent_key in self.unique_net_keys: - objects_to_save = { - "counter": self._counter, - "policy": self._policy_networks[agent_key], - "observation": self._observation_networks[agent_key], - "target_policy": self._target_policy_networks[agent_key], - "policy_optimizer": self._policy_optimizer, - "num_steps": self._num_steps, - } - - subdir = os.path.join("trainer", agent_key) - checkpointer = tf2_savers.Checkpointer( - time_delta_minutes=15, - directory=checkpoint_subpath, - objects_to_save=objects_to_save, - subdirectory=subdir, - ) - self._system_checkpointer[agent_key] = checkpointer - - self._timestamp: Optional[float] = None - - @tf.function - def _update_target_networks(self) -> None: - for key in self.unique_net_keys: - # Update target network. - online_variables = (*self._policy_networks[key].variables,) - target_variables = (*self._target_policy_networks[key].variables,) - - # Make online -> target network update ops. - if tf.math.mod(self._num_steps, self._target_update_period) == 0: - for src, dest in zip(online_variables, target_variables): - dest.assign(src) - self._num_steps.assign_add(1) - - def _step(self) -> Dict[str, Dict[str, Any]]: - # Update the target networks - self._update_target_networks() - - inputs = next(self._iterator) - - self._forward(inputs) - - self._backward() - - # Log losses per agent - return self.policy_losses - - # Forward pass that calculates loss. - def _forward(self, inputs: Any) -> None: - data = tree.map_structure( - lambda v: tf.expand_dims(v, axis=0) if len(v.shape) <= 1 else v, inputs.data - ) - data = tf2_utils.batch_to_sequence(data) - - observations, actions, rewards, discounts, _, extra = data - - core_states = extra["core_states"] - - self.bs = actions[self._agents[0]].shape[1] # Batch Size - T = actions[self._agents[0]].shape[0] # Episode Length - - with tf.GradientTape(persistent=True) as tape: - policy_losses = {agent_id: tf.zeros(self.bs) for agent_id in self._agents} - - # Unroll episode and store states, messages, action values - # for policy and target policy - policy = {} - target_policy = {} - - policy["messages"] = { - -1: { - agent_id: core_states[agent_id]["message"][0] - for agent_id in self._agents - } - } - policy["states"] = { - -1: { - agent_id: core_states[agent_id]["state"][0] - for agent_id in self._agents - } - } - policy["actions"] = {} - policy["channel"] = {} - - target_policy["messages"] = { - -1: { - agent_id: core_states[agent_id]["message"][0] - for agent_id in self._agents - } - } - target_policy["states"] = { - -1: { - agent_id: core_states[agent_id]["state"][0] - for agent_id in self._agents - } - } - target_policy["actions"] = {} - target_policy["channel"] = {} - - # For all time-steps - for t in range(0, T, 1): - policy["channel"][t - 1] = self._communication_module.process_messages( - policy["messages"][t - 1] - )[self._agents[0]] - target_policy["channel"][ - t - 1 - ] = self._communication_module.process_messages( - target_policy["messages"][t - 1] - )[ - self._agents[0] - ] - - policy["messages"][t] = {} - policy["states"][t] = {} - policy["actions"][t] = {} - - target_policy["messages"][t] = {} - target_policy["states"][t] = {} - target_policy["actions"][t] = {} - - # For each agent - for agent_id in self._agents: - agent_type = self.agent_net_keys[agent_id] - # Agent input at time t - obs_in = observations[agent_id].observation[t] - - # Policy - state_in = policy["states"][t - 1][agent_id] - message_in = policy["channel"][t - 1] - - (action, message), state = self._policy_networks[agent_type]( - obs_in, state_in, message_in - ) - - # Target policy - target_state_in = target_policy["states"][t - 1][agent_id] - target_message_in = target_policy["channel"][t - 1] - - ( - target_action, - target_message, - ), target_state = self._target_policy_networks[agent_type]( - obs_in, target_state_in, target_message_in - ) - - policy["messages"][t][agent_id] = message - policy["states"][t][agent_id] = state - policy["actions"][t][agent_id] = action - - target_policy["messages"][t][agent_id] = target_message - target_policy["states"][t][agent_id] = target_state - target_policy["actions"][t][agent_id] = target_action - - policy["channel"][t] = self._communication_module.process_messages( - policy["messages"][t] - )[self._agents[0]] - - target_policy["channel"][t] = self._communication_module.process_messages( - target_policy["messages"][t] - )[self._agents[0]] - - # Backtrack episode and calculate loss - # For t=T to 1, -1 do - for t in range(T - 1, 0, -1): - # For each agent a do - for agent_id in self._agents: - # agent_type = self.agent_net_keys[agent_id] - # All at timestep t - 1 - action = actions[agent_id][t - 1] - message = core_states[agent_id]["message"][t - 1] - reward = rewards[agent_id][t - 1] - terminal = t == T - 1 - - discount = tf.cast( - self._discount, dtype=discounts[agent_id][t, 0].dtype - ) - - # y_t_a = r_t - y_action = reward - y_message = reward - - # y_t_a = r_t + discount * max_u Q(t) - if not terminal: - y_action += discount * tf.reduce_max( - target_policy["actions"][t][agent_id] - ) - y_message += discount * tf.reduce_max( - target_policy["messages"][t][agent_id] - ) - - # d_Q_t_a = y_t_a - Q(t-1) - td_action = y_action - tf.gather( - policy["actions"][t - 1][agent_id], action, batch_dims=1 - ) - - # d_theta = d_theta + d_Q_t_a ^ 2 - policy_losses[agent_id] += td_action ** 2 - - # Communication grads for RIAL - # td_comm = y_message - tf.gather( - # policy["messages"][t - 1][agent_id], - # tf.argmax(message, axis=-1), - # batch_dims=1, - # ) - # policy_losses[agent_id] += td_comm ** 2 - - # Average over batches - for key in policy_losses.keys(): - policy_losses[key] = { - "policy_loss": tf.reduce_mean(policy_losses[key], axis=0) - } - - self.policy_losses: Dict[str, Dict[str, tf.Tensor]] = policy_losses - self.tape = tape - - def _backward(self) -> None: - - # TODO (dries): I still need to figure out the - # total_loss thing. Not per agent I see but per batch? - - # Calculate the gradients and update the networks - policy_losses = self.policy_losses - - tape = self.tape - for agent in self._agents: - agent_key = self.agent_net_keys[agent] - - # Get trainable variables. - # policy_variables = ( - # self._observation_networks[agent_key].trainable_variables - # + self._policy_networks[agent_key].trainable_variables - # ) - policy_variables = self._policy_networks[agent_key].trainable_variables - - # Compute gradients. - # Note: Warning "WARNING:tensorflow:Calling GradientTape.gradient - # on a persistent tape inside its context is significantly less efficient - # than calling it outside the context." caused by losses.dpg, which calls - # tape.gradient. - policy_gradients = tape.gradient(policy_losses[agent], policy_variables) - - # Maybe clip gradients. - if self._clipping: - policy_gradients = tf.clip_by_global_norm(policy_gradients, 40.0)[0] - - # Apply gradients. - self._policy_optimizer.apply(policy_gradients, policy_variables) - train_utils.safe_del(self, "tape") - - def step(self) -> None: - # Run the learning step. - fetches = self._step() - - # Compute elapsed time. - timestamp = time.time() - elapsed_time = timestamp - self._timestamp if self._timestamp else 0 - self._timestamp = timestamp - - # Update our counts and record it. - counts = self._counter.increment(steps=1, walltime=elapsed_time) - fetches.update(counts) - - # Checkpoint the networks. - if len(self._system_checkpointer.keys()) > 0: - for agent_key in self.unique_net_keys: - checkpointer = self._system_checkpointer[agent_key] - checkpointer.save() - - self._logger.write(fetches) - - def get_variables(self, names: Sequence[str]) -> Dict[str, Dict[str, np.ndarray]]: - variables: Dict[str, Dict[str, np.ndarray]] = {} - for network_type in names: - variables[network_type] = { - agent: tf2_utils.to_numpy( - self._system_network_variables[network_type][agent] - ) - for agent in self.unique_net_keys - } - return variables + super().__init__( + agents=agents, + agent_types=agent_types, + q_networks=q_networks, + target_q_networks=target_q_networks, + target_update_period=target_update_period, + dataset=dataset, + optimizer=optimizer, + discount=discount, + shared_weights=shared_weights, + exploration_scheduler=exploration_scheduler, + clipping=clipping, + fingerprint=fingerprint, + counter=counter, + logger=logger, + checkpoint=checkpoint, + checkpoint_subpath=checkpoint_subpath, + communication_module=communication_module, + ) \ No newline at end of file From ad37b1d66c179f237f63408518b4c20ab846ad4b Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Sat, 22 May 2021 17:40:19 +0200 Subject: [PATCH 029/188] fix mypy issues --- mava/utils/environments/starcraft2_utils.py | 20 +++---- mava/wrappers/__init__.py | 2 +- mava/wrappers/pettingzoo.py | 2 +- mava/wrappers/starcraft2.py | 63 +++++++++++---------- 4 files changed, 44 insertions(+), 43 deletions(-) diff --git a/mava/utils/environments/starcraft2_utils.py b/mava/utils/environments/starcraft2_utils.py index 5af3a9f2e..dddf84bca 100644 --- a/mava/utils/environments/starcraft2_utils.py +++ b/mava/utils/environments/starcraft2_utils.py @@ -15,31 +15,27 @@ """Starcraft 2 environment factory.""" -import importlib -from typing import Any, List, Optional, Union +from typing import Any, Optional import dm_env -import numpy as np - from smac.env import StarCraft2Env -from mava.wrappers import ParallelEnvWrapper, SMACEnvWrapper + +from mava.wrappers import SMACEnvWrapper def make_environment( evaluation: bool = False, - map_name="3m", + map_name: str = "3m", random_seed: Optional[int] = None, **kwargs: Any, ) -> dm_env.Environment: - """Wraps an Pettingzoo environment. + """Wraps an starcraft 2 environment. Args: - env_class: str, class of the environment, e.g. MPE or Atari. - env_name: str, name of environment, .e.g simple_spread or Pong. - evaluation: bool, to change the behaviour during evaluation. + map_name: str, name of micromanagement level. Returns: - A Pettingzoo environment wrapped as a DeepMind environment. + A starcraft 2 smac environment wrapped as a DeepMind environment. """ del evaluation @@ -51,4 +47,4 @@ def make_environment( if random_seed and hasattr(environment, "seed"): environment.seed(random_seed) - return environment \ No newline at end of file + return environment diff --git a/mava/wrappers/__init__.py b/mava/wrappers/__init__.py index 8a0859e72..538c70390 100644 --- a/mava/wrappers/__init__.py +++ b/mava/wrappers/__init__.py @@ -24,7 +24,7 @@ PettingZooAECEnvWrapper, PettingZooParallelEnvWrapper, ) -from mava.wrappers.starcraft import SMACEnvWrapper +from mava.wrappers.starcraft2 import SMACEnvWrapper from mava.wrappers.system_trainer_statistics import ( DetailedTrainerStatistics, DetailedTrainerStatisticsWithEpsilon, diff --git a/mava/wrappers/pettingzoo.py b/mava/wrappers/pettingzoo.py index 1c9a64164..1671fd3ea 100644 --- a/mava/wrappers/pettingzoo.py +++ b/mava/wrappers/pettingzoo.py @@ -218,7 +218,7 @@ def __init__( self._environment, env_preprocess_wrappers ) - def reset(self) -> Tuple[dm_env.TimeStep, np.array]: + def reset(self) -> dm_env.TimeStep: """Resets the episode.""" self._reset_next_step = False self._step_type = dm_env.StepType.FIRST diff --git a/mava/wrappers/starcraft2.py b/mava/wrappers/starcraft2.py index 2e86a3ee3..6314b36f0 100644 --- a/mava/wrappers/starcraft2.py +++ b/mava/wrappers/starcraft2.py @@ -17,8 +17,8 @@ # Documentation available at smac/blob/master/docs/smac.md """Wraps a StarCraft II MARL environment (SMAC) as a dm_env environment.""" -import random -from typing import Any, Dict, List, Optional, Tuple, Type + +from typing import Any, Dict, List, Tuple, Type import dm_env import numpy as np @@ -39,16 +39,16 @@ class SMACEnvWrapper(ParallelEnvWrapper): Based on RLlib wrapper provided by SMAC. """ - def __init__(self, **smac_args: Optional[Tuple]) -> None: + def __init__(self, environment: StarCraft2Env) -> None: """Create a new multi-agent StarCraft env compatible with RLlib. Arguments: smac_args (dict): Arguments to pass to the underlying smac.env.starcraft.StarCraft2Env instance. """ - self._environment = StarCraft2Env(**smac_args) + self._environment = environment self._reset_next_step = True - self._ready_agents: List = [] + self._agents: List = [] self.observation_space = Dict( { "observation": Box(-1, 1, shape=(self._env.get_obs_size(),)), @@ -71,32 +71,32 @@ def reset(self) -> Tuple[dm_env.TimeStep, np.array]: # Convert observations observe: Dict[str, np.ndarray] = {} - self.possible_agents = [] + self._possible_agents = [] for i, obs in enumerate(obs_list): agent = f"agent_{i}" observe[agent] = { - "action_mask": np.array(self._env.get_avail_agent_actions(i)), "observation": obs, + "action_mask": np.array(self._env.get_avail_agent_actions(i)), } - self.possible_agents.append(agent) + self._possible_agents.append(agent) observations = self._convert_observations( - observe, {agent: False for agent in self.possible_agents} + observe, {agent: False for agent in self._possible_agents} ) - self._ready_agents = list(range(len(obs_list))) + self._agents = list(range(len(obs_list))) # create discount spec discount_spec = self.discount_spec() self._discounts = { agent: convert_np_type(discount_spec[agent].dtype, 1) - for agent in self._environment.possible_agents + for agent in self._possible_agents } # create rewards spec rewards_spec = self.reward_spec() rewards = { agent: convert_np_type(rewards_spec[agent].dtype, 0) - for agent in self.possible_agents + for agent in self._possible_agents } # dm_env timestep @@ -133,28 +133,32 @@ def step(self, actions: Dict[str, np.ndarray]) -> Tuple[dm_env.TimeStep, np.arra # ) actions_feed = list(actions.values()) - reward, terminated, info = self._env.step(actions_feed) + reward, terminated, info = self._environment.step(actions_feed) obs_list = self._environment.get_obs() state = self._environment.get_state() self._env_done = terminated - observations = {} + observe = {} + rewards = {} + dones = {} for i, obs in enumerate(obs_list): - observations[f"agent_{i}"] = { - "action_mask": self._env.get_avail_agent_actions(i), + agent = f"agent_{i}" + observe[agent] = { "observation": obs, + "action_mask": np.array(self._env.get_avail_agent_actions(i)), } - rewards = {agent: reward for agent in actions.keys()} - dones = {agent: terminated for agent in actions.keys()} - - # self._ready_agents = list(range(len(obs_list))) + rewards[agent] = reward + dones[agent] = terminated + observations = self._convert_observations(observe, dones) + self._agents = list(range(len(obs_list))) rewards_spec = self.reward_spec() + # Handle empty rewards if not rewards: rewards = { agent: convert_np_type(rewards_spec[agent].dtype, 0) - for agent in self.possible_agents + for agent in self._possible_agents } else: rewards = { @@ -162,9 +166,6 @@ def step(self, actions: Dict[str, np.ndarray]) -> Tuple[dm_env.TimeStep, np.arra for agent, reward in rewards.items() } - if observations: - observations = self._convert_observations(observations, dones) - if self.env_done(): self._step_type = dm_env.StepType.LAST self._reset_next_step = True @@ -214,22 +215,22 @@ def observation_spec(self) -> types.Observation: legal_actions=_convert_to_spec(self.observation_space["action_mask"]), terminal=specs.Array((1,), np.float32), ) - for agent in self.possible_agents + for agent in self._possible_agents } def action_spec(self) -> Dict[str, specs.DiscreteArray]: return { agent: _convert_to_spec(self._environment.action_space) - for agent in self.possible_agents + for agent in self._possible_agents } def reward_spec(self) -> Dict[str, specs.Array]: - return {agent: specs.Array((), np.float32) for agent in self.possible_agents} + return {agent: specs.Array((), np.float32) for agent in self._possible_agents} def discount_spec(self) -> Dict[str, specs.BoundedArray]: return { agent: specs.BoundedArray((), np.float32, minimum=0, maximum=1.0) - for agent in self.possible_agents + for agent in self._possible_agents } def extra_spec(self) -> Dict[str, specs.BoundedArray]: @@ -237,7 +238,11 @@ def extra_spec(self) -> Dict[str, specs.BoundedArray]: @property def agents(self) -> List: - return self._environment.agents + return self._agents + + @property + def possible_agents(self) -> List: + return self._possible_agents @property def environment(self) -> ParallelEnv: From 47fed0abe2af9b03536e47609e884949933a7e4b Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Sat, 22 May 2021 17:42:56 +0200 Subject: [PATCH 030/188] change name of wrapper to smac --- mava/utils/environments/{starcraft2_utils.py => smac_utils.py} | 0 mava/wrappers/__init__.py | 2 +- mava/wrappers/{starcraft2.py => smac.py} | 0 3 files changed, 1 insertion(+), 1 deletion(-) rename mava/utils/environments/{starcraft2_utils.py => smac_utils.py} (100%) rename mava/wrappers/{starcraft2.py => smac.py} (100%) diff --git a/mava/utils/environments/starcraft2_utils.py b/mava/utils/environments/smac_utils.py similarity index 100% rename from mava/utils/environments/starcraft2_utils.py rename to mava/utils/environments/smac_utils.py diff --git a/mava/wrappers/__init__.py b/mava/wrappers/__init__.py index 538c70390..b0f47b4b8 100644 --- a/mava/wrappers/__init__.py +++ b/mava/wrappers/__init__.py @@ -24,7 +24,7 @@ PettingZooAECEnvWrapper, PettingZooParallelEnvWrapper, ) -from mava.wrappers.starcraft2 import SMACEnvWrapper +from mava.wrappers.smac import SMACEnvWrapper from mava.wrappers.system_trainer_statistics import ( DetailedTrainerStatistics, DetailedTrainerStatisticsWithEpsilon, diff --git a/mava/wrappers/starcraft2.py b/mava/wrappers/smac.py similarity index 100% rename from mava/wrappers/starcraft2.py rename to mava/wrappers/smac.py From 8bc92952438ee59d5ea5b776f608f376c42d68ff Mon Sep 17 00:00:00 2001 From: Kevin Eloff Date: Sat, 22 May 2021 17:43:25 +0200 Subject: [PATCH 031/188] feat: use dial executor --- examples/debugging_envs/run_recurrent_dial.py | 7 ++++--- mava/systems/tf/dial/builder.py | 13 +++++++------ mava/systems/tf/dial/system.py | 19 ++++++++++++------- mava/systems/tf/dial/training.py | 2 +- 4 files changed, 24 insertions(+), 17 deletions(-) diff --git a/examples/debugging_envs/run_recurrent_dial.py b/examples/debugging_envs/run_recurrent_dial.py index 57671b930..6c8e9dfca 100644 --- a/examples/debugging_envs/run_recurrent_dial.py +++ b/examples/debugging_envs/run_recurrent_dial.py @@ -35,7 +35,7 @@ epsilon_greedy_action_selector, ) from mava.systems.tf import dial -from mava.systems.tf.madqn.execution import MADQNRecurrentCommExecutor +from mava.systems.tf.dial.execution import DIALExecutor from mava.systems.tf.madqn.training import RecurrentCommMADQNTrainer from mava.utils import lp_utils from mava.utils.environments import debugging_utils @@ -160,13 +160,14 @@ def main(_: Any) -> None: logger_factory=logger_factory, num_executors=2, trainer_fn=RecurrentCommMADQNTrainer, - executor_fn=MADQNRecurrentCommExecutor, + executor_fn=DIALExecutor, exploration_scheduler_fn=LinearExplorationScheduler, communication_module=BroadcastedCommunication, sequence_length=6, epsilon_min=0.05, epsilon_decay=5e-4, - optimizer=snt.optimizers.Adam(learning_rate=1e-4), + # optimizer=snt.optimizers.Adam(learning_rate=1e-4), + optimizer=snt.optimizers.RMSProp(learning_rate=1e-4, momentum=0.95), checkpoint_subpath=checkpoint_dir, n_step=1, batch_size=32, diff --git a/mava/systems/tf/dial/builder.py b/mava/systems/tf/dial/builder.py index 6e4af83e4..6ecd93f77 100644 --- a/mava/systems/tf/dial/builder.py +++ b/mava/systems/tf/dial/builder.py @@ -31,8 +31,7 @@ ) from mava.components.tf.modules.stabilising import FingerPrintStabalisation from mava.systems.tf import executors -from mava.systems.tf.dial import training -from mava.systems.tf.madqn import execution +from mava.systems.tf.madqn import execution, training @dataclasses.dataclass @@ -89,7 +88,9 @@ class DIALBuilder: def __init__( self, config: DIALConfig, - trainer_fn: Type[training.DIALTrainer] = training.DIALTrainer, + trainer_fn: Type[ + training.RecurrentCommMADQNTrainer + ] = training.RecurrentCommMADQNTrainer, executor_fn: Type[core.Executor] = execution.MADQNRecurrentCommExecutor, extra_specs: Dict[str, Any] = {}, exploration_scheduler_fn: Type[ @@ -218,10 +219,10 @@ def make_executor( self, q_networks: Dict[str, snt.Module], action_selectors: Dict[str, Any], + communication_module: BaseCommunicationModule, adder: Optional[adders.ParallelAdder] = None, variable_source: Optional[core.VariableSource] = None, - trainer: Optional[training.DIALTrainer] = None, - communication_module: Optional[BaseCommunicationModule] = None, + trainer: Optional[training.RecurrentCommMADQNTrainer] = None, evaluator: bool = False, ) -> core.Executor: """Create an executor instance. @@ -274,9 +275,9 @@ def make_trainer( self, networks: Dict[str, Dict[str, snt.Module]], dataset: Iterator[reverb.ReplaySample], + communication_module: BaseCommunicationModule, counter: Optional[counting.Counter] = None, logger: Optional[types.NestedLogger] = None, - communication_module: Optional[BaseCommunicationModule] = None, ) -> core.Trainer: """Creates an instance of the trainer. Args: diff --git a/mava/systems/tf/dial/system.py b/mava/systems/tf/dial/system.py index f6d5ef611..a6bb1b60b 100644 --- a/mava/systems/tf/dial/system.py +++ b/mava/systems/tf/dial/system.py @@ -30,14 +30,17 @@ from mava import core from mava import specs as mava_specs from mava.components.tf.architectures import DecentralisedValueActor -from mava.components.tf.modules.communication import BaseCommunicationModule +from mava.components.tf.modules.communication import ( + BaseCommunicationModule, + BroadcastedCommunication, +) from mava.components.tf.modules.exploration import LinearExplorationScheduler from mava.components.tf.modules.stabilising import FingerPrintStabalisation from mava.environment_loop import ParallelEnvironmentLoop from mava.systems.tf import executors from mava.systems.tf import savers as tf2_savers -from mava.systems.tf.dial import builder, training -from mava.systems.tf.madqn import execution +from mava.systems.tf.dial import builder +from mava.systems.tf.madqn import execution, training from mava.utils import lp_utils from mava.utils.loggers import MavaLogger, logger_utils from mava.wrappers import DetailedPerAgentStatistics @@ -83,8 +86,10 @@ def __init__( network_factory: Callable[[acme_specs.BoundedArray], Dict[str, snt.Module]], logger_factory: Callable[[str], MavaLogger] = None, architecture: Type[DecentralisedValueActor] = DecentralisedValueActor, - trainer_fn: Type[training.DIALTrainer] = training.DIALTrainer, - communication_module: Type[BaseCommunicationModule] = None, + trainer_fn: Type[ + training.RecurrentCommMADQNTrainer + ] = training.RecurrentCommMADQNTrainer, + communication_module: Type[BaseCommunicationModule] = BroadcastedCommunication, executor_fn: Type[core.Executor] = execution.MADQNFeedForwardExecutor, exploration_scheduler_fn: Type[ LinearExplorationScheduler @@ -289,7 +294,7 @@ def executor( replay: reverb.Client, variable_source: acme.VariableSource, counter: counting.Counter, - trainer: Optional[training.DIALTrainer] = None, + trainer: Optional[training.RecurrentCommMADQNTrainer] = None, ) -> mava.ParallelEnvironmentLoop: """The executor process.""" @@ -364,7 +369,7 @@ def evaluator( self, variable_source: acme.VariableSource, counter: counting.Counter, - trainer: training.DIALTrainer, + trainer: training.RecurrentCommMADQNTrainer, ) -> Any: """The evaluation process.""" diff --git a/mava/systems/tf/dial/training.py b/mava/systems/tf/dial/training.py index 2803a04d7..982e0350b 100644 --- a/mava/systems/tf/dial/training.py +++ b/mava/systems/tf/dial/training.py @@ -73,4 +73,4 @@ def __init__( checkpoint=checkpoint, checkpoint_subpath=checkpoint_subpath, communication_module=communication_module, - ) \ No newline at end of file + ) From 2c7af20696b5b467996012a91d6673dec4f5d4de Mon Sep 17 00:00:00 2001 From: Kevin Eloff Date: Sat, 22 May 2021 17:51:33 +0200 Subject: [PATCH 032/188] feat: update env --- examples/debugging_envs/run_recurrent_dial.py | 4 ++-- mava/utils/debugging/environments/switch_game.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/debugging_envs/run_recurrent_dial.py b/examples/debugging_envs/run_recurrent_dial.py index 6c8e9dfca..1c205a1b0 100644 --- a/examples/debugging_envs/run_recurrent_dial.py +++ b/examples/debugging_envs/run_recurrent_dial.py @@ -36,7 +36,7 @@ ) from mava.systems.tf import dial from mava.systems.tf.dial.execution import DIALExecutor -from mava.systems.tf.madqn.training import RecurrentCommMADQNTrainer +from mava.systems.tf.dial.training import DIALTrainer from mava.utils import lp_utils from mava.utils.environments import debugging_utils from mava.utils.loggers import logger_utils @@ -159,7 +159,7 @@ def main(_: Any) -> None: network_factory=network_factory, logger_factory=logger_factory, num_executors=2, - trainer_fn=RecurrentCommMADQNTrainer, + trainer_fn=DIALTrainer, executor_fn=DIALExecutor, exploration_scheduler_fn=LinearExplorationScheduler, communication_module=BroadcastedCommunication, diff --git a/mava/utils/debugging/environments/switch_game.py b/mava/utils/debugging/environments/switch_game.py index 4931bad4f..21fc6845a 100644 --- a/mava/utils/debugging/environments/switch_game.py +++ b/mava/utils/debugging/environments/switch_game.py @@ -128,7 +128,7 @@ def _get_info(self, agent_id: str) -> Dict: # get observation for a particular agent def _get_obs(self, a_i: int, agent_id: str) -> np.array: selected = 1.0 if a_i == self.selected_agent else 0.0 - return np.array([selected, self.time], dtype=np.float32) + return np.array([selected, a_i], dtype=np.float32) # get dones for a particular agent # unused right now -- agents are allowed to go beyond the viewing screen From fb9157002bf9661de725129a438289f771ee2ab2 Mon Sep 17 00:00:00 2001 From: Kevin Eloff Date: Sat, 22 May 2021 18:16:56 +0200 Subject: [PATCH 033/188] feat: message masking --- examples/debugging_envs/run_recurrent_dial.py | 5 + mava/systems/tf/dial/execution.py | 28 ++++++ mava/systems/tf/dial/training.py | 97 ++++++++++++++++++- 3 files changed, 129 insertions(+), 1 deletion(-) diff --git a/examples/debugging_envs/run_recurrent_dial.py b/examples/debugging_envs/run_recurrent_dial.py index 1c205a1b0..e415bce53 100644 --- a/examples/debugging_envs/run_recurrent_dial.py +++ b/examples/debugging_envs/run_recurrent_dial.py @@ -37,6 +37,9 @@ from mava.systems.tf import dial from mava.systems.tf.dial.execution import DIALExecutor from mava.systems.tf.dial.training import DIALTrainer + +# from mava.systems.tf.madqn.execution import MADQNRecurrentCommExecutor +# from mava.systems.tf.madqn.training import RecurrentCommMADQNTrainer from mava.utils import lp_utils from mava.utils.environments import debugging_utils from mava.utils.loggers import logger_utils @@ -161,6 +164,8 @@ def main(_: Any) -> None: num_executors=2, trainer_fn=DIALTrainer, executor_fn=DIALExecutor, + # trainer_fn=RecurrentCommMADQNTrainer, + # executor_fn=MADQNRecurrentCommExecutor, exploration_scheduler_fn=LinearExplorationScheduler, communication_module=BroadcastedCommunication, sequence_length=6, diff --git a/mava/systems/tf/dial/execution.py b/mava/systems/tf/dial/execution.py index 6fc3971d7..ce68e973d 100644 --- a/mava/systems/tf/dial/execution.py +++ b/mava/systems/tf/dial/execution.py @@ -25,6 +25,8 @@ from typing import Any, Dict, Optional import sonnet as snt +import tensorflow as tf +from acme import types from acme.tf import variable_utils as tf2_variable_utils from mava import adders @@ -77,3 +79,29 @@ def __init__( self._states: Dict[str, Any] = {} self._messages: Dict[str, Any] = {} + + def _policy( + self, + agent: str, + observation: types.NestedTensor, + state: types.NestedTensor, + message: types.NestedTensor, + legal_actions: types.NestedTensor, + epsilon: tf.Tensor, + ) -> types.NestedTensor: + + (action, m_values), new_state = super()._policy( + agent, + observation, + state, + message, + legal_actions, + epsilon, + ) + + # Mask message if obs[0] == 1. + # Note: this is specific to switch env + if observation[0] == 0: + m_values = tf.zeros_like(m_values) + + return (action, m_values), new_state diff --git a/mava/systems/tf/dial/training.py b/mava/systems/tf/dial/training.py index 982e0350b..d8f29ae3d 100644 --- a/mava/systems/tf/dial/training.py +++ b/mava/systems/tf/dial/training.py @@ -16,10 +16,14 @@ """DIAL trainer implementation.""" -from typing import Dict, List +from typing import Any, Dict, List import sonnet as snt import tensorflow as tf +import tree +import trfl +from acme.tf import utils as tf2_utils +from acme.types import NestedArray from acme.utils import counting, loggers from mava.components.tf.modules.communication import BaseCommunicationModule @@ -74,3 +78,94 @@ def __init__( checkpoint_subpath=checkpoint_subpath, communication_module=communication_module, ) + + def _forward(self, inputs: Any) -> None: + data = tree.map_structure( + lambda v: tf.expand_dims(v, axis=0) if len(v.shape) <= 1 else v, inputs.data + ) + data = tf2_utils.batch_to_sequence(data) + + observations, actions, rewards, discounts, _, extra = data + + # core_states = extra["core_states"] + core_state = tree.map_structure( + lambda s: s[:, 0, :], inputs.data.extras["core_states"] + ) + core_message = tree.map_structure( + lambda s: s[:, 0, :], inputs.data.extras["core_messages"] + ) + + with tf.GradientTape(persistent=True) as tape: + q_network_losses: Dict[str, NestedArray] = { + agent: {"q_value_loss": tf.zeros(())} for agent in self._agents + } + + T = actions[self._agents[0]].shape[0] + + state = {agent: core_state[agent][0] for agent in self._agents} + target_state = {agent: core_state[agent][0] for agent in self._agents} + + message = {agent: core_message[agent][0] for agent in self._agents} + target_message = {agent: core_message[agent][0] for agent in self._agents} + + # _target_q_networks must be 1 step ahead + target_channel = self._communication_module.process_messages(target_message) + for agent in self._agents: + agent_key = self.agent_net_keys[agent] + (q_targ, m), s = self._target_q_networks[agent_key]( + observations[agent].observation[0], + target_state[agent], + target_channel[agent], + ) + target_state[agent] = s + target_message[agent] = m + + for t in range(1, T, 1): + channel = self._communication_module.process_messages(message) + target_channel = self._communication_module.process_messages( + target_message + ) + + for agent in self._agents: + agent_key = self.agent_net_keys[agent] + + # Cast the additional discount + # to match the environment discount dtype. + + discount = tf.cast(self._discount, dtype=discounts[agent][0].dtype) + + (q_targ, m), s = self._target_q_networks[agent_key]( + observations[agent].observation[t], + target_state[agent], + target_channel[agent], + ) + print(m.shape) + target_state[agent] = s + target_message[agent] = tf.math.multiply( + m, observations[agent].observation[t][:, :1] + ) + + (q, m), s = self._q_networks[agent_key]( + observations[agent].observation[t - 1], + state[agent], + channel[agent], + ) + print("m2 ", m.shape) + state[agent] = s + message[agent] = tf.math.multiply( + m, observations[agent].observation[t - 1][:, :1] + ) + + loss, _ = trfl.qlearning( + q, + actions[agent][t - 1], + rewards[agent][t - 1], + discount * discounts[agent][t], + q_targ, + ) + + loss = tf.reduce_mean(loss) + q_network_losses[agent]["q_value_loss"] += loss + + self._q_network_losses = q_network_losses + self.tape = tape From 6cb2edec9ce0a52e2dfabe5638cb83d54b6a7cb6 Mon Sep 17 00:00:00 2001 From: Dries Date: Sat, 22 May 2021 19:13:33 +0200 Subject: [PATCH 034/188] fix: mypy. --- mava/environment_loop.py | 3 ++- mava/systems/system.py | 4 ++-- mava/systems/tf/mad4pg/__init__.py | 6 ++++-- mava/systems/tf/mad4pg/execution.py | 10 +++++++++- mava/systems/tf/mad4pg/system.py | 2 +- mava/systems/tf/maddpg/__init__.py | 7 ++++--- mava/systems/tf/maddpg/execution.py | 25 ++++++++++++++----------- mava/systems/tf/maddpg/system.py | 3 ++- 8 files changed, 38 insertions(+), 22 deletions(-) diff --git a/mava/environment_loop.py b/mava/environment_loop.py index 3e9e7e755..6599abef5 100644 --- a/mava/environment_loop.py +++ b/mava/environment_loop.py @@ -282,7 +282,8 @@ def run_episode(self) -> loggers.LoggingData: actions = self._get_actions(timestep) if type(actions) == tuple: - # Return other extra action information like the policy information as well + # Return other action information + # e.g. the policy information. env_actions, _ = actions else: env_actions = actions diff --git a/mava/systems/system.py b/mava/systems/system.py index 769295d81..58ca13b2a 100644 --- a/mava/systems/system.py +++ b/mava/systems/system.py @@ -15,7 +15,7 @@ """The base system interface.""" -from typing import Dict, Sequence, Union +from typing import Any, Dict, Sequence, Tuple, Union import dm_env from acme import types @@ -77,7 +77,7 @@ def select_action( def select_actions( self, observations: Dict[str, types.NestedArray] - ) -> Dict[str, types.NestedArray]: + ) -> Union[Dict[str, Any], Tuple[Dict[str, Any], Dict[str, Any]]]: return self._executor.select_actions(observations) def observe_first( diff --git a/mava/systems/tf/mad4pg/__init__.py b/mava/systems/tf/mad4pg/__init__.py index 5b3235d5f..ab020dea6 100644 --- a/mava/systems/tf/mad4pg/__init__.py +++ b/mava/systems/tf/mad4pg/__init__.py @@ -14,6 +14,10 @@ # limitations under the License. """Implementations of a MAD4PG agent.""" +from mava.systems.tf.mad4pg.execution import ( + MAD4PGFeedForwardExecutor, + MAD4PGRecurrentExecutor, +) from mava.systems.tf.mad4pg.system import MAD4PG from mava.systems.tf.mad4pg.training import ( BaseMAD4PGTrainer, @@ -21,5 +25,3 @@ DecentralisedMAD4PGTrainer, StateBasedMAD4PGTrainer, ) -from mava.systems.tf.mad4pg.execution import (MAD4PGFeedForwardExecutor, - MAD4PGRecurrentExecutor) diff --git a/mava/systems/tf/mad4pg/execution.py b/mava/systems/tf/mad4pg/execution.py index 30d4700eb..72f27a6b0 100644 --- a/mava/systems/tf/mad4pg/execution.py +++ b/mava/systems/tf/mad4pg/execution.py @@ -16,10 +16,14 @@ from typing import Dict, Optional import sonnet as snt +from acme.specs import EnvironmentSpec from acme.tf import variable_utils as tf2_variable_utils from mava import adders -from mava.systems.tf.maddpg.execution import MADDPGFeedForwardExecutor, MADDPGRecurrentExecutor +from mava.systems.tf.maddpg.execution import ( + MADDPGFeedForwardExecutor, + MADDPGRecurrentExecutor, +) class MAD4PGFeedForwardExecutor(MADDPGFeedForwardExecutor): @@ -33,16 +37,20 @@ class MAD4PGFeedForwardExecutor(MADDPGFeedForwardExecutor): def __init__( self, policy_networks: Dict[str, snt.Module], + agent_specs: Dict[str, EnvironmentSpec], adder: Optional[adders.ParallelAdder] = None, variable_client: Optional[tf2_variable_utils.VariableClient] = None, shared_weights: bool = True, ): super().__init__( policy_networks=policy_networks, + agent_specs=agent_specs, adder=adder, variable_client=variable_client, shared_weights=shared_weights, ) + + class MAD4PGRecurrentExecutor(MADDPGRecurrentExecutor): """A recurrent executor for MAD4PG. An executor based on a recurrent policy for each agent in the system diff --git a/mava/systems/tf/mad4pg/system.py b/mava/systems/tf/mad4pg/system.py index f89a09668..60a3c65d8 100644 --- a/mava/systems/tf/mad4pg/system.py +++ b/mava/systems/tf/mad4pg/system.py @@ -24,8 +24,8 @@ from mava import specs as mava_specs from mava.components.tf.architectures import DecentralisedQValueActorCritic from mava.environment_loop import ParallelEnvironmentLoop -from mava.systems.tf.maddpg.execution import MADDPGFeedForwardExecutor from mava.systems.tf.mad4pg import training +from mava.systems.tf.maddpg.execution import MADDPGFeedForwardExecutor from mava.systems.tf.maddpg.system import MADDPG from mava.utils.loggers import MavaLogger diff --git a/mava/systems/tf/maddpg/__init__.py b/mava/systems/tf/maddpg/__init__.py index bdb6dd1eb..c6d952f6c 100644 --- a/mava/systems/tf/maddpg/__init__.py +++ b/mava/systems/tf/maddpg/__init__.py @@ -15,6 +15,10 @@ """Implementations of a MADDPG agent.""" +from mava.systems.tf.mad4pg.execution import ( + MADDPGFeedForwardExecutor, + MADDPGRecurrentExecutor, +) from mava.systems.tf.maddpg.system import MADDPG from mava.systems.tf.maddpg.training import ( BaseMADDPGTrainer, @@ -23,6 +27,3 @@ NetworkedMADDPGTrainer, StateBasedMADDPGTrainer, ) - -from mava.systems.tf.mad4pg.execution import (MADDPGFeedForwardExecutor, - MADDPGRecurrentExecutor) diff --git a/mava/systems/tf/maddpg/execution.py b/mava/systems/tf/maddpg/execution.py index 6ac12e005..d3e1f015f 100644 --- a/mava/systems/tf/maddpg/execution.py +++ b/mava/systems/tf/maddpg/execution.py @@ -20,18 +20,20 @@ import tensorflow as tf import tensorflow_probability as tfp from acme import types -from dm_env import specs -Array = specs.Array -BoundedArray = specs.BoundedArray -DiscreteArray = specs.DiscreteArray +from acme.specs import EnvironmentSpec # Internal imports. from acme.tf import utils as tf2_utils from acme.tf import variable_utils as tf2_variable_utils +from dm_env import specs from mava import adders from mava.systems.tf import executors -from acme.specs import EnvironmentSpec + +Array = specs.Array +BoundedArray = specs.BoundedArray +DiscreteArray = specs.DiscreteArray + tfd = tfp.distributions @@ -141,12 +143,13 @@ def observe( # TODO (dries): Sort out this mypy issue. self._adder.add(policy, next_timestep, next_extras) # type: ignore + class MADDPGRecurrentExecutor(executors.FeedForwardExecutor): def __init__( - self, - policy_networks: Dict[str, snt.Module], - adder: Optional[adders.ParallelAdder] = None, - variable_client: Optional[tf2_variable_utils.VariableClient] = None, - shared_weights: bool = True, + self, + policy_networks: Dict[str, snt.Module], + adder: Optional[adders.ParallelAdder] = None, + variable_client: Optional[tf2_variable_utils.VariableClient] = None, + shared_weights: bool = True, ): - raise NotImplementedError \ No newline at end of file + raise NotImplementedError diff --git a/mava/systems/tf/maddpg/system.py b/mava/systems/tf/maddpg/system.py index 19a4b6498..e41ac062e 100644 --- a/mava/systems/tf/maddpg/system.py +++ b/mava/systems/tf/maddpg/system.py @@ -32,13 +32,14 @@ from mava.components.tf.architectures import DecentralisedQValueActorCritic from mava.environment_loop import ParallelEnvironmentLoop from mava.systems.tf import executors -from mava.systems.tf.maddpg.execution import MADDPGFeedForwardExecutor from mava.systems.tf import savers as tf2_savers from mava.systems.tf.maddpg import builder, training +from mava.systems.tf.maddpg.execution import MADDPGFeedForwardExecutor from mava.utils import lp_utils from mava.utils.loggers import MavaLogger, logger_utils from mava.wrappers import DetailedPerAgentStatistics + class MADDPG: """MADDPG system. This implements a single-process DDPG system. This is an actor-critic based From 2b3e1c43cb719fadc5d90d1c180050dc72671d33 Mon Sep 17 00:00:00 2001 From: Dries Date: Sun, 23 May 2021 08:32:49 +0200 Subject: [PATCH 035/188] fix: Implement recurrent discrete/continuous MA-DDPG and MA-D4PG. --- mava/systems/tf/executors.py | 63 ++++--------- mava/systems/tf/mad4pg/execution.py | 2 + mava/systems/tf/maddpg/__init__.py | 2 +- mava/systems/tf/maddpg/execution.py | 140 ++++++++++++++++++++++++---- 4 files changed, 141 insertions(+), 66 deletions(-) diff --git a/mava/systems/tf/executors.py b/mava/systems/tf/executors.py index a6da36015..49b59ae1f 100644 --- a/mava/systems/tf/executors.py +++ b/mava/systems/tf/executors.py @@ -43,10 +43,10 @@ def __init__( """ # Store these for later use. + self._shared_weights = shared_weights + self._policy_networks = policy_networks self._adder = adder self._variable_client = variable_client - self._policy_networks = policy_networks - self._shared_weights = shared_weights @tf.function def _policy( @@ -146,12 +146,11 @@ def __init__( store_recurrent_state: Whether to pass the recurrent state to the adder. """ # Store these for later use. + self._policy_networks = policy_networks + self._shared_weights = shared_weights self._adder = adder self._variable_client = variable_client - self._policy_networks = policy_networks self._store_recurrent_state = store_recurrent_state - self._shared_weights = shared_weights - self._states: Dict[str, Any] = {} @tf.function @@ -160,7 +159,10 @@ def _policy( agent: str, observation: types.NestedTensor, state: types.NestedTensor, - ) -> Tuple[types.NestedTensor, types.NestedTensor]: + ) -> Union[ + Tuple[types.NestedTensor, types.NestedTensor], + Tuple[types.NestedTensor, types.NestedTensor, types.NestedTensor], + ]: # Add a dummy batch dimension and as a side effect convert numpy to TF. batched_observation = tf2_utils.add_batch_dim(observation) @@ -176,9 +178,9 @@ def _policy( return action, new_state - # def _update_state(self, agent: str, new_state: types.NestedArray) -> None: - # self._prev_states[agent] = self._states[agent] - # self._states[agent] = new_state + def _update_state(self, agent: str, new_state: types.NestedArray) -> None: + # Bookkeeping of recurrent states for the observe method. + self._states[agent] = new_state def select_action( self, agent: str, observation: types.NestedArray @@ -209,7 +211,6 @@ def observe_first( timestep: dm_env.TimeStep, extras: Optional[Dict[str, types.NestedArray]] = {}, ) -> None: - # Re-initialize the RNN state. for agent, _ in timestep.observation.items(): # index network either on agent type or on agent id @@ -256,24 +257,14 @@ def observe( def select_actions( self, observations: Dict[str, types.NestedArray] - ) -> Dict[str, types.NestedArray]: + ) -> Union[ + Dict[str, types.NestedArray], + Tuple[Dict[str, types.NestedArray], Dict[str, types.NestedArray]], + ]: actions = {} for agent, observation in observations.items(): - # Step the recurrent policy forward given the current observation and state. - policy_output, new_state = self._policy( - agent, observation.observation, self._states[agent] - ) - - # Bookkeeping of recurrent states for the observe method. - self._states[agent] = new_state - # self._update_state(agent, new_state) - - # TODO Mask actions here using observation.legal_actions - # What happens in discrete vs cont case - actions[agent] = tf2_utils.to_numpy_squeeze(policy_output) - - # Return a numpy array with squeezed out batch dimension. + actions[agent] = self.select_action(agent=agent, observation=observation) return actions def update(self, wait: bool = False) -> None: @@ -446,27 +437,7 @@ def select_actions( self, observations: Dict[str, types.NestedArray] ) -> Dict[str, types.NestedArray]: actions = {} - - message_inputs = self._communication_module.process_messages(self._messages) - for agent, observation in observations.items(): - - # Step the recurrent policy forward given the current observation and state. - policy_output, message, new_state = self._policy( - agent, - observation.observation, - self._states[agent], - message_inputs[agent], - ) - - # Bookkeeping of recurrent states for the observe method. - self._states[agent] = new_state - - self._messages[agent] = message - - # TODO Mask actions here using observation.legal_actions - # What happens in discrete vs cont case - actions[agent] = tf2_utils.to_numpy_squeeze(policy_output) - + actions[agent] = self._select_action(agent, observations) # Return a numpy array with squeezed out batch dimension. return actions diff --git a/mava/systems/tf/mad4pg/execution.py b/mava/systems/tf/mad4pg/execution.py index 72f27a6b0..8819618c5 100644 --- a/mava/systems/tf/mad4pg/execution.py +++ b/mava/systems/tf/mad4pg/execution.py @@ -62,12 +62,14 @@ class MAD4PGRecurrentExecutor(MADDPGRecurrentExecutor): def __init__( self, policy_networks: Dict[str, snt.Module], + agent_specs: Dict[str, EnvironmentSpec], adder: Optional[adders.ParallelAdder] = None, variable_client: Optional[tf2_variable_utils.VariableClient] = None, shared_weights: bool = True, ): super().__init__( policy_networks=policy_networks, + agent_specs=agent_specs, adder=adder, variable_client=variable_client, shared_weights=shared_weights, diff --git a/mava/systems/tf/maddpg/__init__.py b/mava/systems/tf/maddpg/__init__.py index c6d952f6c..50841f280 100644 --- a/mava/systems/tf/maddpg/__init__.py +++ b/mava/systems/tf/maddpg/__init__.py @@ -15,7 +15,7 @@ """Implementations of a MADDPG agent.""" -from mava.systems.tf.mad4pg.execution import ( +from mava.systems.tf.maddpg.execution import ( MADDPGFeedForwardExecutor, MADDPGRecurrentExecutor, ) diff --git a/mava/systems/tf/maddpg/execution.py b/mava/systems/tf/maddpg/execution.py index d3e1f015f..8c70b408a 100644 --- a/mava/systems/tf/maddpg/execution.py +++ b/mava/systems/tf/maddpg/execution.py @@ -65,11 +65,13 @@ def __init__( """ # Store these for later use. - self._adder = adder - self._variable_client = variable_client - self._policy_networks = policy_networks self._agent_specs = agent_specs - self._shared_weights = shared_weights + super().__init__( + policy_networks=policy_networks, + shared_weights=shared_weights, + adder=adder, + variable_client=variable_client, + ) @tf.function def _policy( @@ -116,20 +118,9 @@ def select_actions( actions = {} policies = {} for agent, observation in observations.items(): - action, policy = self.select_action(agent, observation) - actions[agent] = action - policies[agent] = policy + actions[agent], policies[agent] = self.select_action(agent, observation) return actions, policies - def observe_first( - self, - timestep: dm_env.TimeStep, - extras: Dict[str, types.NestedArray] = {}, - ) -> None: - - if self._adder: - self._adder.add_first(timestep, extras) - def observe( self, actions: Union[ @@ -139,17 +130,128 @@ def observe( next_extras: Dict[str, types.NestedArray] = {}, ) -> None: if self._adder: - env_actions, policy = actions + _, policy = actions # TODO (dries): Sort out this mypy issue. self._adder.add(policy, next_timestep, next_extras) # type: ignore -class MADDPGRecurrentExecutor(executors.FeedForwardExecutor): +class MADDPGRecurrentExecutor(executors.RecurrentExecutor): def __init__( self, policy_networks: Dict[str, snt.Module], + agent_specs: Dict[str, EnvironmentSpec], adder: Optional[adders.ParallelAdder] = None, variable_client: Optional[tf2_variable_utils.VariableClient] = None, shared_weights: bool = True, + store_recurrent_state: bool = True, ): - raise NotImplementedError + + """Initializes the executor. + Args: + networks: the (recurrent) policy to run for each agent in the system. + shared_weights: specify if weights are shared between agent networks. + adder: the adder object to which allows to add experiences to a + dataset/replay buffer. + variable_client: object which allows to copy weights from the trainer copy + of the policies to the executor copy (in case they are separate). + """ + + # Store these for later use. + self._agent_specs = agent_specs + + super().__init__( + policy_networks=policy_networks, + shared_weights=shared_weights, + adder=adder, + variable_client=variable_client, + store_recurrent_state=store_recurrent_state, + ) + + @tf.function + def _policy( + self, + agent: str, + observation: types.NestedTensor, + state: types.NestedTensor, + ) -> Tuple[types.NestedTensor, types.NestedTensor, types.NestedTensor]: + + # Add a dummy batch dimension and as a side effect convert numpy to TF. + batched_observation = tf2_utils.add_batch_dim(observation) + + # index network either on agent type or on agent id + agent_key = agent.split("_")[0] if self._shared_weights else agent + + # Compute the policy, conditioned on the observation. + policy, new_state = self._policy_networks[agent_key](batched_observation, state) + + # TODO (dries): Make this support hybrid action spaces. + if type(self._agent_specs[agent].actions) == BoundedArray: + # Continuous action + action = policy + elif type(self._agent_specs[agent].actions) == DiscreteArray: + action = tf.math.argmax(policy, axis=1) + else: + raise NotImplementedError + + return action, policy, new_state + + def select_action( + self, agent: str, observation: types.NestedArray + ) -> types.NestedArray: + + # TODO Mask actions here using observation.legal_actions + # Initialize the RNN state if necessary. + if self._states[agent] is None: + # index network either on agent type or on agent id + agent_key = agent.split("_")[0] if self._shared_weights else agent + self._states[agent] = self._policy_networks[agent_key].initia_state(1) + + # Step the recurrent policy forward given the current observation and state. + action, policy, new_state = self._policy( + agent, observation.observation, self._states[agent] + ) + + # Bookkeeping of recurrent states for the observe method. + self._update_state(agent, new_state) + + # Return a numpy array with squeezed out batch dimension. + action = tf2_utils.to_numpy_squeeze(action) + policy = tf2_utils.to_numpy_squeeze(policy) + return action, policy + + def select_actions( + self, observations: Dict[str, types.NestedArray] + ) -> Tuple[Dict[str, types.NestedArray], Dict[str, types.NestedArray]]: + + actions = {} + policies = {} + for agent, observation in observations.items(): + actions[agent], policies[agent] = self.select_action(agent, observation) + return actions, policies + + def observe( + self, + actions: Dict[str, types.NestedArray], + next_timestep: dm_env.TimeStep, + next_extras: Optional[Dict[str, types.NestedArray]] = {}, + ) -> None: + if not self._adder: + return + _, policy = actions + if not self._store_recurrent_state: + if next_extras: + # TODO (dries): Sort out this mypy issue. + self._adder.add(policy, next_timestep, next_extras) # type: ignore + else: + self._adder.add(policy, next_timestep) # type: ignore + return + + numpy_states = { + agent: tf2_utils.to_numpy_squeeze(_state) + for agent, _state in self._states.items() + } + if next_extras: + next_extras.update({"core_states": numpy_states}) + self._adder.add(policy, next_timestep, next_extras) # type: ignore + else: + self._adder.add(policy, next_timestep, numpy_states) # type: ignore From 8eea9710dc5ada582a6d0af70544d085487b008f Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Sun, 23 May 2021 09:25:59 +0200 Subject: [PATCH 036/188] example of running madqn on starcraft environment --- examples/smac/run_recurrent_madqn.py | 174 ++++++++++++++++++++++++++ mava/utils/environments/smac_utils.py | 19 +++ mava/wrappers/smac.py | 31 ++--- 3 files changed, 209 insertions(+), 15 deletions(-) create mode 100644 examples/smac/run_recurrent_madqn.py diff --git a/examples/smac/run_recurrent_madqn.py b/examples/smac/run_recurrent_madqn.py new file mode 100644 index 000000000..3d5d8d77f --- /dev/null +++ b/examples/smac/run_recurrent_madqn.py @@ -0,0 +1,174 @@ +# python3 +# Copyright 2021 [...placeholder...]. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Example running recurrent MADQN on multi-agent Starcraft 2 (SMAC) environment.""" + +import functools +from datetime import datetime +from typing import Any, Dict, Mapping, Optional, Sequence, Union + +import launchpad as lp +import sonnet as snt +import tensorflow as tf +from absl import app, flags +from acme import types +from acme.tf import networks +from launchpad.nodes.python.local_multi_processing import PythonProcess + +from mava import specs as mava_specs +from mava.components.tf.modules.exploration import LinearExplorationScheduler +from mava.components.tf.networks import epsilon_greedy_action_selector +from mava.systems.tf import madqn +from mava.systems.tf.madqn.execution import MADQNRecurrentExecutor +from mava.systems.tf.madqn.training import RecurrentMADQNTrainer +from mava.utils import lp_utils +from mava.utils.environments import smac_utils +from mava.utils.loggers import logger_utils + +FLAGS = flags.FLAGS +flags.DEFINE_string( + "map_name", + "3m", + "Starcraft 2 micromanagement map name (str).", +) + +flags.DEFINE_string( + "mava_id", + str(datetime.now()), + "Experiment identifier that can be used to continue experiments.", +) +flags.DEFINE_string("base_dir", "~/mava/", "Base dir to store experiments.") + + +def make_networks( + environment_spec: mava_specs.MAEnvironmentSpec, + q_networks_layer_sizes: Union[Dict[str, Sequence], Sequence] = ( + 512, + 512, + 256, + ), + shared_weights: bool = True, +) -> Mapping[str, types.TensorTransformation]: + """Creates networks used by the agents.""" + + specs = environment_spec.get_agent_specs() + + # Create agent_type specs + if shared_weights: + type_specs = {key.split("_")[0]: specs[key] for key in specs.keys()} + specs = type_specs + + if isinstance(q_networks_layer_sizes, Sequence): + q_networks_layer_sizes = {key: q_networks_layer_sizes for key in specs.keys()} + + def action_selector_fn( + q_values: types.NestedTensor, + legal_actions: types.NestedTensor, + epsilon: Optional[tf.Variable] = None, + ) -> types.NestedTensor: + return epsilon_greedy_action_selector( + action_values=q_values, legal_actions_mask=legal_actions, epsilon=epsilon + ) + + q_networks = {} + action_selectors = {} + for key in specs.keys(): + + # Get total number of action dimensions from action spec. + num_dimensions = specs[key].actions.num_values + + # Create the policy network. + q_network = snt.DeepRNN( + [ + networks.LayerNormMLP( + q_networks_layer_sizes[key], activate_final=False + ), + snt.LSTM(25), + snt.nets.MLP([128]), + networks.NearZeroInitializedLinear(num_dimensions), + ] + ) + + # epsilon greedy action selector + action_selector = action_selector_fn + + q_networks[key] = q_network + action_selectors[key] = action_selector + + return { + "q_networks": q_networks, + "action_selectors": action_selectors, + } + + +def main(_: Any) -> None: + + # environment + environment_factory = functools.partial( + smac_utils.make_environment, map_name=FLAGS.map_name + ) + + # networks + network_factory = lp_utils.partial_kwargs(make_networks) + + # Checkpointer appends "Checkpoints" to checkpoint_dir + checkpoint_dir = f"{FLAGS.base_dir}/{FLAGS.mava_id}" + + # loggers + log_every = 10 + logger_factory = functools.partial( + logger_utils.make_logger, + directory=FLAGS.base_dir, + to_terminal=True, + to_tensorboard=True, + time_stamp=FLAGS.mava_id, + time_delta=log_every, + ) + + # distributed program + program = madqn.MADQN( + environment_factory=environment_factory, + network_factory=network_factory, + logger_factory=logger_factory, + num_executors=2, + trainer_fn=RecurrentMADQNTrainer, + executor_fn=MADQNRecurrentExecutor, + exploration_scheduler_fn=LinearExplorationScheduler, + epsilon_min=0.05, + epsilon_decay=5e-4, + optimizer=snt.optimizers.Adam(learning_rate=1e-4), + checkpoint_subpath=checkpoint_dir, + n_step=1, + batch_size=32, + ).build() + + # launch + gpu_id = -1 + env_vars = {"CUDA_VISIBLE_DEVICES": str(gpu_id)} + local_resources = { + "trainer": [], + "evaluator": PythonProcess(env=env_vars), + "executor": PythonProcess(env=env_vars), + } + lp.launch( + program, + lp.LaunchType.LOCAL_MULTI_PROCESSING, + terminal="current_terminal", + local_resources=local_resources, + ) + + +if __name__ == "__main__": + app.run(main) diff --git a/mava/utils/environments/smac_utils.py b/mava/utils/environments/smac_utils.py index dddf84bca..7044c7b49 100644 --- a/mava/utils/environments/smac_utils.py +++ b/mava/utils/environments/smac_utils.py @@ -48,3 +48,22 @@ def make_environment( environment.seed(random_seed) return environment + + +class CloudpickleWrapper: + """ + Uses cloudpickle to serialize contents (otherwise multiprocessing tries to use pickle) + """ + + def __init__(self, x): + self.x = x + + def __getstate__(self): + import cloudpickle + + return cloudpickle.dumps(self.x) + + def __setstate__(self, ob): + import pickle + + self.x = pickle.loads(ob) \ No newline at end of file diff --git a/mava/wrappers/smac.py b/mava/wrappers/smac.py index 6314b36f0..2e6d58757 100644 --- a/mava/wrappers/smac.py +++ b/mava/wrappers/smac.py @@ -48,14 +48,17 @@ def __init__(self, environment: StarCraft2Env) -> None: self._environment = environment self._reset_next_step = True - self._agents: List = [] - self.observation_space = Dict( - { - "observation": Box(-1, 1, shape=(self._env.get_obs_size(),)), - "action_mask": Box(0, 1, shape=(self._env.get_total_actions(),)), - } + self._possible_agents = [ + f"agent_{i}" for i in range(self._environment.get_env_info()["n_agents"]) + ] + self._agents = self._possible_agents + self.observation_space = { + "observation": Box(-1, 1, shape=(self._environment.get_obs_size(),)), + "action_mask": Box(0, 1, shape=(self._environment.get_total_actions(),)), + } + self.action_space: Type[Discrete] = Discrete( + self._environment.get_total_actions() ) - self.action_space: Type[Discrete] = Discrete(self._env.get_total_actions()) def reset(self) -> Tuple[dm_env.TimeStep, np.array]: """Resets the env and returns observations from ready agents. @@ -67,18 +70,16 @@ def reset(self) -> Tuple[dm_env.TimeStep, np.array]: self._step_type = dm_env.StepType.FIRST # reset internal SC2 env - obs_list, state = self._env.reset() + obs_list, state = self._environment.reset() # Convert observations observe: Dict[str, np.ndarray] = {} - self._possible_agents = [] + for i, obs in enumerate(obs_list): - agent = f"agent_{i}" - observe[agent] = { + observe[f"agent_{i}"] = { "observation": obs, - "action_mask": np.array(self._env.get_avail_agent_actions(i)), + "action_mask": np.array(self._environment.get_avail_agent_actions(i)), } - self._possible_agents.append(agent) observations = self._convert_observations( observe, {agent: False for agent in self._possible_agents} ) @@ -145,7 +146,7 @@ def step(self, actions: Dict[str, np.ndarray]) -> Tuple[dm_env.TimeStep, np.arra agent = f"agent_{i}" observe[agent] = { "observation": obs, - "action_mask": np.array(self._env.get_avail_agent_actions(i)), + "action_mask": np.array(self._environment.get_avail_agent_actions(i)), } rewards[agent] = reward dones[agent] = terminated @@ -220,7 +221,7 @@ def observation_spec(self) -> types.Observation: def action_spec(self) -> Dict[str, specs.DiscreteArray]: return { - agent: _convert_to_spec(self._environment.action_space) + agent: _convert_to_spec(self.action_space) for agent in self._possible_agents } From 8b299af3f34817d0dab08a1c8addae420706413b Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Sun, 23 May 2021 09:36:16 +0200 Subject: [PATCH 037/188] fix mypy issues --- mava/utils/environments/smac_utils.py | 19 ------------------- mava/wrappers/smac.py | 4 ++-- 2 files changed, 2 insertions(+), 21 deletions(-) diff --git a/mava/utils/environments/smac_utils.py b/mava/utils/environments/smac_utils.py index 7044c7b49..dddf84bca 100644 --- a/mava/utils/environments/smac_utils.py +++ b/mava/utils/environments/smac_utils.py @@ -48,22 +48,3 @@ def make_environment( environment.seed(random_seed) return environment - - -class CloudpickleWrapper: - """ - Uses cloudpickle to serialize contents (otherwise multiprocessing tries to use pickle) - """ - - def __init__(self, x): - self.x = x - - def __getstate__(self): - import cloudpickle - - return cloudpickle.dumps(self.x) - - def __setstate__(self, ob): - import pickle - - self.x = pickle.loads(ob) \ No newline at end of file diff --git a/mava/wrappers/smac.py b/mava/wrappers/smac.py index 2e6d58757..4b5f6f188 100644 --- a/mava/wrappers/smac.py +++ b/mava/wrappers/smac.py @@ -84,7 +84,7 @@ def reset(self) -> Tuple[dm_env.TimeStep, np.array]: observe, {agent: False for agent in self._possible_agents} ) - self._agents = list(range(len(obs_list))) + self._agents = list(observe.keys()) # create discount spec discount_spec = self.discount_spec() @@ -152,7 +152,7 @@ def step(self, actions: Dict[str, np.ndarray]) -> Tuple[dm_env.TimeStep, np.arra dones[agent] = terminated observations = self._convert_observations(observe, dones) - self._agents = list(range(len(obs_list))) + self._agents = list(observe.keys()) rewards_spec = self.reward_spec() # Handle empty rewards From e0ef40d1989a03e4add0e524e54b229021493d7f Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Sun, 23 May 2021 09:45:46 +0200 Subject: [PATCH 038/188] update readme and workflow for sc2 --- .github/workflows/ci.yaml | 3 +++ README.md | 6 ++++++ 2 files changed, 9 insertions(+) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index fcd48ea70..cfb3453c4 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -36,6 +36,9 @@ jobs: AutoROM -v # Open CV and Headless screen. sudo apt-get install ffmpeg libsm6 libxext6 xvfb -y + # SC2 + sudo apt-get -y install git + pip install git+https://github.com/oxwhirl/smac.git - name: Format code run: | # Check code follows black formatting. diff --git a/README.md b/README.md index 545a6d45e..78c370d08 100644 --- a/README.md +++ b/README.md @@ -65,6 +65,12 @@ We have tested `mava` on Python 3.6, 3.7 and 3.8. ``` and navigate to `http://127.0.0.1:6006/`. +3. Install multi-agent Starcraft 2 environment [Optional]: + To install the environment, please run the provided bash script. + ```bash + chmod +x install_sc2.sh + ./install_sc2.sh + ### Python virtual environment 1. If not using docker, we strongly recommend using a From 7651e554ab74e3f9760c059f82f92483d3766887 Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Sun, 23 May 2021 10:07:14 +0200 Subject: [PATCH 039/188] update yaml for smac install --- .github/workflows/ci.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index cfb3453c4..98af25385 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -37,7 +37,7 @@ jobs: # Open CV and Headless screen. sudo apt-get install ffmpeg libsm6 libxext6 xvfb -y # SC2 - sudo apt-get -y install git + sudo apt-get install git -y pip install git+https://github.com/oxwhirl/smac.git - name: Format code run: | From df38243951321d067f0b6b609547e8a6860aad77 Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Sun, 23 May 2021 10:32:38 +0200 Subject: [PATCH 040/188] change versions of python to test to only include 3.7 and 3.8 --- .github/workflows/ci.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index 98af25385..ee3891406 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -12,7 +12,7 @@ jobs: timeout-minutes: 30 strategy: matrix: - python-version: [3.6.13, 3.7.1, 3.8.0] + python-version: [3.7.1, 3.8.0] #[3.6.13, 3.7.1, 3.8.0] steps: - uses: actions/checkout@v2 - name: Set up Python ${{ matrix.python-version }} From cc99ecfde8e6fca39302a546fd58305a1d8a54dd Mon Sep 17 00:00:00 2001 From: Dries Date: Sun, 23 May 2021 13:32:28 +0200 Subject: [PATCH 041/188] fix: Small fixes. --- mava/wrappers/smac.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mava/wrappers/smac.py b/mava/wrappers/smac.py index 4b5f6f188..790a0f73c 100644 --- a/mava/wrappers/smac.py +++ b/mava/wrappers/smac.py @@ -180,7 +180,7 @@ def step(self, actions: Dict[str, np.ndarray]) -> Tuple[dm_env.TimeStep, np.arra step_type=self._step_type, ) - return timestep, state + return timestep, {"s_t": state} def env_done(self) -> bool: """ From 1901a56666ed3eea238d5a055b560423695eb987 Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Sun, 23 May 2021 14:22:47 +0200 Subject: [PATCH 042/188] Minor changes --- .github/workflows/ci.yaml | 2 +- examples/smac/run_recurrent_madqn.py | 2 +- mava/utils/environments/smac_utils.py | 12 +++++++++++- mava/wrappers/smac.py | 7 +++++++ 4 files changed, 20 insertions(+), 3 deletions(-) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index ee3891406..98af25385 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -12,7 +12,7 @@ jobs: timeout-minutes: 30 strategy: matrix: - python-version: [3.7.1, 3.8.0] #[3.6.13, 3.7.1, 3.8.0] + python-version: [3.6.13, 3.7.1, 3.8.0] steps: - uses: actions/checkout@v2 - name: Set up Python ${{ matrix.python-version }} diff --git a/examples/smac/run_recurrent_madqn.py b/examples/smac/run_recurrent_madqn.py index 3d5d8d77f..049624851 100644 --- a/examples/smac/run_recurrent_madqn.py +++ b/examples/smac/run_recurrent_madqn.py @@ -142,7 +142,7 @@ def main(_: Any) -> None: environment_factory=environment_factory, network_factory=network_factory, logger_factory=logger_factory, - num_executors=2, + num_executors=1, trainer_fn=RecurrentMADQNTrainer, executor_fn=MADQNRecurrentExecutor, exploration_scheduler_fn=LinearExplorationScheduler, diff --git a/mava/utils/environments/smac_utils.py b/mava/utils/environments/smac_utils.py index dddf84bca..55f8780e3 100644 --- a/mava/utils/environments/smac_utils.py +++ b/mava/utils/environments/smac_utils.py @@ -15,7 +15,7 @@ """Starcraft 2 environment factory.""" -from typing import Any, Optional +from typing import Any, Optional, Dict import dm_env from smac.env import StarCraft2Env @@ -23,6 +23,16 @@ from mava.wrappers import SMACEnvWrapper +def load_smac_env(env_config: Dict[str, Any]) -> StarCraft2Env: + """Loads a smac environment given a config dict. Also, the possible agents in the + environment are set""" + + env = StarCraft2Env(**env_config) + env.possible_agents = list(range(env.n_agents)) + + return env + + def make_environment( evaluation: bool = False, map_name: str = "3m", diff --git a/mava/wrappers/smac.py b/mava/wrappers/smac.py index 4b5f6f188..4f12ab3e9 100644 --- a/mava/wrappers/smac.py +++ b/mava/wrappers/smac.py @@ -56,9 +56,13 @@ def __init__(self, environment: StarCraft2Env) -> None: "observation": Box(-1, 1, shape=(self._environment.get_obs_size(),)), "action_mask": Box(0, 1, shape=(self._environment.get_total_actions(),)), } + self.observation_spaces = { + agent: self.observation_space["observation"] for agent in self._agents + } self.action_space: Type[Discrete] = Discrete( self._environment.get_total_actions() ) + self.action_spaces = {agent: self.action_space for agent in self._agents} def reset(self) -> Tuple[dm_env.TimeStep, np.array]: """Resets the env and returns observations from ready agents. @@ -237,6 +241,9 @@ def discount_spec(self) -> Dict[str, specs.BoundedArray]: def extra_spec(self) -> Dict[str, specs.BoundedArray]: return {} + def seed(self, random_seed): + self._environment._seed = random_seed + @property def agents(self) -> List: return self._agents From 461f8102457446beea86c22d24a67c065715b1a8 Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Sun, 23 May 2021 14:50:03 +0200 Subject: [PATCH 043/188] test ci without setuptools --- .github/workflows/ci.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index 98af25385..b879ecdbe 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -25,7 +25,7 @@ jobs: # Ensure encoding consistency export LC_ALL=en_US.UTF-8 export LANG=en_US.UTF-8 - pip install --upgrade pip setuptools + # pip install --upgrade pip setuptools pip install . pip install .[flatland] pip install .[tf,envs,reverb,testing_formatting,launchpad,record_episode] From d00a0e99e484c06e48d3b74db605226b3f58509b Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Sun, 23 May 2021 15:03:36 +0200 Subject: [PATCH 044/188] : --- mava/utils/environments/smac_utils.py | 2 +- mava/wrappers/smac.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/mava/utils/environments/smac_utils.py b/mava/utils/environments/smac_utils.py index 55f8780e3..4db2fa554 100644 --- a/mava/utils/environments/smac_utils.py +++ b/mava/utils/environments/smac_utils.py @@ -15,7 +15,7 @@ """Starcraft 2 environment factory.""" -from typing import Any, Optional, Dict +from typing import Any, Dict, Optional import dm_env from smac.env import StarCraft2Env diff --git a/mava/wrappers/smac.py b/mava/wrappers/smac.py index eb716e640..b381fc82c 100644 --- a/mava/wrappers/smac.py +++ b/mava/wrappers/smac.py @@ -241,7 +241,7 @@ def discount_spec(self) -> Dict[str, specs.BoundedArray]: def extra_spec(self) -> Dict[str, specs.BoundedArray]: return {} - def seed(self, random_seed): + def seed(self, random_seed: int) -> None: self._environment._seed = random_seed @property From 3e70ef2c6f77cb7b2822dacc77fc6df6ba7045c2 Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Sun, 23 May 2021 15:06:32 +0200 Subject: [PATCH 045/188] small updates to ci and dockerfile --- .github/workflows/ci.yaml | 3 ++- Dockerfile | 2 +- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index b879ecdbe..80cd04420 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -21,11 +21,12 @@ jobs: python-version: ${{ matrix.python-version }} - name: Install dependencies run: | + pip install --upgrade pip setuptools export PIP_DEFAULT_TIMEOUT=100 # Ensure encoding consistency export LC_ALL=en_US.UTF-8 export LANG=en_US.UTF-8 - # pip install --upgrade pip setuptools + # Mava dependencies pip install . pip install .[flatland] pip install .[tf,envs,reverb,testing_formatting,launchpad,record_episode] diff --git a/Dockerfile b/Dockerfile index 8e2b6edda..57603cd0b 100755 --- a/Dockerfile +++ b/Dockerfile @@ -32,7 +32,7 @@ RUN python -m pip install --upgrade pip RUN python -m pip install -e .[flatland] RUN python -m pip install -e .[tf,envs,reverb,launchpad,testing_formatting,record_episode] -# install starcraft 2 environment +# Install starcraft 2 environment RUN apt-get -y install git RUN python -m pip install git+https://github.com/oxwhirl/smac.git ENV SC2PATH /home/app/mava/3rdparty/StarCraftII From 2ee7b8c8df89cf2d6d94e9b3c0e89851d1001f80 Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Sun, 23 May 2021 15:08:58 +0200 Subject: [PATCH 046/188] small update to ci --- .github/workflows/ci.yaml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index 80cd04420..f6b3909c8 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -21,12 +21,13 @@ jobs: python-version: ${{ matrix.python-version }} - name: Install dependencies run: | - pip install --upgrade pip setuptools export PIP_DEFAULT_TIMEOUT=100 # Ensure encoding consistency export LC_ALL=en_US.UTF-8 export LANG=en_US.UTF-8 # Mava dependencies + pip uninstall -y enum34 + pip install --upgrade pip setuptools pip install . pip install .[flatland] pip install .[tf,envs,reverb,testing_formatting,launchpad,record_episode] From 897d2c39dd8570c2a7781fef11965a3f3ecb3e4b Mon Sep 17 00:00:00 2001 From: Kale-ab Date: Sun, 23 May 2021 15:15:43 +0200 Subject: [PATCH 047/188] fix: Added baseline cost weighting param to critic loss calc. --- mava/systems/tf/mappo/training.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mava/systems/tf/mappo/training.py b/mava/systems/tf/mappo/training.py index fc134458d..e0af4f36f 100644 --- a/mava/systems/tf/mappo/training.py +++ b/mava/systems/tf/mappo/training.py @@ -290,7 +290,7 @@ def _forward(self, inputs: Any) -> None: # Do not use the loss provided by td_lambda as they sum the losses over # the sequence length rather than averaging them. - critic_loss = tf.reduce_mean( + critic_loss = self._baseline_cost * tf.reduce_mean( tf.square(td_lambda_extra.temporal_differences), name="CriticLoss" ) From 6260a2cfe1431994037420750efebe261ca3134f Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Sun, 23 May 2021 15:37:33 +0200 Subject: [PATCH 048/188] fix: small update to ci to fix checks failing --- .github/workflows/ci.yaml | 1 + .gitignore | 4 ++++ 2 files changed, 5 insertions(+) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index 8abedcba0..eab06c6c0 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -25,6 +25,7 @@ jobs: # Ensure encoding consistency export LC_ALL=en_US.UTF-8 export LANG=en_US.UTF-8 + pip uninstall -y enum34 pip install --upgrade pip setuptools pip install . pip install .[flatland] diff --git a/.gitignore b/.gitignore index 3b997dbf1..e4a189ead 100644 --- a/.gitignore +++ b/.gitignore @@ -33,6 +33,9 @@ MANIFEST *.manifest *.spec +# mac +__MACOSX + # Installer logs pip-log.txt pip-delete-this-directory.txt @@ -147,3 +150,4 @@ mava/workspace.code-workspace .DS_Store mava_testing .devcontainer/devcontainer.json +3rdparty From 15367a24d01c4000bb1457e1f4ded870fd67de9a Mon Sep 17 00:00:00 2001 From: Dries Date: Sun, 23 May 2021 17:01:54 +0200 Subject: [PATCH 049/188] fix: Fix legal_actions dtype. --- mava/wrappers/smac.py | 17 ++++++++++++++--- 1 file changed, 14 insertions(+), 3 deletions(-) diff --git a/mava/wrappers/smac.py b/mava/wrappers/smac.py index b381fc82c..46f7b6041 100644 --- a/mava/wrappers/smac.py +++ b/mava/wrappers/smac.py @@ -82,8 +82,11 @@ def reset(self) -> Tuple[dm_env.TimeStep, np.array]: for i, obs in enumerate(obs_list): observe[f"agent_{i}"] = { "observation": obs, - "action_mask": np.array(self._environment.get_avail_agent_actions(i)), + "action_mask": np.array( + self._environment.get_avail_agent_actions(i), dtype=np.float32 + ), } + observations = self._convert_observations( observe, {agent: False for agent in self._possible_agents} ) @@ -150,7 +153,9 @@ def step(self, actions: Dict[str, np.ndarray]) -> Tuple[dm_env.TimeStep, np.arra agent = f"agent_{i}" observe[agent] = { "observation": obs, - "action_mask": np.array(self._environment.get_avail_agent_actions(i)), + "action_mask": np.array( + self._environment.get_avail_agent_actions(i), dtype=np.float32 + ), } rewards[agent] = reward dones[agent] = terminated @@ -239,7 +244,13 @@ def discount_spec(self) -> Dict[str, specs.BoundedArray]: } def extra_spec(self) -> Dict[str, specs.BoundedArray]: - return {} + state = self._environment.get_state() + # TODO (dries): What should the real bounds be of the state spec? + return { + "s_t": specs.BoundedArray( + state.shape, np.float32, minimum=float("-inf"), maximum=float("inf") + ) + } def seed(self, random_seed: int) -> None: self._environment._seed = random_seed From 739a5463e78112a7b3731e85b11327861ffbf72f Mon Sep 17 00:00:00 2001 From: Kale-ab Date: Sun, 23 May 2021 21:39:31 +0200 Subject: [PATCH 050/188] fix: Passing none values to target update rate and passing these params to mad4pg. --- mava/systems/tf/mad4pg/system.py | 4 ++++ mava/systems/tf/maddpg/builder.py | 2 +- mava/systems/tf/maddpg/system.py | 4 ++-- mava/systems/tf/maddpg/training.py | 5 +++-- 4 files changed, 10 insertions(+), 5 deletions(-) diff --git a/mava/systems/tf/mad4pg/system.py b/mava/systems/tf/mad4pg/system.py index 60a3c65d8..e5aa84b40 100644 --- a/mava/systems/tf/mad4pg/system.py +++ b/mava/systems/tf/mad4pg/system.py @@ -58,7 +58,9 @@ def __init__( discount: float = 0.99, batch_size: int = 256, prefetch_size: int = 4, + target_averaging: bool = False, target_update_period: int = 100, + target_update_rate: float = None, executor_variable_update_period: int = 1000, min_replay_size: int = 1000, max_replay_size: int = 1000000, @@ -151,4 +153,6 @@ def __init__( eval_loop_fn=eval_loop_fn, train_loop_fn_kwargs=train_loop_fn_kwargs, eval_loop_fn_kwargs=eval_loop_fn_kwargs, + target_averaging=target_averaging, + target_update_rate=target_update_rate, ) diff --git a/mava/systems/tf/maddpg/builder.py b/mava/systems/tf/maddpg/builder.py index 42fea1d5c..1fd2a33d7 100644 --- a/mava/systems/tf/maddpg/builder.py +++ b/mava/systems/tf/maddpg/builder.py @@ -74,7 +74,7 @@ class MADDPGConfig: prefetch_size: int = 4 target_averaging: bool = False target_update_period: int = 100 - target_update_rate: float = 0.01 + target_update_rate: Optional[float] = None executor_variable_update_period: int = 1000 min_replay_size: int = 1000 max_replay_size: int = 1000000 diff --git a/mava/systems/tf/maddpg/system.py b/mava/systems/tf/maddpg/system.py index e41ac062e..95f2aea46 100644 --- a/mava/systems/tf/maddpg/system.py +++ b/mava/systems/tf/maddpg/system.py @@ -15,7 +15,7 @@ """MADDPG system implementation.""" import functools -from typing import Any, Callable, Dict, List, Type, Union +from typing import Any, Callable, Dict, List, Optional, Type, Union import acme import dm_env @@ -70,7 +70,7 @@ def __init__( prefetch_size: int = 4, target_averaging: bool = False, target_update_period: int = 100, - target_update_rate: float = 0.01, + target_update_rate: Optional[float] = None, executor_variable_update_period: int = 1000, min_replay_size: int = 1000, max_replay_size: int = 1000000, diff --git a/mava/systems/tf/maddpg/training.py b/mava/systems/tf/maddpg/training.py index 11fb35aa2..f1a35b6f3 100644 --- a/mava/systems/tf/maddpg/training.py +++ b/mava/systems/tf/maddpg/training.py @@ -198,7 +198,6 @@ def __init__( self._timestamp: Optional[float] = None def _update_target_networks(self) -> None: - assert 0.0 < self._target_update_rate < 1.0 for key in self.unique_net_keys: # Update target network. online_variables = ( @@ -213,6 +212,7 @@ def _update_target_networks(self) -> None: ) if self._target_averaging: + assert 0.0 < self._target_update_rate < 1.0 tau = self._target_update_rate for src, dest in zip(online_variables, target_variables): dest.assign(dest * (1.0 - tau) + src * tau) @@ -1029,7 +1029,7 @@ def __init__( self._timestamp: Optional[float] = None def _update_target_networks(self) -> None: - assert 0.0 < self._target_update_rate < 1.0 + for key in self.unique_net_keys: # Update target network. online_variables = ( @@ -1044,6 +1044,7 @@ def _update_target_networks(self) -> None: ) if self._target_averaging: + assert 0.0 < self._target_update_rate < 1.0 tau = self._target_update_rate for src, dest in zip(online_variables, target_variables): dest.assign(dest * (1.0 - tau) + src * tau) From c1d16c71d80ee1b04bdeff003ebbf05b0d73f2ea Mon Sep 17 00:00:00 2001 From: Kale-ab Date: Sun, 23 May 2021 21:46:55 +0200 Subject: [PATCH 051/188] fix: made type explicit. --- mava/systems/tf/mad4pg/system.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/mava/systems/tf/mad4pg/system.py b/mava/systems/tf/mad4pg/system.py index e5aa84b40..8df5e5447 100644 --- a/mava/systems/tf/mad4pg/system.py +++ b/mava/systems/tf/mad4pg/system.py @@ -14,7 +14,7 @@ # limitations under the License. """MAD4PG system implementation.""" -from typing import Callable, Dict, Type, Union +from typing import Callable, Dict, Optional, Type, Union import dm_env import sonnet as snt @@ -60,7 +60,7 @@ def __init__( prefetch_size: int = 4, target_averaging: bool = False, target_update_period: int = 100, - target_update_rate: float = None, + target_update_rate: Optional[float] = None, executor_variable_update_period: int = 1000, min_replay_size: int = 1000, max_replay_size: int = 1000000, From 3df2ab0715a66fede092c51233d4ce78aa771bcb Mon Sep 17 00:00:00 2001 From: Arnu Pretorius Date: Mon, 24 May 2021 08:15:45 +0200 Subject: [PATCH 052/188] Update install_sc2.sh --- install_sc2.sh | 1 + 1 file changed, 1 insertion(+) diff --git a/install_sc2.sh b/install_sc2.sh index b7fa1ff2d..f3a4f306b 100755 --- a/install_sc2.sh +++ b/install_sc2.sh @@ -1,5 +1,6 @@ #!/bin/bash # Install SC2 and add the custom maps +# Script adapted from https://github.com/oxwhirl/pymarl mkdir 3rdparty cd 3rdparty From 39bd994fb3d4118f51e94d3e09c68ea9bfa2aee9 Mon Sep 17 00:00:00 2001 From: Arnu Pretorius Date: Mon, 24 May 2021 08:20:15 +0200 Subject: [PATCH 053/188] Remove commented lines of code in wrapper --- mava/wrappers/smac.py | 12 ------------ 1 file changed, 12 deletions(-) diff --git a/mava/wrappers/smac.py b/mava/wrappers/smac.py index 46f7b6041..104831b0f 100644 --- a/mava/wrappers/smac.py +++ b/mava/wrappers/smac.py @@ -128,18 +128,6 @@ def step(self, actions: Dict[str, np.ndarray]) -> Tuple[dm_env.TimeStep, np.arra if self._reset_next_step: return self.reset() - # actions = [] - # for i in self._ready_agents: - # if i not in action_dict: - # raise ValueError(f"You must supply an action for agent: {i}") - # actions.append(action_dict[i]) - - # if len(actions) != len(self._ready_agents): - # raise ValueError( - # f"Number of actions ({len(actions)}) does not match number \ - # of ready agents (len(self._ready_agents))." - # ) - actions_feed = list(actions.values()) reward, terminated, info = self._environment.step(actions_feed) obs_list = self._environment.get_obs() From 659f08b6117268b21786ca29ae38bb70ba0fdfd4 Mon Sep 17 00:00:00 2001 From: Arnu Pretorius Date: Mon, 24 May 2021 10:21:32 +0200 Subject: [PATCH 054/188] Remove chmod in instructions for sc2 install --- README.md | 1 - 1 file changed, 1 deletion(-) diff --git a/README.md b/README.md index 78c370d08..ab5cdbd0d 100644 --- a/README.md +++ b/README.md @@ -68,7 +68,6 @@ We have tested `mava` on Python 3.6, 3.7 and 3.8. 3. Install multi-agent Starcraft 2 environment [Optional]: To install the environment, please run the provided bash script. ```bash - chmod +x install_sc2.sh ./install_sc2.sh ### Python virtual environment From 3c9be70263a492c4d0bfe7b185695a456c1d5bbf Mon Sep 17 00:00:00 2001 From: Dries Date: Mon, 24 May 2021 11:09:45 +0200 Subject: [PATCH 055/188] feature: Add state-based maddpg example. --- examples/README.md | 73 +++++-- .../run_decentralised_recurrent_maddpg.py | 2 +- .../run_state_based_recurrent_maddpg.py | 188 ++++++++++++++++++ .../run_decentralised_recurrent_maddpg.py | 4 +- .../tf/architectures/state_based.py | 2 +- mava/systems/tf/maddpg/training.py | 20 +- 6 files changed, 258 insertions(+), 31 deletions(-) create mode 100644 examples/debugging_envs/run_state_based_recurrent_maddpg.py diff --git a/examples/README.md b/examples/README.md index 327601a6a..b48f814dd 100644 --- a/examples/README.md +++ b/examples/README.md @@ -9,38 +9,69 @@ In Mava we use a variety of different environments which include the ## Continuous control We include a number of systems running on continuous control tasks. -- [PPO (centralised)](debugging_envs/run_centralised_feedforward_mappo.py): - a PPO system using a centralised critic on the continuous action space simple_spread MPE debugging environment. -- [MA-DDPG (feedforward)](debugging_envs/run_feedforward_maddpg.py): - an MA-DDPG system (without recurrence) on the continuous action space simple_spread MPE debugging environment. -- [MA-D4PG](debugging_envs/run_feedforward_mad4pg.py): +- [MA-DDPG (centralised, feedforward)](debugging_envs/run_centralised_feedforward_maddpg.py): + an MA-DDPG system on the continuous action space simple_spread MPE debugging environment. +- [PPO (centralised, feedforward)](debugging_envs/run_centralised_feedforward_mappo.py): + a PPO system on the continuous action space simple_spread MPE debugging environment. +- [MA-D4PG (decentralised, feedforward)](debugging_envs/run_decentralised_feedforward_mad4pg.py): an MA-D4PG system on the continuous action space simple_spread MPE debugging environment. -- [MA-DDPG (recurrent)](debugging_envs/run_recurrent_maddpg.py): - an MA-DDPG system (with recurrence) on the continuous action space simple_spread MPE debugging environment. +- [MA-DDPG (decentralised, feedforward)](debugging_envs/run_decentralised_feedforward_maddpg.py): + an MA-DDPG system on the continuous action space simple_spread MPE debugging environment. +- [PPO (decentralised, feedforward)](debugging_envs/run_decentralised_feedforward_mappo.py): + a PPO system on the continuous action space simple_spread MPE debugging environment. - [MA-D4PG (decentralised, recurrent)](debugging_envs/run_decentralised_recurrent_mad4pg.py): - an MA-D4PG system (with recurrence) on the continuous action space simple_spread MPE debugging environment. -- [PPO (centralised, feedforward)](petting_zoo/run_centralised_feedforward_mappo.py): + an MA-D4PG system on the continuous action space simple_spread MPE debugging environment. +- [MA-DDPG (decentralised, recurrent)](debugging_envs/run_decentralised_recurrent_maddpg.py): + an MA-DDPG system on the continuous action space simple_spread MPE debugging environment. +- [MA-DDPG (decentralised, recurrent)](debugging_envs/run_decentralised_recurrent_maddpg.py): + an MA-DDPG system on the continuous action space simple_spread MPE debugging environment. +- [MA-DDPG (decentralised, feedforward)](debugging_envs/run_feedforward_maddpg_record_video.py): + an MA-DDPG system (with video wrapping) on the continuous action space simple_spread MPE debugging environment. +- [MA-DDPG (networked, feedforward)](debugging_envs/run_networked_feedforward_maddpg.py): + an MA-D4PG system on the continuous action space simple_spread MPE debugging environment. + +- [MA-DDPG (state-based, recurrent)](debugging_envs/run_state_based_recurrent_maddpg.py): + an MA-D4PG system on the continuous action space simple_spread MPE debugging environment. +- [PPO (centralised, feedforward)](petting_zoo/run_centralised_feedforward_mappo.py): a PPO system using a centralised critic on the continuous action space multiwalker_v7 PettingZoo environment. -- [MA-DDPG (centralised)](petting_zoo/run_feedforward_maddpg.py): +- [MA-D4PG (decentralised, feedforward)](petting_zoo/run_decentralised_feedforward_mad4pg.py): + an MA-D4PG system on the continuous action space multiwalker_v7 PettingZoo environment. +- [MA-DDPG (decentralised, feedforward)](petting_zoo/run_decentralised_feedforward_maddpg.py): + an MA-DDPG system on the continuous action space multiwalker_v7 PettingZoo environment. +- [MA-DDPG (decentralised, recurrent)](petting_zoo/run_decentralised_recurrent_maddpg.py): an MA-DDPG system on the continuous action space multiwalker_v7 PettingZoo environment. -- [MA-DDPG (recurrent)](petting_zoo/run_recurrent_maddpg.py): - an MA-DDPG system (with recurrence) on the continuous action space multiwalker_v7 PettingZoo environment. +- [MA-DDPG (decentralised, feedforward)](petting_zoo/run_feedforward_maddpg_record_video.py): + an MA-DDPG system (with video wrapping) on the continuous action space multiwalker_v7 PettingZoo environment. [petting_zoo]: https://github.com/PettingZoo-Team/PettingZoo ## Discrete control We also include a number of systems running on discrete action space environments. - -- [MA-DQN (feedforward)](debugging_envs/run_feedforward_madqn.py): +- [MA-D4PG (decentralised, feedforward)](debugging_envs/run_decentralised_feedforward_discrete_mad4pg.py): + an MA-D4PG system on the discrete action space simple_spread MPE debugging environment. +- [MA-DDPG (decentralised, feedforward)](debugging_envs/run_decentralised_feedforward_discrete_maddpg.py): + an MA-DDPG system on the discrete action space simple_spread MPE debugging environment. +- [MA-DQN (feedforward)](debugging_envs/run_feedforward_madqn.py): an MA-DQN system running on the discrete action space simple_spread MPE debugging environment. -- [PPO](debugging_envs/run_feedforward_mappo.py): - a PPO system using a decentralised critic on the discrete action space simple_spread MPE debugging environment. -- [QMIX](debugging_envs/run_feedforward_qmix.py): +- [MA-DQN (feedforward)](debugging_envs/run_feedforward_madqn.py): + an MA-DQN system (with fingerprints) running on the discrete action space simple_spread MPE debugging environment. +- [QMIX](debugging_envs/run_feedforward_qmix.py): a QMIX system on the two step custom environment. -- [DIAL](debugging_envs/run_recurrent_dial.py): +- [VDN](debugging_envs/run_feedforward_vdn.py): + a VDN system on the two step custom environment. +- [DIAL](debugging_envs/run_recurrent_dial.py): a DIAL system on the custom SwitchGame environment. -- [DIAL](debugging_envs/run_recurrent_dial_spread.py): +- [DIAL](debugging_envs/run_recurrent_dial_spread.py): a DIAL system on the discrete action space simple_spread MPE debugging environment. -- [MA-DQN (feedforward)](debugging_envs/run_feedforward_madqn.py): - an MA-DQN system running on the discrete action space maze_craze_v2 PettingZoo environment. +- [MA-DQN (recurrent)](debugging_envs/run_recurrent_madqn.py): + an MA-DQN system running on the discrete action space simple_spread MPE debugging environment. +- [MA-DQN (feedforward)](petting_zoo/run_feedforward_madqn.py): + an MA-DQN system running on the continuous action space multiwalker_v7 PettingZoo environment. +- [MA-DQN (feedforward)](petting_zoo/run_feedforward_madqn.py): + an MA-DQN system (with fingerprints) running on the continuous action space multiwalker_v7 PettingZoo environment. +- [QMIX](petting_zoo/run_feedforward_qmix.py): + a QMIX system on the two step custom environment. +- [VDN](petting_zoo/run_feedforward_vdn.py): + a VDN system on the two step custom environment. + diff --git a/examples/debugging_envs/run_decentralised_recurrent_maddpg.py b/examples/debugging_envs/run_decentralised_recurrent_maddpg.py index 44c5308f6..b591fd67e 100644 --- a/examples/debugging_envs/run_decentralised_recurrent_maddpg.py +++ b/examples/debugging_envs/run_decentralised_recurrent_maddpg.py @@ -29,7 +29,7 @@ from mava import specs as mava_specs from mava.systems.tf import maddpg -from mava.systems.tf.mad4pg.execution import MADDPGRecurrentExecutor +from mava.systems.tf.maddpg.execution import MADDPGRecurrentExecutor from mava.systems.tf.maddpg.training import DecentralisedRecurrentMADDPGTrainer from mava.utils import lp_utils from mava.utils.environments import debugging_utils diff --git a/examples/debugging_envs/run_state_based_recurrent_maddpg.py b/examples/debugging_envs/run_state_based_recurrent_maddpg.py new file mode 100644 index 000000000..419749d4f --- /dev/null +++ b/examples/debugging_envs/run_state_based_recurrent_maddpg.py @@ -0,0 +1,188 @@ +# python3 +# Copyright 2021 [...placeholder...]. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Example running recurrent MADDPG on the debug MPE environments.""" +import functools +from datetime import datetime +from typing import Any, Dict, Mapping, Sequence, Union + +import launchpad as lp +import numpy as np +import sonnet as snt +from absl import app, flags +from acme import types +from acme.tf import networks +from acme.tf import utils as tf2_utils +from launchpad.nodes.python.local_multi_processing import PythonProcess + +from mava import specs as mava_specs +from mava.systems.tf import maddpg +from mava.systems.tf.maddpg.execution import MADDPGRecurrentExecutor +from mava.components.tf.architectures import StateBasedQValueCritic +from mava.systems.tf.maddpg.training import StateBasedRecurrentMADDPGTrainer +from mava.utils import lp_utils +from mava.utils.environments import debugging_utils +from mava.utils.loggers import logger_utils + +FLAGS = flags.FLAGS +flags.DEFINE_string( + "env_name", + "simple_spread", + "Debugging environment name (str).", +) +flags.DEFINE_string( + "action_space", + "continuous", + "Environment action space type (str).", +) +flags.DEFINE_string( + "mava_id", + str(datetime.now()), + "Experiment identifier that can be used to continue experiments.", +) +flags.DEFINE_string("base_dir", "~/mava/", "Base dir to store experiments.") + + +def make_networks( + environment_spec: mava_specs.MAEnvironmentSpec, + policy_networks_layer_sizes: Union[Dict[str, Sequence], Sequence] = ( + 256, + 256, + 256, + ), + critic_networks_layer_sizes: Union[Dict[str, Sequence], Sequence] = (512, 512, 256), + shared_weights: bool = True, + sigma: float = 0.3, +) -> Mapping[str, types.TensorTransformation]: + """Creates networks used by the agents.""" + specs = environment_spec.get_agent_specs() + + # Create agent_type specs + if shared_weights: + type_specs = {key.split("_")[0]: specs[key] for key in specs.keys()} + specs = type_specs + + if isinstance(policy_networks_layer_sizes, Sequence): + policy_networks_layer_sizes = { + key: policy_networks_layer_sizes for key in specs.keys() + } + if isinstance(critic_networks_layer_sizes, Sequence): + critic_networks_layer_sizes = { + key: critic_networks_layer_sizes for key in specs.keys() + } + + observation_networks = {} + policy_networks = {} + critic_networks = {} + for key in specs.keys(): + + # Get total number of action dimensions from action spec. + num_dimensions = np.prod(specs[key].actions.shape, dtype=int) + + # Create the observation network. + observation_network = tf2_utils.to_sonnet_module(tf2_utils.batch_concat) + + # Create the policy network. + policy_network = snt.DeepRNN( + [ + observation_network, + snt.Flatten(), + snt.nets.MLP(policy_networks_layer_sizes[key]), + snt.LSTM(25), + snt.nets.MLP([128]), + networks.NearZeroInitializedLinear(num_dimensions), + networks.TanhToSpec(specs[key].actions), + networks.ClippedGaussian(sigma), + networks.ClipToSpec(specs[key].actions), + ] + ) + + # Create the critic network. + critic_network = snt.Sequential( + [ + # The multiplexer concatenates the observations/actions. + networks.CriticMultiplexer(), + networks.LayerNormMLP( + critic_networks_layer_sizes[key], activate_final=False + ), + snt.Linear(1), + ] + ) + + observation_networks[key] = observation_network + policy_networks[key] = policy_network + critic_networks[key] = critic_network + + return { + "observations": observation_networks, + "policies": policy_networks, + "critics": critic_networks, + } + + +def main(_: Any) -> None: + # environment + environment_factory = functools.partial( + debugging_utils.make_environment, + env_name=FLAGS.env_name, + action_space=FLAGS.action_space, + num_agents=3, + return_state_info=True, + ) + + network_factory = lp_utils.partial_kwargs(make_networks) + + # Checkpointer appends "Checkpoints" to checkpoint_dir + checkpoint_dir = f"{FLAGS.base_dir}/{FLAGS.mava_id}" + + # loggers + log_every = 10 + logger_factory = functools.partial( + logger_utils.make_logger, + directory=FLAGS.base_dir, + to_terminal=True, + to_tensorboard=True, + time_stamp=FLAGS.mava_id, + time_delta=log_every, + ) + + program = maddpg.MADDPG( + environment_factory=environment_factory, + network_factory=network_factory, + logger_factory=logger_factory, + num_executors=2, + architecture=StateBasedQValueCritic, + trainer_fn=StateBasedRecurrentMADDPGTrainer, + executor_fn=MADDPGRecurrentExecutor, + checkpoint_subpath=checkpoint_dir, + ).build() + + # launch + gpu_id = -1 + env_vars = {"CUDA_VISIBLE_DEVICES": str(gpu_id)} + local_resources = { + "trainer": [], + "evaluator": PythonProcess(env=env_vars), + "executor": PythonProcess(env=env_vars), + } + lp.launch( + program, + lp.LaunchType.LOCAL_MULTI_PROCESSING, + terminal="current_terminal", + local_resources=local_resources, + ) + +if __name__ == "__main__": + app.run(main) diff --git a/examples/petting_zoo/run_decentralised_recurrent_maddpg.py b/examples/petting_zoo/run_decentralised_recurrent_maddpg.py index 2f6e7f046..de588cad6 100644 --- a/examples/petting_zoo/run_decentralised_recurrent_maddpg.py +++ b/examples/petting_zoo/run_decentralised_recurrent_maddpg.py @@ -31,7 +31,7 @@ from mava import specs as mava_specs from mava.systems.tf import maddpg from mava.systems.tf.maddpg.execution import MADDPGRecurrentExecutor -from mava.systems.tf.maddpg.training import DecentralisedRecurrentMADDPGTrainer +from mava.systems.tf.maddpg.training import StateBasedRecurrentMADDPGTrainer from mava.utils import lp_utils from mava.utils.environments import pettingzoo_utils from mava.utils.loggers import logger_utils @@ -163,7 +163,7 @@ def main(_: Any) -> None: network_factory=network_factory, logger_factory=logger_factory, num_executors=2, - trainer_fn=DecentralisedRecurrentMADDPGTrainer, + trainer_fn=StateBasedRecurrentMADDPGTrainer, executor_fn=MADDPGRecurrentExecutor, policy_optimizer=snt.optimizers.Adam(learning_rate=1e-4), critic_optimizer=snt.optimizers.Adam(learning_rate=1e-4), diff --git a/mava/components/tf/architectures/state_based.py b/mava/components/tf/architectures/state_based.py index eec6172a5..d99b16cb5 100644 --- a/mava/components/tf/architectures/state_based.py +++ b/mava/components/tf/architectures/state_based.py @@ -98,7 +98,7 @@ def _get_critic_specs( # Create one critic per agent. Each critic gets # absolute state information of the environment. - critic_state_shape = self._env_spec.get_extra_specs()["env_state"].shape + critic_state_shape = self._env_spec.get_extra_specs()["s_t"].shape critic_obs_spec = tf.TensorSpec( shape=critic_state_shape, dtype=tf.dtypes.float32, diff --git a/mava/systems/tf/maddpg/training.py b/mava/systems/tf/maddpg/training.py index 11fb35aa2..e23447097 100644 --- a/mava/systems/tf/maddpg/training.py +++ b/mava/systems/tf/maddpg/training.py @@ -848,8 +848,8 @@ def _get_critic_feed( agent: str, ) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor, tf.Tensor]: # State based - o_tm1_feed = e_tm1["env_state"] - o_t_feed = e_t["env_state"] + o_tm1_feed = e_tm1["s_t"] + o_t_feed = e_t["s_t"] a_tm1_feed = tf.stack([x for x in a_tm1.values()], 1) a_t_feed = tf.stack([x for x in a_t.values()], 1) return o_tm1_feed, o_t_feed, a_tm1_feed, a_t_feed @@ -1268,7 +1268,8 @@ def _forward(self, inputs: Any) -> None: # Get dpg Q values. obs_comb, _ = train_utils.combine_dim(target_obs_trans_feed) act_comb, _ = train_utils.combine_dim(dpg_actions_feed) - dpg_q_values = self._critic_networks[agent_key](obs_comb, act_comb) + dpg_actions_comb, _ = train_utils.combine_dim(dpg_actions) + dpg_q_values = tf.squeeze(self._critic_networks[agent_key](obs_comb, act_comb)) # Actor loss. If clipping is true use dqda clipping and clip the norm. # dpg_q_values = tf.squeeze(dpg_q_values, axis=-1) # [B] @@ -1276,14 +1277,21 @@ def _forward(self, inputs: Any) -> None: dqda_clipping = 1.0 if self._max_gradient_norm is not None else None clip_norm = True if self._max_gradient_norm is not None else False + print("dpg_q_values: ", dpg_q_values.shape) + print("dpg_actions_comb: ", dpg_actions_comb.shape) + policy_loss = losses.dpg( dpg_q_values, - act_comb, + dpg_actions_comb, tape=tape, dqda_clipping=dqda_clipping, clip_norm=clip_norm, ) + print("policy_loss: ", policy_loss.shape) self.policy_losses[agent] = tf.reduce_mean(policy_loss, axis=0) + print("policy_loss reduced: ", self.policy_losses[agent].shape) + exit() + self.tape = tape # Backward pass that calculates gradients and updates network. @@ -1641,8 +1649,8 @@ def _get_critic_feed( ) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor, tf.Tensor]: # State based - obs_trans_feed = extras["env_state"] - target_obs_trans_feed = extras["env_state"] + obs_trans_feed = extras["s_t"] + target_obs_trans_feed = extras["s_t"] action_feed = tf.stack([x for x in actions.values()], -1) target_actions_feed = tf.stack([x for x in target_actions.values()], -1) return obs_trans_feed, target_obs_trans_feed, action_feed, target_actions_feed From 8b955c0f6127289c2a3d465ced9fff057a2b9e37 Mon Sep 17 00:00:00 2001 From: Kevin Eloff Date: Mon, 24 May 2021 11:47:04 +0200 Subject: [PATCH 056/188] feat: loss masking --- examples/debugging_envs/run_recurrent_dial.py | 6 ++---- mava/systems/tf/dial/__init__.py | 4 ++-- mava/systems/tf/dial/execution.py | 11 ++-------- mava/systems/tf/dial/system.py | 4 ++-- mava/systems/tf/dial/training.py | 21 ++++++++++++------- 5 files changed, 22 insertions(+), 24 deletions(-) diff --git a/examples/debugging_envs/run_recurrent_dial.py b/examples/debugging_envs/run_recurrent_dial.py index e415bce53..72f593e0f 100644 --- a/examples/debugging_envs/run_recurrent_dial.py +++ b/examples/debugging_envs/run_recurrent_dial.py @@ -35,8 +35,6 @@ epsilon_greedy_action_selector, ) from mava.systems.tf import dial -from mava.systems.tf.dial.execution import DIALExecutor -from mava.systems.tf.dial.training import DIALTrainer # from mava.systems.tf.madqn.execution import MADQNRecurrentCommExecutor # from mava.systems.tf.madqn.training import RecurrentCommMADQNTrainer @@ -162,8 +160,8 @@ def main(_: Any) -> None: network_factory=network_factory, logger_factory=logger_factory, num_executors=2, - trainer_fn=DIALTrainer, - executor_fn=DIALExecutor, + trainer_fn=dial.DIALSwitchTrainer, + executor_fn=dial.DIALSwitchExecutor, # trainer_fn=RecurrentCommMADQNTrainer, # executor_fn=MADQNRecurrentCommExecutor, exploration_scheduler_fn=LinearExplorationScheduler, diff --git a/mava/systems/tf/dial/__init__.py b/mava/systems/tf/dial/__init__.py index 9a55ca0fb..dbfea7e80 100644 --- a/mava/systems/tf/dial/__init__.py +++ b/mava/systems/tf/dial/__init__.py @@ -13,6 +13,6 @@ # See the License for the specific language governing permissions and # limitations under the License. -from mava.systems.tf.dial.execution import DIALExecutor +from mava.systems.tf.dial.execution import DIALSwitchExecutor from mava.systems.tf.dial.system import DIAL -from mava.systems.tf.dial.training import DIALTrainer +from mava.systems.tf.dial.training import DIALSwitchTrainer diff --git a/mava/systems/tf/dial/execution.py b/mava/systems/tf/dial/execution.py index ce68e973d..65ff18242 100644 --- a/mava/systems/tf/dial/execution.py +++ b/mava/systems/tf/dial/execution.py @@ -13,14 +13,6 @@ # See the License for the specific language governing permissions and # limitations under the License. -# TODO (Kevin): implement DIAL executor (if required) -# Helper resources -# - single agent generic actors in acme: -# https://github.com/deepmind/acme/blob/master/acme/agents/tf/actors.py -# - single agent custom actor for Impala in acme: -# https://github.com/deepmind/acme/blob/master/acme/agents/tf/impala/acting.py -# - multi-agent generic executors in mava: mava/systems/tf/executors.py - """DIAL executor implementation.""" from typing import Any, Dict, Optional @@ -35,12 +27,13 @@ from mava.systems.tf.madqn.training import MADQNTrainer -class DIALExecutor(MADQNRecurrentCommExecutor): +class DIALSwitchExecutor(MADQNRecurrentCommExecutor): """DIAL executor. An executor based on a recurrent communicating policy for each agent in the system which takes non-batched observations and outputs non-batched actions. It also allows adding experiences to replay and updating the weights from the policy on the learner. + Note: this executor is specific to switch game env. """ def __init__( diff --git a/mava/systems/tf/dial/system.py b/mava/systems/tf/dial/system.py index a6bb1b60b..3009b2147 100644 --- a/mava/systems/tf/dial/system.py +++ b/mava/systems/tf/dial/system.py @@ -13,7 +13,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -"""Defines the MADQN system class.""" +"""Defines the DIAL system class.""" import functools from typing import Any, Callable, Dict, Optional, Type @@ -115,7 +115,7 @@ def __init__( target_update_period: int = 100, executor_variable_update_period: int = 1000, max_executor_steps: int = None, - checkpoint: bool = True, + checkpoint: bool = False, checkpoint_subpath: str = "~/mava/", logger_config: Dict = {}, train_loop_fn: Callable = ParallelEnvironmentLoop, diff --git a/mava/systems/tf/dial/training.py b/mava/systems/tf/dial/training.py index d8f29ae3d..59d5de581 100644 --- a/mava/systems/tf/dial/training.py +++ b/mava/systems/tf/dial/training.py @@ -33,10 +33,11 @@ from mava.systems.tf.madqn.training import RecurrentCommMADQNTrainer -class DIALTrainer(RecurrentCommMADQNTrainer): - """Recurrent Comm DIAL trainer. - This is the trainer component of a MADQN system. IE it takes a dataset as input +class DIALSwitchTrainer(RecurrentCommMADQNTrainer): + """Recurrent Comm DIAL Switch trainer. + This is the trainer component of a DIAL system. IE it takes a dataset as input and implements update functionality to learn from this dataset. + Note: this trainer is specific to switch game env. """ def __init__( @@ -94,14 +95,17 @@ def _forward(self, inputs: Any) -> None: core_message = tree.map_structure( lambda s: s[:, 0, :], inputs.data.extras["core_messages"] ) + T = actions[self._agents[0]].shape[0] + + # Use fact that end of episode always has the reward to + # find episode lengths. This is used to mask loss. + ep_end = tf.argmax(tf.math.abs(rewards[self._agents[0]]), axis=0) with tf.GradientTape(persistent=True) as tape: q_network_losses: Dict[str, NestedArray] = { agent: {"q_value_loss": tf.zeros(())} for agent in self._agents } - T = actions[self._agents[0]].shape[0] - state = {agent: core_state[agent][0] for agent in self._agents} target_state = {agent: core_state[agent][0] for agent in self._agents} @@ -139,7 +143,7 @@ def _forward(self, inputs: Any) -> None: target_state[agent], target_channel[agent], ) - print(m.shape) + target_state[agent] = s target_message[agent] = tf.math.multiply( m, observations[agent].observation[t][:, :1] @@ -150,7 +154,7 @@ def _forward(self, inputs: Any) -> None: state[agent], channel[agent], ) - print("m2 ", m.shape) + state[agent] = s message[agent] = tf.math.multiply( m, observations[agent].observation[t - 1][:, :1] @@ -162,8 +166,11 @@ def _forward(self, inputs: Any) -> None: rewards[agent][t - 1], discount * discounts[agent][t], q_targ, + # q_targ*(ep_end <= t), # mask last episode ) + # Index loss (mask ended episodes) + loss = tf.reduce_mean(loss[ep_end <= t]) loss = tf.reduce_mean(loss) q_network_losses[agent]["q_value_loss"] += loss From e243276f7258b52689bee68eb0228bbab126e4a1 Mon Sep 17 00:00:00 2001 From: Kevin Eloff Date: Mon, 24 May 2021 12:02:57 +0200 Subject: [PATCH 057/188] refactoring --- .../run_recurrent_dial_spread.py | 191 ------------------ mava/components/tf/networks/__init__.py | 1 - mava/components/tf/networks/dial.py | 105 ---------- mava/systems/tf/dial/system.py | 6 +- mava/systems/tf/dial/training.py | 4 +- 5 files changed, 5 insertions(+), 302 deletions(-) delete mode 100644 examples/debugging_envs/run_recurrent_dial_spread.py delete mode 100644 mava/components/tf/networks/dial.py diff --git a/examples/debugging_envs/run_recurrent_dial_spread.py b/examples/debugging_envs/run_recurrent_dial_spread.py deleted file mode 100644 index ac67b88c3..000000000 --- a/examples/debugging_envs/run_recurrent_dial_spread.py +++ /dev/null @@ -1,191 +0,0 @@ -# python3 -# Copyright 2021 [...placeholder...]. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Example running DIAL""" -import functools -from datetime import datetime -from typing import Any, Dict, Mapping, Sequence, Union - -import launchpad as lp -import numpy as np -import tensorflow as tf -from absl import app, flags -from acme import types -from acme.tf import utils as tf2_utils -from acme.wrappers.gym_wrapper import _convert_to_spec -from gym import spaces -from launchpad.nodes.python.local_multi_processing import PythonProcess - -from mava import specs as mava_specs -from mava.components.tf.networks import DIALPolicy -from mava.systems.tf import dial -from mava.utils import lp_utils -from mava.utils.environments import debugging_utils -from mava.utils.loggers import logger_utils - -FLAGS = flags.FLAGS -flags.DEFINE_string( - "env_name", - "simple_spread", - "Debugging environment name (str).", -) -flags.DEFINE_string( - "action_space", - "discrete", - "Environment action space type (str).", -) -flags.DEFINE_string( - "mava_id", - str(datetime.now()), - "Experiment identifier that can be used to continue experiments.", -) -flags.DEFINE_string("base_dir", "~/mava/", "Base dir to store experiments.") - - -# TODO Kevin: Define message head node correctly -def make_networks( - environment_spec: mava_specs.MAEnvironmentSpec, - policy_network_gru_hidden_sizes: Union[Dict[str, int], int] = 128, - policy_network_gru_layers: Union[Dict[str, int], int] = 2, - policy_network_task_mlp_sizes: Union[Dict[str, Sequence], Sequence] = (128,), - policy_network_message_in_mlp_sizes: Union[Dict[str, Sequence], Sequence] = (128,), - policy_network_message_out_mlp_sizes: Union[Dict[str, Sequence], Sequence] = (128,), - policy_network_output_mlp_sizes: Union[Dict[str, Sequence], Sequence] = (128,), - message_size: int = 10, - shared_weights: bool = True, - sigma: float = 0.3, -) -> Mapping[str, types.TensorTransformation]: - """Creates networks used by the agents.""" - specs = environment_spec.get_agent_specs() - # extra_specs = environment_spec.get_extra_specs() - - # Create agent_type specs - if shared_weights: - type_specs = {key.split("_")[0]: specs[key] for key in specs.keys()} - specs = type_specs - - if isinstance(policy_network_gru_hidden_sizes, int): - policy_network_gru_hidden_sizes = { - key: policy_network_gru_hidden_sizes for key in specs.keys() - } - - if isinstance(policy_network_gru_layers, int): - policy_network_gru_layers = { - key: policy_network_gru_layers for key in specs.keys() - } - - if isinstance(policy_network_task_mlp_sizes, Sequence): - policy_network_task_mlp_sizes = { - key: policy_network_task_mlp_sizes for key in specs.keys() - } - - if isinstance(policy_network_message_in_mlp_sizes, Sequence): - policy_network_message_in_mlp_sizes = { - key: policy_network_message_in_mlp_sizes for key in specs.keys() - } - - if isinstance(policy_network_message_out_mlp_sizes, Sequence): - policy_network_message_out_mlp_sizes = { - key: policy_network_message_out_mlp_sizes for key in specs.keys() - } - - if isinstance(policy_network_output_mlp_sizes, Sequence): - policy_network_output_mlp_sizes = { - key: policy_network_output_mlp_sizes for key in specs.keys() - } - - observation_networks = {} - policy_networks = {} - - for key in specs.keys(): - # Create the shared observation network - observation_network = tf2_utils.to_sonnet_module(tf.identity) - - # Create the policy network. - policy_network = DIALPolicy( - action_spec=specs[key].actions, - message_spec=_convert_to_spec( - spaces.Box(-np.inf, np.inf, (message_size,), dtype=np.float32) - ), - gru_hidden_size=policy_network_gru_hidden_sizes[key], - gru_layers=policy_network_gru_layers[key], - task_mlp_size=policy_network_task_mlp_sizes[key], - message_in_mlp_size=policy_network_message_in_mlp_sizes[key], - message_out_mlp_size=policy_network_message_out_mlp_sizes[key], - output_mlp_size=policy_network_output_mlp_sizes[key], - ) - - observation_networks[key] = observation_network - policy_networks[key] = policy_network - - return { - "policies": policy_networks, - "observations": observation_networks, - } - - -def main(_: Any) -> None: - - # environment - environment_factory = lp_utils.partial_kwargs( - debugging_utils.make_environment, - env_name=FLAGS.env_name, - action_space=FLAGS.action_space, - ) - - network_factory = lp_utils.partial_kwargs(make_networks) - - # Checkpointer appends "Checkpoints" to checkpoint_dir - checkpoint_dir = f"{FLAGS.base_dir}/{FLAGS.mava_id}" - - # loggers - log_every = 10 - logger_factory = functools.partial( - logger_utils.make_logger, - directory=FLAGS.base_dir, - to_terminal=True, - to_tensorboard=True, - time_stamp=FLAGS.mava_id, - time_delta=log_every, - ) - - program = dial.DIAL( - environment_factory=environment_factory, - network_factory=network_factory, - logger_factory=logger_factory, - num_executors=10, - batch_size=32, - sequence_length=100, - checkpoint_subpath=checkpoint_dir, - ).build() - - # launch - gpu_id = -1 - env_vars = {"CUDA_VISIBLE_DEVICES": str(gpu_id)} - local_resources = { - "trainer": [], - "evaluator": PythonProcess(env=env_vars), - "executor": PythonProcess(env=env_vars), - } - lp.launch( - program, - lp.LaunchType.LOCAL_MULTI_PROCESSING, - terminal="current_terminal", - local_resources=local_resources, - ) - - -if __name__ == "__main__": - app.run(main) diff --git a/mava/components/tf/networks/__init__.py b/mava/components/tf/networks/__init__.py index fcb25d850..12bfe9e2b 100644 --- a/mava/components/tf/networks/__init__.py +++ b/mava/components/tf/networks/__init__.py @@ -16,7 +16,6 @@ from mava.components.tf.networks.additive import AdditiveMixingNetwork from mava.components.tf.networks.communication import CommunicationNetwork -from mava.components.tf.networks.dial import DIALPolicy from mava.components.tf.networks.epsilon_greedy import epsilon_greedy_action_selector from mava.components.tf.networks.fingerprints import ObservationNetworkWithFingerprint from mava.components.tf.networks.monotonic import MonotonicMixingNetwork diff --git a/mava/components/tf/networks/dial.py b/mava/components/tf/networks/dial.py deleted file mode 100644 index 71b2d79ff..000000000 --- a/mava/components/tf/networks/dial.py +++ /dev/null @@ -1,105 +0,0 @@ -# Copyright 2021 [...placeholder...]. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from typing import Optional, Sequence - -import sonnet as snt -import tensorflow as tf -from acme.specs import EnvironmentSpec -from acme.tf import networks - - -class DIALPolicy(snt.RNNCore): - def __init__( - self, - action_spec: EnvironmentSpec, - message_spec: EnvironmentSpec, - gru_hidden_size: int, - gru_layers: int, - task_mlp_size: Sequence, - message_in_mlp_size: Sequence, - message_out_mlp_size: Sequence, - output_mlp_size: Sequence, - name: str = None, - ): - super(DIALPolicy, self).__init__(name=name) - self._action_spec = action_spec - self._action_dim = self._action_spec.num_values - self._message_spec = message_spec - self._message_dim = self._message_spec.shape[0] - - self._gru_hidden_size = gru_hidden_size - - self.task_mlp = networks.LayerNormMLP( - task_mlp_size, - activate_final=True, - ) - - self.message_in_mlp = networks.LayerNormMLP( - message_in_mlp_size, - activate_final=True, - ) - - self.gru = snt.GRU(gru_hidden_size) - - self.output_mlp = snt.Sequential( - [ - networks.LayerNormMLP(output_mlp_size, activate_final=True), - networks.NearZeroInitializedLinear(self._action_dim), - networks.TanhToSpec(self._action_spec), - ] - ) - - self.message_out_mlp = snt.Sequential( - [ - networks.LayerNormMLP(message_out_mlp_size), - networks.LayerNormMLP([self._message_dim]), - # TODO (dries): Why does this TanhToSpec break things? - # Is it the -inf -> inf bounds? - # Also should NearZeroInitializedLinear be the final layer? - # # networks.TanhToSpec(self._message_spec), - # TODO (Arnu): might need to implement more stable version. - ] - ) - - self._gru_layers = gru_layers - - def initial_state(self, batch_size: int = 1) -> snt.Module: - return self.gru.initial_state(batch_size) - - def initial_message(self, batch_size: int = 1) -> snt.Module: - return tf.zeros([batch_size, self._message_dim], dtype=tf.float32) - - def __call__( - self, - x: snt.Module, - state: Optional[snt.Module] = None, - message: Optional[snt.Module] = None, - ) -> snt.Module: - - if state is None: - state = self.initial_state() - if message is None: - message = tf.zeros((x.shape[0], self._message_dim), dtype=tf.float32) - - x_task = self.task_mlp(x) - x_message = self.message_in_mlp(message) - - x = tf.concat([x_task, x_message], axis=1) - - x, state = self.gru(x, state) - - x_output = self.output_mlp(x) - x_message = self.message_out_mlp(x) - return (x_output, x_message), state diff --git a/mava/systems/tf/dial/system.py b/mava/systems/tf/dial/system.py index 3009b2147..4de921c7c 100644 --- a/mava/systems/tf/dial/system.py +++ b/mava/systems/tf/dial/system.py @@ -105,12 +105,12 @@ def __init__( prefetch_size: int = 4, min_replay_size: int = 1000, max_replay_size: int = 1000000, - samples_per_insert: Optional[float] = 32.0, + samples_per_insert: Optional[float] = 4.0, n_step: int = 5, - sequence_length: int = 20, + sequence_length: int = 6, period: int = 20, clipping: bool = True, - discount: float = 0.99, + discount: float = 1, optimizer: snt.Optimizer = snt.optimizers.Adam(learning_rate=1e-4), target_update_period: int = 100, executor_variable_update_period: int = 1000, diff --git a/mava/systems/tf/dial/training.py b/mava/systems/tf/dial/training.py index 59d5de581..159bbd610 100644 --- a/mava/systems/tf/dial/training.py +++ b/mava/systems/tf/dial/training.py @@ -99,7 +99,7 @@ def _forward(self, inputs: Any) -> None: # Use fact that end of episode always has the reward to # find episode lengths. This is used to mask loss. - ep_end = tf.argmax(tf.math.abs(rewards[self._agents[0]]), axis=0) + # ep_end = tf.argmax(tf.math.abs(rewards[self._agents[0]]), axis=0) with tf.GradientTape(persistent=True) as tape: q_network_losses: Dict[str, NestedArray] = { @@ -170,7 +170,7 @@ def _forward(self, inputs: Any) -> None: ) # Index loss (mask ended episodes) - loss = tf.reduce_mean(loss[ep_end <= t]) + # loss = tf.reduce_mean(loss[ep_end <= t]) loss = tf.reduce_mean(loss) q_network_losses[agent]["q_value_loss"] += loss From f2f035cbaf6811b8545b1c6ab1326d783a106389 Mon Sep 17 00:00:00 2001 From: Kevin Eloff Date: Mon, 24 May 2021 12:25:45 +0200 Subject: [PATCH 058/188] fix: mypy --- mava/systems/tf/qmix/builder.py | 2 ++ mava/systems/tf/vdn/builder.py | 2 ++ 2 files changed, 4 insertions(+) diff --git a/mava/systems/tf/qmix/builder.py b/mava/systems/tf/qmix/builder.py index ffbb27ec6..4d4806313 100644 --- a/mava/systems/tf/qmix/builder.py +++ b/mava/systems/tf/qmix/builder.py @@ -21,6 +21,7 @@ from acme.utils import counting from mava import core, types +from mava.components.tf.modules.communication import BaseCommunicationModule from mava.components.tf.modules.exploration.exploration_scheduling import ( LinearExplorationScheduler, ) @@ -89,6 +90,7 @@ def make_trainer( dataset: Iterator[reverb.ReplaySample], counter: Optional[counting.Counter] = None, logger: Optional[types.NestedLogger] = None, + communication_module: Optional[BaseCommunicationModule] = None, ) -> core.Trainer: """Creates an instance of the trainer. Args: diff --git a/mava/systems/tf/vdn/builder.py b/mava/systems/tf/vdn/builder.py index 916150ebb..972aa9272 100644 --- a/mava/systems/tf/vdn/builder.py +++ b/mava/systems/tf/vdn/builder.py @@ -21,6 +21,7 @@ from acme.utils import counting from mava import core, types +from mava.components.tf.modules.communication import BaseCommunicationModule from mava.components.tf.modules.exploration.exploration_scheduling import ( LinearExplorationScheduler, ) @@ -90,6 +91,7 @@ def make_trainer( dataset: Iterator[reverb.ReplaySample], counter: Optional[counting.Counter] = None, logger: Optional[types.NestedLogger] = None, + communication_module: Optional[BaseCommunicationModule] = None, ) -> core.Trainer: """Creates an instance of the trainer. Args: From 3e9875c097a63d3f974f80f266099685545b38e9 Mon Sep 17 00:00:00 2001 From: Dries Date: Mon, 24 May 2021 12:49:59 +0200 Subject: [PATCH 059/188] Update trainers. --- mava/systems/tf/mad4pg/training.py | 9 ++++++++- mava/systems/tf/maddpg/training.py | 29 ++++++++++++++--------------- 2 files changed, 22 insertions(+), 16 deletions(-) diff --git a/mava/systems/tf/mad4pg/training.py b/mava/systems/tf/mad4pg/training.py index c70a50373..c2b123776 100644 --- a/mava/systems/tf/mad4pg/training.py +++ b/mava/systems/tf/mad4pg/training.py @@ -601,6 +601,13 @@ def _forward(self, inputs: Any) -> None: dpg_actions = tf2_utils.batch_to_sequence(outputs) + # Note (dries): This is done to so that losses.dpg can verify using gradient.tape that there is a + # gradient relationship between dpg_q_values and dpg_actions_comb. + dpg_actions_comb, dim = train_utils.combine_dim(dpg_actions) + + # Note (dries): This seemingly useless line is is important! Don't remove it. See above note. + dpg_actions = train_utils.extract_dim(dpg_actions_comb, dim) + # Get dpg actions dpg_actions_feed = self._get_dpg_feed( target_actions, dpg_actions, agent @@ -618,7 +625,7 @@ def _forward(self, inputs: Any) -> None: policy_loss = losses.dpg( dpg_q_values, - act_comb, + dpg_actions_comb, tape=tape, dqda_clipping=dqda_clipping, clip_norm=clip_norm, diff --git a/mava/systems/tf/maddpg/training.py b/mava/systems/tf/maddpg/training.py index 8e6e0a027..6d3e17662 100644 --- a/mava/systems/tf/maddpg/training.py +++ b/mava/systems/tf/maddpg/training.py @@ -372,6 +372,7 @@ def _forward(self, inputs: Any) -> None: dqda_clipping=dqda_clipping, clip_norm=clip_norm, ) + self.policy_losses[agent] = tf.reduce_mean(policy_loss, axis=0) self.tape = tape @@ -631,8 +632,8 @@ def _get_dpg_feed( # Note (dries): Copy has to be made because the input # variables cannot be changed. dpg_a_t_feed = copy.copy(a_t) - dpg_a_t_feed[agent] = dpg_a_t tree.map_structure(tf.stop_gradient, dpg_a_t_feed) + dpg_a_t_feed[agent] = dpg_a_t return dpg_a_t_feed @@ -755,8 +756,8 @@ def _get_dpg_feed( # Note (dries): Copy has to be made because the input # variables cannot be changed. dpg_a_t_feed = copy.copy(a_t) - dpg_a_t_feed[agent] = dpg_a_t tree.map_structure(tf.stop_gradient, dpg_a_t_feed) + dpg_a_t_feed[agent] = dpg_a_t return dpg_a_t_feed @@ -864,8 +865,8 @@ def _get_dpg_feed( # Note (dries): Copy has to be made because the input # variables cannot be changed. dpg_a_t_feed = copy.copy(a_t) - dpg_a_t_feed[agent] = dpg_a_t tree.map_structure(tf.stop_gradient, dpg_a_t_feed) + dpg_a_t_feed[agent] = dpg_a_t return dpg_a_t_feed @@ -1261,6 +1262,13 @@ def _forward(self, inputs: Any) -> None: dpg_actions = tf2_utils.batch_to_sequence(outputs) + # Note (dries): This is done to so that losses.dpg can verify using gradient.tape that there is a + # gradient relationship between dpg_q_values and dpg_actions_comb. + dpg_actions_comb, dim = train_utils.combine_dim(dpg_actions) + + # Note (dries): This seemingly useless line is is important! Don't remove it. See above note. + dpg_actions = train_utils.extract_dim(dpg_actions_comb, dim) + # Get dpg actions dpg_actions_feed = self._get_dpg_feed( target_actions, dpg_actions, agent @@ -1269,18 +1277,13 @@ def _forward(self, inputs: Any) -> None: # Get dpg Q values. obs_comb, _ = train_utils.combine_dim(target_obs_trans_feed) act_comb, _ = train_utils.combine_dim(dpg_actions_feed) - dpg_actions_comb, _ = train_utils.combine_dim(dpg_actions) + dpg_q_values = tf.squeeze(self._critic_networks[agent_key](obs_comb, act_comb)) # Actor loss. If clipping is true use dqda clipping and clip the norm. - # dpg_q_values = tf.squeeze(dpg_q_values, axis=-1) # [B] - dqda_clipping = 1.0 if self._max_gradient_norm is not None else None clip_norm = True if self._max_gradient_norm is not None else False - print("dpg_q_values: ", dpg_q_values.shape) - print("dpg_actions_comb: ", dpg_actions_comb.shape) - policy_loss = losses.dpg( dpg_q_values, dpg_actions_comb, @@ -1288,11 +1291,7 @@ def _forward(self, inputs: Any) -> None: dqda_clipping=dqda_clipping, clip_norm=clip_norm, ) - print("policy_loss: ", policy_loss.shape) self.policy_losses[agent] = tf.reduce_mean(policy_loss, axis=0) - print("policy_loss reduced: ", self.policy_losses[agent].shape) - exit() - self.tape = tape # Backward pass that calculates gradients and updates network. @@ -1553,11 +1552,11 @@ def _get_dpg_feed( # Note (dries): Copy has to be made because the input # variables cannot be changed. dpg_actions_feed = copy.copy(actions) + tree.map_structure(tf.stop_gradient, dpg_actions_feed) dpg_actions_feed[agent] = dpg_actions dpg_actions_feed = tf.squeeze( tf.stack([x for x in dpg_actions_feed.values()], -1) ) - tree.map_structure(tf.stop_gradient, dpg_actions_feed) return dpg_actions_feed @@ -1666,9 +1665,9 @@ def _get_dpg_feed( # Note (dries): Copy has to be made because the input # variables cannot be changed. dpg_actions_feed = copy.copy(actions) + tree.map_structure(tf.stop_gradient, dpg_actions_feed) dpg_actions_feed[agent] = dpg_actions dpg_actions_feed = tf.squeeze( tf.stack([x for x in dpg_actions_feed.values()], -1) ) - tree.map_structure(tf.stop_gradient, dpg_actions_feed) return dpg_actions_feed From e777089572927e6182375e357e221a7f69a746b2 Mon Sep 17 00:00:00 2001 From: Kevin Eloff Date: Mon, 24 May 2021 14:09:17 +0200 Subject: [PATCH 060/188] feat: loss masking --- examples/debugging_envs/run_recurrent_dial.py | 2 +- mava/systems/tf/dial/system.py | 2 +- mava/systems/tf/dial/training.py | 23 +++++++++++++++---- 3 files changed, 21 insertions(+), 6 deletions(-) diff --git a/examples/debugging_envs/run_recurrent_dial.py b/examples/debugging_envs/run_recurrent_dial.py index 72f593e0f..23264cdaf 100644 --- a/examples/debugging_envs/run_recurrent_dial.py +++ b/examples/debugging_envs/run_recurrent_dial.py @@ -168,7 +168,7 @@ def main(_: Any) -> None: communication_module=BroadcastedCommunication, sequence_length=6, epsilon_min=0.05, - epsilon_decay=5e-4, + epsilon_decay=1e-2, # optimizer=snt.optimizers.Adam(learning_rate=1e-4), optimizer=snt.optimizers.RMSProp(learning_rate=1e-4, momentum=0.95), checkpoint_subpath=checkpoint_dir, diff --git a/mava/systems/tf/dial/system.py b/mava/systems/tf/dial/system.py index 4de921c7c..00b62284d 100644 --- a/mava/systems/tf/dial/system.py +++ b/mava/systems/tf/dial/system.py @@ -96,7 +96,7 @@ def __init__( ] = LinearExplorationScheduler, replay_stabilisation_fn: Optional[Type[FingerPrintStabalisation]] = None, epsilon_min: float = 0.05, - epsilon_decay: float = 1e-4, + epsilon_decay: float = 1e-3, num_executors: int = 1, num_caches: int = 0, environment_spec: mava_specs.MAEnvironmentSpec = None, diff --git a/mava/systems/tf/dial/training.py b/mava/systems/tf/dial/training.py index 159bbd610..10732930e 100644 --- a/mava/systems/tf/dial/training.py +++ b/mava/systems/tf/dial/training.py @@ -99,7 +99,7 @@ def _forward(self, inputs: Any) -> None: # Use fact that end of episode always has the reward to # find episode lengths. This is used to mask loss. - # ep_end = tf.argmax(tf.math.abs(rewards[self._agents[0]]), axis=0) + ep_end = tf.argmax(tf.math.abs(rewards[self._agents[0]]), axis=0) with tf.GradientTape(persistent=True) as tape: q_network_losses: Dict[str, NestedArray] = { @@ -160,19 +160,34 @@ def _forward(self, inputs: Any) -> None: m, observations[agent].observation[t - 1][:, :1] ) + # Mask target + q_targ = tf.concat( + [ + [q_targ[i]] + if t <= ep_end[i] + else [tf.zeros_like(q_targ[i])] + for i in range(q_targ.shape[0]) + ], + axis=0, + ) + loss, _ = trfl.qlearning( q, actions[agent][t - 1], rewards[agent][t - 1], discount * discounts[agent][t], q_targ, - # q_targ*(ep_end <= t), # mask last episode ) # Index loss (mask ended episodes) - # loss = tf.reduce_mean(loss[ep_end <= t]) - loss = tf.reduce_mean(loss) + if not tf.reduce_any(t - 1 <= ep_end): + continue + + loss = tf.reduce_mean(loss[t - 1 <= ep_end]) + # loss = tf.reduce_mean(loss) q_network_losses[agent]["q_value_loss"] += loss + # print(q_network_losses['agent_0']) self._q_network_losses = q_network_losses self.tape = tape + # {}.t From 6fec483166989921d768b94ea980753816b25963 Mon Sep 17 00:00:00 2001 From: Kevin Eloff Date: Mon, 24 May 2021 14:23:17 +0200 Subject: [PATCH 061/188] feat: channel capacity 1 --- examples/debugging_envs/run_recurrent_dial.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/debugging_envs/run_recurrent_dial.py b/examples/debugging_envs/run_recurrent_dial.py index 23264cdaf..a6fb93ef2 100644 --- a/examples/debugging_envs/run_recurrent_dial.py +++ b/examples/debugging_envs/run_recurrent_dial.py @@ -64,7 +64,7 @@ def make_networks( environment_spec: mava_specs.MAEnvironmentSpec, - message_size: int = 10, + message_size: int = 1, shared_weights: bool = True, ) -> Mapping[str, types.TensorTransformation]: """Creates networks used by the agents.""" @@ -168,7 +168,7 @@ def main(_: Any) -> None: communication_module=BroadcastedCommunication, sequence_length=6, epsilon_min=0.05, - epsilon_decay=1e-2, + epsilon_decay=5e-4, # optimizer=snt.optimizers.Adam(learning_rate=1e-4), optimizer=snt.optimizers.RMSProp(learning_rate=1e-4, momentum=0.95), checkpoint_subpath=checkpoint_dir, From 2b9b0a64913d3dfa93293ae441d51a7eaaf31ebf Mon Sep 17 00:00:00 2001 From: Kale-ab Date: Mon, 24 May 2021 17:21:01 +0200 Subject: [PATCH 062/188] feat: Split optims by agent type. --- mava/systems/tf/mad4pg/training.py | 96 ++++++++-------- mava/systems/tf/maddpg/builder.py | 14 ++- mava/systems/tf/maddpg/training.py | 124 ++++++++++----------- mava/wrappers/system_trainer_statistics.py | 6 +- 4 files changed, 124 insertions(+), 116 deletions(-) diff --git a/mava/systems/tf/mad4pg/training.py b/mava/systems/tf/mad4pg/training.py index c70a50373..33e5875e9 100644 --- a/mava/systems/tf/mad4pg/training.py +++ b/mava/systems/tf/mad4pg/training.py @@ -52,8 +52,8 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizer: snt.Optimizer, - critic_optimizer: snt.Optimizer, + policy_optimizers: Dict[str, snt.Optimizer], + critic_optimizers: Dict[str, snt.Optimizer], discount: float, target_averaging: bool, target_update_period: int, @@ -83,8 +83,8 @@ def __init__( observation_network: an optional online network to process observations before the policy and the critic. target_observation_network: the target observation network. - policy_optimizer: the optimizer to be applied to the DPG (policy) loss. - critic_optimizer: the optimizer to be applied to the critic loss. + policy_optimizers: the optimizer to be applied to the DPG (policy) loss. + critic_optimizers: the optimizer to be applied to the critic loss. clipping: whether to clip gradients by global norm. counter: counter object used to keep track of steps. logger: logger object to be used by learner. @@ -98,8 +98,8 @@ def __init__( critic_networks=critic_networks, target_policy_networks=target_policy_networks, target_critic_networks=target_critic_networks, - policy_optimizer=policy_optimizer, - critic_optimizer=critic_optimizer, + policy_optimizers=policy_optimizers, + critic_optimizers=critic_optimizers, discount=discount, target_averaging=target_averaging, target_update_period=target_update_period, @@ -203,8 +203,8 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizer: snt.Optimizer, - critic_optimizer: snt.Optimizer, + policy_optimizers: Dict[str, snt.Optimizer], + critic_optimizers: Dict[str, snt.Optimizer], discount: float, target_averaging: bool, target_update_period: int, @@ -234,8 +234,8 @@ def __init__( observation_network: an optional online network to process observations before the policy and the critic. target_observation_network: the target observation network. - policy_optimizer: the optimizer to be applied to the DPG (policy) loss. - critic_optimizer: the optimizer to be applied to the critic loss. + policy_optimizers: the optimizer to be applied to the DPG (policy) loss. + critic_optimizers: the optimizer to be applied to the critic loss. clipping: whether to clip gradients by global norm. counter: counter object used to keep track of steps. logger: logger object to be used by learner. @@ -257,8 +257,8 @@ def __init__( observation_networks=observation_networks, target_observation_networks=target_observation_networks, shared_weights=shared_weights, - policy_optimizer=policy_optimizer, - critic_optimizer=critic_optimizer, + policy_optimizers=policy_optimizers, + critic_optimizers=critic_optimizers, max_gradient_norm=max_gradient_norm, counter=counter, logger=logger, @@ -281,8 +281,8 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizer: snt.Optimizer, - critic_optimizer: snt.Optimizer, + policy_optimizers: Dict[str, snt.Optimizer], + critic_optimizers: Dict[str, snt.Optimizer], discount: float, target_averaging: bool, target_update_period: int, @@ -312,8 +312,8 @@ def __init__( observation_network: an optional online network to process observations before the policy and the critic. target_observation_network: the target observation network. - policy_optimizer: the optimizer to be applied to the DPG (policy) loss. - critic_optimizer: the optimizer to be applied to the critic loss. + policy_optimizers: the optimizer to be applied to the DPG (policy) loss. + critic_optimizers: the optimizer to be applied to the critic loss. clipping: whether to clip gradients by global norm. counter: counter object used to keep track of steps. logger: logger object to be used by learner. @@ -335,8 +335,8 @@ def __init__( observation_networks=observation_networks, target_observation_networks=target_observation_networks, shared_weights=shared_weights, - policy_optimizer=policy_optimizer, - critic_optimizer=critic_optimizer, + policy_optimizers=policy_optimizers, + critic_optimizers=critic_optimizers, max_gradient_norm=max_gradient_norm, counter=counter, logger=logger, @@ -359,8 +359,8 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizer: snt.Optimizer, - critic_optimizer: snt.Optimizer, + policy_optimizers: Dict[str, snt.Optimizer], + critic_optimizers: Dict[str, snt.Optimizer], discount: float, target_averaging: bool, target_update_period: int, @@ -390,8 +390,8 @@ def __init__( observation_network: an optional online network to process observations before the policy and the critic. target_observation_network: the target observation network. - policy_optimizer: the optimizer to be applied to the DPG (policy) loss. - critic_optimizer: the optimizer to be applied to the critic loss. + policy_optimizers: the optimizer to be applied to the DPG (policy) loss. + critic_optimizers: the optimizer to be applied to the critic loss. clipping: whether to clip gradients by global norm. counter: counter object used to keep track of steps. logger: logger object to be used by learner. @@ -413,8 +413,8 @@ def __init__( observation_networks=observation_networks, target_observation_networks=target_observation_networks, shared_weights=shared_weights, - policy_optimizer=policy_optimizer, - critic_optimizer=critic_optimizer, + policy_optimizers=policy_optimizers, + critic_optimizers=critic_optimizers, max_gradient_norm=max_gradient_norm, counter=counter, logger=logger, @@ -437,8 +437,8 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizer: snt.Optimizer, - critic_optimizer: snt.Optimizer, + policy_optimizers: Dict[str, snt.Optimizer], + critic_optimizers: Dict[str, snt.Optimizer], discount: float, target_averaging: bool, target_update_period: int, @@ -469,8 +469,8 @@ def __init__( observation_network: an optional online network to process observations before the policy and the critic. target_observation_network: the target observation network. - policy_optimizer: the optimizer to be applied to the DPG (policy) loss. - critic_optimizer: the optimizer to be applied to the critic loss. + policy_optimizers: the optimizer to be applied to the DPG (policy) loss. + critic_optimizers: the optimizer to be applied to the critic loss. clipping: whether to clip gradients by global norm. counter: counter object used to keep track of steps. logger: logger object to be used by learner. @@ -492,8 +492,8 @@ def __init__( observation_networks=observation_networks, target_observation_networks=target_observation_networks, shared_weights=shared_weights, - policy_optimizer=policy_optimizer, - critic_optimizer=critic_optimizer, + policy_optimizers=policy_optimizers, + critic_optimizers=critic_optimizers, max_gradient_norm=max_gradient_norm, counter=counter, logger=logger, @@ -643,8 +643,8 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizer: snt.Optimizer, - critic_optimizer: snt.Optimizer, + policy_optimizers: Dict[str, snt.Optimizer], + critic_optimizers: Dict[str, snt.Optimizer], discount: float, target_averaging: bool, target_update_period: int, @@ -675,8 +675,8 @@ def __init__( observation_network: an optional online network to process observations before the policy and the critic. target_observation_network: the target observation network. - policy_optimizer: the optimizer to be applied to the DPG (policy) loss. - critic_optimizer: the optimizer to be applied to the critic loss. + policy_optimizers: the optimizer to be applied to the DPG (policy) loss. + critic_optimizers: the optimizer to be applied to the critic loss. clipping: whether to clip gradients by global norm. counter: counter object used to keep track of steps. logger: logger object to be used by learner. @@ -698,8 +698,8 @@ def __init__( observation_networks=observation_networks, target_observation_networks=target_observation_networks, shared_weights=shared_weights, - policy_optimizer=policy_optimizer, - critic_optimizer=critic_optimizer, + policy_optimizers=policy_optimizers, + critic_optimizers=critic_optimizers, max_gradient_norm=max_gradient_norm, counter=counter, logger=logger, @@ -725,8 +725,8 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizer: snt.Optimizer, - critic_optimizer: snt.Optimizer, + policy_optimizers: Dict[str, snt.Optimizer], + critic_optimizers: Dict[str, snt.Optimizer], discount: float, target_averaging: bool, target_update_period: int, @@ -757,8 +757,8 @@ def __init__( observation_network: an optional online network to process observations before the policy and the critic. target_observation_network: the target observation network. - policy_optimizer: the optimizer to be applied to the DPG (policy) loss. - critic_optimizer: the optimizer to be applied to the critic loss. + policy_optimizers: the optimizer to be applied to the DPG (policy) loss. + critic_optimizers: the optimizer to be applied to the critic loss. clipping: whether to clip gradients by global norm. counter: counter object used to keep track of steps. logger: logger object to be used by learner. @@ -780,8 +780,8 @@ def __init__( observation_networks=observation_networks, target_observation_networks=target_observation_networks, shared_weights=shared_weights, - policy_optimizer=policy_optimizer, - critic_optimizer=critic_optimizer, + policy_optimizers=policy_optimizers, + critic_optimizers=critic_optimizers, max_gradient_norm=max_gradient_norm, counter=counter, logger=logger, @@ -807,8 +807,8 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizer: snt.Optimizer, - critic_optimizer: snt.Optimizer, + policy_optimizers: Dict[str, snt.Optimizer], + critic_optimizers: Dict[str, snt.Optimizer], discount: float, target_averaging: bool, target_update_period: int, @@ -839,8 +839,8 @@ def __init__( observation_network: an optional online network to process observations before the policy and the critic. target_observation_network: the target observation network. - policy_optimizer: the optimizer to be applied to the DPG (policy) loss. - critic_optimizer: the optimizer to be applied to the critic loss. + policy_optimizers: the optimizer to be applied to the DPG (policy) loss. + critic_optimizers: the optimizer to be applied to the critic loss. clipping: whether to clip gradients by global norm. counter: counter object used to keep track of steps. logger: logger object to be used by learner. @@ -862,8 +862,8 @@ def __init__( observation_networks=observation_networks, target_observation_networks=target_observation_networks, shared_weights=shared_weights, - policy_optimizer=policy_optimizer, - critic_optimizer=critic_optimizer, + policy_optimizers=policy_optimizers, + critic_optimizers=critic_optimizers, max_gradient_norm=max_gradient_norm, counter=counter, logger=logger, diff --git a/mava/systems/tf/maddpg/builder.py b/mava/systems/tf/maddpg/builder.py index 1fd2a33d7..3929a42b9 100644 --- a/mava/systems/tf/maddpg/builder.py +++ b/mava/systems/tf/maddpg/builder.py @@ -323,8 +323,16 @@ def make_trainer( target_averaging = self._config.target_averaging target_update_rate = self._config.target_update_rate + # Create optimizers for different agent types. + # TODO(Kale-ab): Allow this to be passed as a system param. + policy_optimizers: Dict[str, snt.Optimizer] = {} + critic_optimizers: Dict[str, snt.Optimizer] = {} + for agent in self._agent_types: + policy_optimizers[agent] = copy.deepcopy(self._config.policy_optimizer) + critic_optimizers[agent] = copy.deepcopy(self._config.critic_optimizer) + # trainer args - trainer_config = { + trainer_config: Dict[str, Any] = { "agents": agents, "agent_types": agent_types, "policy_networks": networks["policies"], @@ -334,8 +342,8 @@ def make_trainer( "target_critic_networks": networks["target_critics"], "target_observation_networks": networks["target_observations"], "shared_weights": shared_weights, - "policy_optimizer": self._config.policy_optimizer, - "critic_optimizer": self._config.critic_optimizer, + "policy_optimizers": policy_optimizers, + "critic_optimizers": critic_optimizers, "max_gradient_norm": max_gradient_norm, "discount": discount, "target_averaging": target_averaging, diff --git a/mava/systems/tf/maddpg/training.py b/mava/systems/tf/maddpg/training.py index f1a35b6f3..89676c09b 100644 --- a/mava/systems/tf/maddpg/training.py +++ b/mava/systems/tf/maddpg/training.py @@ -51,8 +51,8 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizer: snt.Optimizer, - critic_optimizer: snt.Optimizer, + policy_optimizers: Dict[str, snt.Optimizer], + critic_optimizers: Dict[str, snt.Optimizer], discount: float, target_averaging: bool, target_update_period: int, @@ -86,8 +86,8 @@ def __init__( observation_network: an optional online network to process observations before the policy and the critic. target_observation_network: the target observation network. - policy_optimizer: the optimizer to be applied to the DPG (policy) loss. - critic_optimizer: the optimizer to be applied to the critic loss. + policy_optimizers: the optimizer to be applied to the DPG (policy) loss. + critic_optimizers: the optimizer to be applied to the critic loss. clipping: whether to clip gradients by global norm. counter: counter object used to keep track of steps. logger: logger object to be used by learner. @@ -136,8 +136,8 @@ def __init__( # Create an iterator to go through the dataset. self._iterator = iter(dataset) # pytype: disable=wrong-arg-types - self._critic_optimizer = critic_optimizer - self._policy_optimizer = policy_optimizer + self._critic_optimizers = critic_optimizers + self._policy_optimizers = policy_optimizers # Dictionary with network keys for each agent. self.agent_net_keys = {agent: agent for agent in self._agents} @@ -179,8 +179,8 @@ def __init__( "target_policy": self._target_policy_networks[agent_key], "target_critic": self._target_critic_networks[agent_key], "target_observation": self._target_observation_networks[agent_key], - "policy_optimizer": self._policy_optimizer, - "critic_optimizer": self._critic_optimizer, + "policy_optimizer": self._policy_optimizers, + "critic_optimizer": self._critic_optimizers, "num_steps": self._num_steps, } @@ -412,8 +412,8 @@ def _backward(self) -> None: )[0] # Apply gradients. - self._policy_optimizer.apply(policy_gradients, policy_variables) - self._critic_optimizer.apply(critic_gradients, critic_variables) + self._policy_optimizers[agent_key].apply(policy_gradients, policy_variables) + self._critic_optimizers[agent_key].apply(critic_gradients, critic_variables) train_utils.safe_del(self, "tape") def step(self) -> None: @@ -462,8 +462,8 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizer: snt.Optimizer, - critic_optimizer: snt.Optimizer, + policy_optimizers: Dict[str, snt.Optimizer], + critic_optimizers: Dict[str, snt.Optimizer], discount: float, target_averaging: bool, target_update_period: int, @@ -493,8 +493,8 @@ def __init__( observation_network: an optional online network to process observations before the policy and the critic. target_observation_network: the target observation network. - policy_optimizer: the optimizer to be applied to the DPG (policy) loss. - critic_optimizer: the optimizer to be applied to the critic loss. + policy_optimizers: the optimizers to be applied to the DPG (policy) loss. + critic_optimizers: the optimizers to be applied to the critic loss. clipping: whether to clip gradients by global norm. counter: counter object used to keep track of steps. logger: logger object to be used by learner. @@ -516,8 +516,8 @@ def __init__( observation_networks=observation_networks, target_observation_networks=target_observation_networks, shared_weights=shared_weights, - policy_optimizer=policy_optimizer, - critic_optimizer=critic_optimizer, + policy_optimizers=policy_optimizers, + critic_optimizers=critic_optimizers, max_gradient_norm=max_gradient_norm, counter=counter, logger=logger, @@ -540,8 +540,8 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizer: snt.Optimizer, - critic_optimizer: snt.Optimizer, + policy_optimizers: Dict[str, snt.Optimizer], + critic_optimizers: Dict[str, snt.Optimizer], discount: float, target_averaging: bool, target_update_period: int, @@ -571,8 +571,8 @@ def __init__( observation_network: an optional online network to process observations before the policy and the critic. target_observation_network: the target observation network. - policy_optimizer: the optimizer to be applied to the DPG (policy) loss. - critic_optimizer: the optimizer to be applied to the critic loss. + policy_optimizers: the optimizer to be applied to the DPG (policy) loss. + critic_optimizers: the optimizer to be applied to the critic loss. clipping: whether to clip gradients by global norm. counter: counter object used to keep track of steps. logger: logger object to be used by learner. @@ -594,8 +594,8 @@ def __init__( observation_networks=observation_networks, target_observation_networks=target_observation_networks, shared_weights=shared_weights, - policy_optimizer=policy_optimizer, - critic_optimizer=critic_optimizer, + policy_optimizers=policy_optimizers, + critic_optimizers=critic_optimizers, max_gradient_norm=max_gradient_norm, counter=counter, logger=logger, @@ -656,11 +656,11 @@ def __init__( target_update_period: int, target_update_rate: float, dataset: tf.data.Dataset, + policy_optimizers: Dict[str, snt.Optimizer], + critic_optimizers: Dict[str, snt.Optimizer], observation_networks: Dict[str, snt.Module], target_observation_networks: Dict[str, snt.Module], shared_weights: bool = False, - policy_optimizer: snt.Optimizer = None, - critic_optimizer: snt.Optimizer = None, max_gradient_norm: float = None, counter: counting.Counter = None, logger: loggers.Logger = None, @@ -682,8 +682,8 @@ def __init__( observation_network: an optional online network to process observations before the policy and the critic. target_observation_network: the target observation network. - policy_optimizer: the optimizer to be applied to the DPG (policy) loss. - critic_optimizer: the optimizer to be applied to the critic loss. + policy_optimizers: the optimizer to be applied to the DPG (policy) loss. + critic_optimizers: the optimizer to be applied to the critic loss. clipping: whether to clip gradients by global norm. counter: counter object used to keep track of steps. logger: logger object to be used by learner. @@ -705,8 +705,8 @@ def __init__( observation_networks=observation_networks, target_observation_networks=target_observation_networks, shared_weights=shared_weights, - policy_optimizer=policy_optimizer, - critic_optimizer=critic_optimizer, + policy_optimizers=policy_optimizers, + critic_optimizers=critic_optimizers, max_gradient_norm=max_gradient_norm, counter=counter, logger=logger, @@ -774,8 +774,8 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizer: snt.Optimizer, - critic_optimizer: snt.Optimizer, + policy_optimizers: Dict[str, snt.Optimizer], + critic_optimizers: Dict[str, snt.Optimizer], discount: float, target_averaging: bool, target_update_period: int, @@ -805,8 +805,8 @@ def __init__( observation_network: an optional online network to process observations before the policy and the critic. target_observation_network: the target observation network. - policy_optimizer: the optimizer to be applied to the DPG (policy) loss. - critic_optimizer: the optimizer to be applied to the critic loss. + policy_optimizers: the optimizer to be applied to the DPG (policy) loss. + critic_optimizers: the optimizer to be applied to the critic loss. clipping: whether to clip gradients by global norm. counter: counter object used to keep track of steps. logger: logger object to be used by learner. @@ -828,8 +828,8 @@ def __init__( observation_networks=observation_networks, target_observation_networks=target_observation_networks, shared_weights=shared_weights, - policy_optimizer=policy_optimizer, - critic_optimizer=critic_optimizer, + policy_optimizers=policy_optimizers, + critic_optimizers=critic_optimizers, max_gradient_norm=max_gradient_norm, counter=counter, logger=logger, @@ -884,8 +884,8 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizer: snt.Optimizer, - critic_optimizer: snt.Optimizer, + policy_optimizers: Dict[str, snt.Optimizer], + critic_optimizers: Dict[str, snt.Optimizer], discount: float, target_averaging: bool, target_update_period: int, @@ -916,8 +916,8 @@ def __init__( observation_network: an optional online network to process observations before the policy and the critic. target_observation_network: the target observation network. - policy_optimizer: the optimizer to be applied to the DPG (policy) loss. - critic_optimizer: the optimizer to be applied to the critic loss. + policy_optimizers: the optimizer to be applied to the DPG (policy) loss. + critic_optimizers: the optimizer to be applied to the critic loss. clipping: whether to clip gradients by global norm. counter: counter object used to keep track of steps. logger: logger object to be used by learner. @@ -967,8 +967,8 @@ def __init__( # Create an iterator to go through the dataset. self._iterator = iter(dataset) # pytype: disable=wrong-arg-types - self._critic_optimizer = critic_optimizer - self._policy_optimizer = policy_optimizer + self._critic_optimizers = critic_optimizers + self._policy_optimizers = policy_optimizers # Dictionary with network keys for each agent. self.agent_net_keys = {agent: agent for agent in self._agents} @@ -1010,8 +1010,8 @@ def __init__( "target_policy": self._target_policy_networks[agent_key], "target_critic": self._target_critic_networks[agent_key], "target_observation": self._target_observation_networks[agent_key], - "policy_optimizer": self._policy_optimizer, - "critic_optimizer": self._critic_optimizer, + "policy_optimizer": self._policy_optimizers, + "critic_optimizer": self._critic_optimizers, "num_steps": self._num_steps, } @@ -1324,8 +1324,8 @@ def _backward(self) -> None: )[0] # Apply gradients. - self._policy_optimizer.apply(policy_gradients, policy_variables) - self._critic_optimizer.apply(critic_gradients, critic_variables) + self._policy_optimizers[agent_key].apply(policy_gradients, policy_variables) + self._critic_optimizers[agent_key].apply(critic_gradients, critic_variables) train_utils.safe_del(self, "tape") def step(self) -> None: @@ -1374,8 +1374,8 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizer: snt.Optimizer, - critic_optimizer: snt.Optimizer, + policy_optimizers: Dict[str, snt.Optimizer], + critic_optimizers: Dict[str, snt.Optimizer], discount: float, target_averaging: bool, target_update_period: int, @@ -1406,8 +1406,8 @@ def __init__( observation_network: an optional online network to process observations before the policy and the critic. target_observation_network: the target observation network. - policy_optimizer: the optimizer to be applied to the DPG (policy) loss. - critic_optimizer: the optimizer to be applied to the critic loss. + policy_optimizers: the optimizer to be applied to the DPG (policy) loss. + critic_optimizers: the optimizer to be applied to the critic loss. clipping: whether to clip gradients by global norm. counter: counter object used to keep track of steps. logger: logger object to be used by learner. @@ -1429,8 +1429,8 @@ def __init__( observation_networks=observation_networks, target_observation_networks=target_observation_networks, shared_weights=shared_weights, - policy_optimizer=policy_optimizer, - critic_optimizer=critic_optimizer, + policy_optimizers=policy_optimizers, + critic_optimizers=critic_optimizers, max_gradient_norm=max_gradient_norm, counter=counter, logger=logger, @@ -1454,8 +1454,8 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizer: snt.Optimizer, - critic_optimizer: snt.Optimizer, + policy_optimizers: Dict[str, snt.Optimizer], + critic_optimizers: Dict[str, snt.Optimizer], discount: float, target_averaging: bool, target_update_period: int, @@ -1486,8 +1486,8 @@ def __init__( observation_network: an optional online network to process observations before the policy and the critic. target_observation_network: the target observation network. - policy_optimizer: the optimizer to be applied to the DPG (policy) loss. - critic_optimizer: the optimizer to be applied to the critic loss. + policy_optimizers: the optimizer to be applied to the DPG (policy) loss. + critic_optimizers: the optimizer to be applied to the critic loss. clipping: whether to clip gradients by global norm. counter: counter object used to keep track of steps. logger: logger object to be used by learner. @@ -1508,8 +1508,8 @@ def __init__( observation_networks=observation_networks, target_observation_networks=target_observation_networks, shared_weights=shared_weights, - policy_optimizer=policy_optimizer, - critic_optimizer=critic_optimizer, + policy_optimizers=policy_optimizers, + critic_optimizers=critic_optimizers, max_gradient_norm=max_gradient_norm, counter=counter, logger=logger, @@ -1567,8 +1567,8 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizer: snt.Optimizer, - critic_optimizer: snt.Optimizer, + policy_optimizers: Dict[str, snt.Optimizer], + critic_optimizers: Dict[str, snt.Optimizer], discount: float, target_averaging: bool, target_update_period: int, @@ -1599,8 +1599,8 @@ def __init__( observation_network: an optional online network to process observations before the policy and the critic. target_observation_network: the target observation network. - policy_optimizer: the optimizer to be applied to the DPG (policy) loss. - critic_optimizer: the optimizer to be applied to the critic loss. + policy_optimizers: the optimizer to be applied to the DPG (policy) loss. + critic_optimizers: the optimizer to be applied to the critic loss. clipping: whether to clip gradients by global norm. counter: counter object used to keep track of steps. logger: logger object to be used by learner. @@ -1621,8 +1621,8 @@ def __init__( observation_networks=observation_networks, target_observation_networks=target_observation_networks, shared_weights=shared_weights, - policy_optimizer=policy_optimizer, - critic_optimizer=critic_optimizer, + policy_optimizers=policy_optimizers, + critic_optimizers=critic_optimizers, max_gradient_norm=max_gradient_norm, counter=counter, logger=logger, diff --git a/mava/wrappers/system_trainer_statistics.py b/mava/wrappers/system_trainer_statistics.py index 7d005ba0d..9e1ba6fe4 100644 --- a/mava/wrappers/system_trainer_statistics.py +++ b/mava/wrappers/system_trainer_statistics.py @@ -409,7 +409,7 @@ def _backward(self) -> None: policy_gradients = tf.clip_by_global_norm(policy_gradients, 40.0)[0] # Apply gradients. - self._policy_optimizer.apply(policy_gradients, policy_variables) + self._policy_optimizers[agent_key].apply(policy_gradients, policy_variables) if log_current_timestep: if self.log_weights: @@ -509,8 +509,8 @@ def _backward(self) -> None: )[0] # Apply gradients. - self._policy_optimizer.apply(policy_gradients, policy_variables) - self._critic_optimizer.apply(critic_gradients, critic_variables) + self._policy_optimizers[agent_key].apply(policy_gradients, policy_variables) + self._critic_optimizers[agent_key].apply(critic_gradients, critic_variables) if log_current_timestep: if self.log_weights: From 6a3e16efd672fc5a4c3314c2dd9a6288df90039f Mon Sep 17 00:00:00 2001 From: Kale-ab Date: Mon, 24 May 2021 17:28:11 +0200 Subject: [PATCH 063/188] feat: Added discrete maddpg PZ example and set mappo to use diff optims. --- Makefile | 4 +- README.md | 2 +- ...entralised_feedforward_mad4pg_discrete.py} | 0 ...entralised_feedforward_maddpg_discrete.py} | 0 ...ntralised_feedforward_maddpg_continous.py} | 2 +- ...centralised_feedforward_maddpg_discrete.py | 198 ++++++++++++++++++ mava/systems/tf/mappo/builder.py | 12 +- mava/systems/tf/mappo/training.py | 44 ++-- 8 files changed, 234 insertions(+), 28 deletions(-) rename examples/debugging_envs/{run_decentralised_feedforward_discrete_mad4pg.py => run_decentralised_feedforward_mad4pg_discrete.py} (100%) rename examples/debugging_envs/{run_decentralised_feedforward_discrete_maddpg.py => run_decentralised_feedforward_maddpg_discrete.py} (100%) rename examples/petting_zoo/{run_decentralised_feedforward_maddpg.py => run_decentralised_feedforward_maddpg_continous.py} (98%) create mode 100644 examples/petting_zoo/run_decentralised_feedforward_maddpg_discrete.py diff --git a/Makefile b/Makefile index 6b77da4a4..b56c8a67a 100755 --- a/Makefile +++ b/Makefile @@ -16,13 +16,13 @@ DOCKER_RUN=docker run $(RUN_FLAGS) $(IMAGE) DOCKER_RUN_TENSORBOARD=docker run $(RUN_FLAGS_TENSORBOARD) $(IMAGE) # Set example to run when using `make run` -MADDPG=examples/debugging_envs/run_decentralised_feedforward_maddpg.py +MADDPG=examples/debugging_envs/run_decentralised_feedforward_maddpg_continous.py MAPPO=examples/debugging_envs/run_decentralised_feedforward_mappo.py MADQN=examples/debugging_envs/run_feedforward_madqn.py QMIX=examples/debugging_envs/run_feedforward_qmix.py VDN=examples/debugging_envs/run_feedforward_vdn.py -MADDPG-PZ=examples/petting_zoo/run_decentralised_feedforward_maddpg.py +MADDPG-PZ=examples/petting_zoo/run_decentralised_feedforward_maddpg_continous.py QMIX-PZ=examples/petting_zoo/run_feedforward_qmix.py VDN-PZ==examples/petting_zoo/run_feedforward_vdn.py diff --git a/README.md b/README.md index ab5cdbd0d..a0a2ddc8a 100644 --- a/README.md +++ b/README.md @@ -51,7 +51,7 @@ We have tested `mava` on Python 3.6, 3.7 and 3.8. ```bash make run EXAMPLE=dir/to/example/example.py ``` - For example, `make run EXAMPLE=examples/petting_zoo/run_decentralised_feedforward_maddpg.py`. + For example, `make run EXAMPLE=examples/petting_zoo/run_decentralised_feedforward_maddpg_continous.py`. Alternatively, you can also run a specific system that is defined in the `Makefile`: ```bash diff --git a/examples/debugging_envs/run_decentralised_feedforward_discrete_mad4pg.py b/examples/debugging_envs/run_decentralised_feedforward_mad4pg_discrete.py similarity index 100% rename from examples/debugging_envs/run_decentralised_feedforward_discrete_mad4pg.py rename to examples/debugging_envs/run_decentralised_feedforward_mad4pg_discrete.py diff --git a/examples/debugging_envs/run_decentralised_feedforward_discrete_maddpg.py b/examples/debugging_envs/run_decentralised_feedforward_maddpg_discrete.py similarity index 100% rename from examples/debugging_envs/run_decentralised_feedforward_discrete_maddpg.py rename to examples/debugging_envs/run_decentralised_feedforward_maddpg_discrete.py diff --git a/examples/petting_zoo/run_decentralised_feedforward_maddpg.py b/examples/petting_zoo/run_decentralised_feedforward_maddpg_continous.py similarity index 98% rename from examples/petting_zoo/run_decentralised_feedforward_maddpg.py rename to examples/petting_zoo/run_decentralised_feedforward_maddpg_continous.py index 177dcc308..23435eb9b 100644 --- a/examples/petting_zoo/run_decentralised_feedforward_maddpg.py +++ b/examples/petting_zoo/run_decentralised_feedforward_maddpg_continous.py @@ -13,7 +13,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -"""Example running MADDPG on pettinzoo MPE environments.""" +"""Example running continous MADDPG on pettinzoo MPE environments.""" import functools from datetime import datetime diff --git a/examples/petting_zoo/run_decentralised_feedforward_maddpg_discrete.py b/examples/petting_zoo/run_decentralised_feedforward_maddpg_discrete.py new file mode 100644 index 000000000..01435143e --- /dev/null +++ b/examples/petting_zoo/run_decentralised_feedforward_maddpg_discrete.py @@ -0,0 +1,198 @@ +# python3 +# Copyright 2021 [...placeholder...]. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Example running discrete MADDPG on pettinzoo MPE environments.""" + +import functools +from datetime import datetime +from typing import Any, Dict, Mapping, Sequence, Union + +import launchpad as lp +import numpy as np +import sonnet as snt +import tensorflow as tf +from absl import app, flags +from acme import types +from acme.specs import BoundedArray, DiscreteArray +from acme.tf import networks +from acme.tf import utils as tf2_utils +from launchpad.nodes.python.local_multi_processing import PythonProcess + +from mava import specs as mava_specs +from mava.systems.tf import maddpg +from mava.utils import lp_utils +from mava.utils.environments import pettingzoo_utils +from mava.utils.loggers import logger_utils + +FLAGS = flags.FLAGS + +flags.DEFINE_string( + "env_class", + "mpe", + "Pettingzoo environment class, e.g. atari (str).", +) + +flags.DEFINE_string( + "env_name", + "simple_adversary_v2", + "Pettingzoo environment name, e.g. pong (str).", +) +flags.DEFINE_string( + "mava_id", + str(datetime.now()), + "Experiment identifier that can be used to continue experiments.", +) +flags.DEFINE_string("base_dir", "~/mava/", "Base dir to store experiments.") + + +def make_networks( + environment_spec: mava_specs.MAEnvironmentSpec, + policy_networks_layer_sizes: Union[Dict[str, Sequence], Sequence] = ( + 256, + 256, + 256, + ), + critic_networks_layer_sizes: Union[Dict[str, Sequence], Sequence] = (512, 512, 256), + shared_weights: bool = True, + sigma: float = 0.3, +) -> Mapping[str, types.TensorTransformation]: + """Creates networks used by the agents.""" + specs = environment_spec.get_agent_specs() + + # Create agent_type specs + if shared_weights: + type_specs = {key.split("_")[0]: specs[key] for key in specs.keys()} + specs = type_specs + + if isinstance(policy_networks_layer_sizes, Sequence): + policy_networks_layer_sizes = { + key: policy_networks_layer_sizes for key in specs.keys() + } + if isinstance(critic_networks_layer_sizes, Sequence): + critic_networks_layer_sizes = { + key: critic_networks_layer_sizes for key in specs.keys() + } + + observation_networks = {} + policy_networks = {} + critic_networks = {} + for key in specs.keys(): + # TODO (dries): Make specs[key].actions + # return a list of specs for hybrid action space + # Get total number of action dimensions from action spec. + agent_act_spec = specs[key].actions + if type(specs[key].actions) == DiscreteArray: + num_actions = agent_act_spec.num_values + minimum = [-1.0] * num_actions + maximum = [1.0] * num_actions + agent_act_spec = BoundedArray( + shape=(num_actions,), + minimum=minimum, + maximum=maximum, + dtype="float32", + name="actions", + ) + + num_dimensions = np.prod(agent_act_spec.shape, dtype=int) + # Create the shared observation network; here simply a state-less operation. + observation_network = tf2_utils.to_sonnet_module(tf.identity) + # Create the policy network. + policy_network = snt.Sequential( + [ + networks.LayerNormMLP( + policy_networks_layer_sizes[key], activate_final=True + ), + networks.NearZeroInitializedLinear(num_dimensions), + networks.TanhToSpec(agent_act_spec), + networks.ClippedGaussian(sigma), + networks.ClipToSpec(agent_act_spec), + ] + ) + + # Create the critic network. + critic_network = snt.Sequential( + [ + # The multiplexer concatenates the observations/actions. + networks.CriticMultiplexer(), + networks.LayerNormMLP( + critic_networks_layer_sizes[key], activate_final=False + ), + snt.Linear(1), + ] + ) + observation_networks[key] = observation_network + policy_networks[key] = policy_network + critic_networks[key] = critic_network + + return { + "policies": policy_networks, + "critics": critic_networks, + "observations": observation_networks, + } + + +def main(_: Any) -> None: + + environment_factory = functools.partial( + pettingzoo_utils.make_environment, + env_class=FLAGS.env_class, + env_name=FLAGS.env_name, + ) + + network_factory = lp_utils.partial_kwargs(make_networks) + + # Checkpointer appends "Checkpoints" to checkpoint_dir + checkpoint_dir = f"{FLAGS.base_dir}/{FLAGS.mava_id}" + + # loggers + log_every = 10 + logger_factory = functools.partial( + logger_utils.make_logger, + directory=FLAGS.base_dir, + to_terminal=True, + to_tensorboard=True, + time_stamp=FLAGS.mava_id, + time_delta=log_every, + ) + + program = maddpg.MADDPG( + environment_factory=environment_factory, + network_factory=network_factory, + logger_factory=logger_factory, + num_executors=2, + policy_optimizer=snt.optimizers.Adam(learning_rate=1e-4), + critic_optimizer=snt.optimizers.Adam(learning_rate=1e-4), + checkpoint_subpath=checkpoint_dir, + ).build() + + # Launch gpu config - let trainer use gpu. + gpu_id = -1 + env_vars = {"CUDA_VISIBLE_DEVICES": str(gpu_id)} + local_resources = { + "trainer": [], + "evaluator": PythonProcess(env=env_vars), + "executor": PythonProcess(env=env_vars), + } + + lp.launch( + program, + lp.LaunchType.LOCAL_MULTI_PROCESSING, + terminal="current_terminal", + local_resources=local_resources, + ) + + +if __name__ == "__main__": + app.run(main) diff --git a/mava/systems/tf/mappo/builder.py b/mava/systems/tf/mappo/builder.py index 431ecdeee..f3c7e9f6e 100644 --- a/mava/systems/tf/mappo/builder.py +++ b/mava/systems/tf/mappo/builder.py @@ -14,6 +14,7 @@ # limitations under the License. """MAPPO builder and config.""" +import copy import dataclasses from typing import Dict, Iterator, List, Optional, Type @@ -242,6 +243,13 @@ def make_trainer( policy_networks = networks["policies"] critic_networks = networks["critics"] + # Create optimizers for different agent types. + policy_optimizers: Dict[str, snt.Optimizer] = {} + critic_optimizers: Dict[str, snt.Optimizer] = {} + for agent in self._agent_types: + policy_optimizers[agent] = copy.deepcopy(self._config.policy_optimizer) + critic_optimizers[agent] = copy.deepcopy(self._config.critic_optimizer) + # The learner updates the parameters (and initializes them). trainer = self._trainer_fn( agents=agents, @@ -251,8 +259,8 @@ def make_trainer( critic_networks=critic_networks, dataset=dataset, shared_weights=shared_weights, - critic_optimizer=self._config.critic_optimizer, - policy_optimizer=self._config.policy_optimizer, + critic_optimizers=critic_optimizers, + policy_optimizers=policy_optimizers, discount=self._config.discount, lambda_gae=self._config.lambda_gae, entropy_cost=self._config.entropy_cost, diff --git a/mava/systems/tf/mappo/training.py b/mava/systems/tf/mappo/training.py index e0af4f36f..08b92049b 100644 --- a/mava/systems/tf/mappo/training.py +++ b/mava/systems/tf/mappo/training.py @@ -47,8 +47,8 @@ def __init__( policy_networks: Dict[str, snt.Module], critic_networks: Dict[str, snt.Module], dataset: tf.data.Dataset, - policy_optimizer: snt.Optimizer, - critic_optimizer: snt.Optimizer, + policy_optimizers: Dict[str, snt.Optimizer], + critic_optimizers: Dict[str, snt.Optimizer], shared_weights: bool, discount: float = 0.99, lambda_gae: float = 1.0, @@ -65,8 +65,8 @@ def __init__( Args: policy_networks: ... critic_networks: ... - policy_optimizer: policy optimizer, - critic_optimizer: critic optimizer, + policy_optimizers: policy optimizer, + critic_optimizers: critic optimizer, shared_weights: ... discount: discount to use for TD updates. dataset: dataset to learn from, whether fixed or from a replay buffer @@ -96,8 +96,8 @@ def __init__( self._critic_networks = critic_networks # Get optimizers - self._policy_optimizer = policy_optimizer - self._critic_optimizer = critic_optimizer + self._policy_optimizers = policy_optimizers + self._critic_optimizers = critic_optimizers # Dictionary with network keys for each agent. self.agent_net_keys = {agent: agent for agent in self._agents} @@ -156,8 +156,8 @@ def __init__( "policy": self._policy_networks[agent_key], "critic": self._critic_networks[agent_key], "observation": self._observation_networks[agent_key], - "policy_optimizer": self._policy_optimizer, - "critic_optimizer": self._critic_optimizer, + "policy_optimizers": self._policy_optimizers, + "critic_optimizers": self._critic_optimizers, } subdir = os.path.join("trainer", agent_key) @@ -255,9 +255,9 @@ def _forward(self, inputs: Any) -> None: discount = discount[:-1] # Get agent network - network_key = agent.split("_")[0] if self._shared_weights else agent - policy_network = self._policy_networks[network_key] - critic_network = self._critic_networks[network_key] + agent_key = agent.split("_")[0] if self._shared_weights else agent + policy_network = self._policy_networks[agent_key] + critic_network = self._critic_networks[agent_key] # Reshape inputs. dims = actor_observation.shape[:2] @@ -342,15 +342,15 @@ def _backward(self) -> None: tape = self.tape for agent in self._agents: - # Get network_key. - network_key = agent.split("_")[0] if self._shared_weights else agent + # Get agent_key. + agent_key = agent.split("_")[0] if self._shared_weights else agent # Get trainable variables. policy_variables = ( - self._observation_networks[network_key].trainable_variables - + self._policy_networks[network_key].trainable_variables + self._observation_networks[agent_key].trainable_variables + + self._policy_networks[agent_key].trainable_variables ) - critic_variables = self._critic_networks[network_key].trainable_variables + critic_variables = self._critic_networks[agent_key].trainable_variables # Get gradients. policy_gradients = tape.gradient(policy_losses[agent], policy_variables) @@ -365,8 +365,8 @@ def _backward(self) -> None: ) # Apply gradients. - self._critic_optimizer.apply(critic_grads, critic_variables) - self._policy_optimizer.apply(policy_grads, policy_variables) + self._critic_optimizers[agent_key].apply(critic_grads, critic_variables) + self._policy_optimizers[agent_key].apply(policy_grads, policy_variables) train_utils.safe_del(self, "tape") @@ -412,8 +412,8 @@ def __init__( policy_networks: Dict[str, snt.Module], critic_networks: Dict[str, snt.Module], dataset: tf.data.Dataset, - policy_optimizer: snt.Optimizer, - critic_optimizer: snt.Optimizer, + policy_optimizers: Dict[str, snt.Optimizer], + critic_optimizers: Dict[str, snt.Optimizer], shared_weights: bool, discount: float = 0.99, lambda_gae: float = 1.0, @@ -435,8 +435,8 @@ def __init__( observation_networks=observation_networks, dataset=dataset, shared_weights=shared_weights, - policy_optimizer=policy_optimizer, - critic_optimizer=critic_optimizer, + policy_optimizers=policy_optimizers, + critic_optimizers=critic_optimizers, discount=discount, lambda_gae=lambda_gae, entropy_cost=entropy_cost, From 0e775345abe41225e09c2fe2f3227fbac4e2f808 Mon Sep 17 00:00:00 2001 From: Kale-ab Date: Mon, 24 May 2021 17:40:21 +0200 Subject: [PATCH 064/188] chore: Updated example env. --- .../run_decentralised_feedforward_maddpg_discrete.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/petting_zoo/run_decentralised_feedforward_maddpg_discrete.py b/examples/petting_zoo/run_decentralised_feedforward_maddpg_discrete.py index 01435143e..0917f4240 100644 --- a/examples/petting_zoo/run_decentralised_feedforward_maddpg_discrete.py +++ b/examples/petting_zoo/run_decentralised_feedforward_maddpg_discrete.py @@ -46,7 +46,7 @@ flags.DEFINE_string( "env_name", - "simple_adversary_v2", + "simple_speaker_listener_v3", "Pettingzoo environment name, e.g. pong (str).", ) flags.DEFINE_string( From 4cc601ef99bf439bddf440fe473d229b748d7326 Mon Sep 17 00:00:00 2001 From: Claude Formanek Date: Mon, 24 May 2021 17:50:36 +0200 Subject: [PATCH 065/188] VDN works --- .../debugging_envs/run_feedforward_qmix_v2.py | 175 +++++++ mava/components/tf/networks/mixers.py | 63 +++ mava/systems/tf/qmixV2/README.md | 0 mava/systems/tf/qmixV2/__init__.py | 19 + mava/systems/tf/qmixV2/builder.py | 327 +++++++++++++ mava/systems/tf/qmixV2/execution.py | 283 ++++++++++++ mava/systems/tf/qmixV2/system.py | 437 ++++++++++++++++++ mava/systems/tf/qmixV2/training.py | 367 +++++++++++++++ mava/wrappers/__init__.py | 4 +- mava/wrappers/smac.py | 5 +- 10 files changed, 1678 insertions(+), 2 deletions(-) create mode 100644 examples/debugging_envs/run_feedforward_qmix_v2.py create mode 100644 mava/components/tf/networks/mixers.py create mode 100644 mava/systems/tf/qmixV2/README.md create mode 100644 mava/systems/tf/qmixV2/__init__.py create mode 100644 mava/systems/tf/qmixV2/builder.py create mode 100644 mava/systems/tf/qmixV2/execution.py create mode 100644 mava/systems/tf/qmixV2/system.py create mode 100644 mava/systems/tf/qmixV2/training.py diff --git a/examples/debugging_envs/run_feedforward_qmix_v2.py b/examples/debugging_envs/run_feedforward_qmix_v2.py new file mode 100644 index 000000000..b4bb850b5 --- /dev/null +++ b/examples/debugging_envs/run_feedforward_qmix_v2.py @@ -0,0 +1,175 @@ +# python3 +# Copyright 2021 [...placeholder...]. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import functools +from datetime import datetime +from typing import Any, Dict, Mapping, Optional, Sequence, Union + +import launchpad as lp +import sonnet as snt +import tensorflow as tf +from absl import app, flags +from acme import types +from acme.tf import networks +from launchpad.nodes.python.local_multi_processing import PythonProcess + +from mava import specs as mava_specs +from mava.components.tf.modules.exploration import LinearExplorationScheduler +from mava.components.tf.networks import epsilon_greedy_action_selector +from mava.components.tf.networks.mixers import QMixer +from mava.systems.tf import qmixV2 +from mava.utils import lp_utils +from mava.utils.environments import debugging_utils +from mava.utils.loggers import logger_utils + +FLAGS = flags.FLAGS +flags.DEFINE_string( + "env_name", + "simple_spread", + "Debugging environment name (str).", +) +flags.DEFINE_string( + "action_space", + "discrete", + "Environment action space type (str).", +) + +flags.DEFINE_string( + "mava_id", + str(datetime.now()), + "Experiment identifier that can be used to continue experiments.", +) +flags.DEFINE_string("base_dir", "./logs/", "Base dir to store experiments.") + + +def make_networks( + environment_spec: mava_specs.MAEnvironmentSpec, + q_networks_layer_sizes: Union[Dict[str, Sequence], Sequence] = ( + 512, + 512, + 256, + ), + shared_weights: bool = True, +) -> Mapping[str, types.TensorTransformation]: + """Creates networks used by the agents.""" + + specs = environment_spec.get_agent_specs() + + # Create agent_type specs + if shared_weights: + type_specs = {key.split("_")[0]: specs[key] for key in specs.keys()} + specs = type_specs + + if isinstance(q_networks_layer_sizes, Sequence): + q_networks_layer_sizes = {key: q_networks_layer_sizes for key in specs.keys()} + + def action_selector_fn( + q_values: types.NestedTensor, + legal_actions: types.NestedTensor, + epsilon: Optional[tf.Variable] = None, + ) -> types.NestedTensor: + return epsilon_greedy_action_selector( + action_values=q_values, legal_actions_mask=legal_actions, epsilon=epsilon + ) + + q_networks = {} + action_selectors = {} + for key in specs.keys(): + + # Get total number of action dimensions from action spec. + num_dimensions = specs[key].actions.num_values + + # Create the policy network. + q_network = snt.Sequential( + [ + networks.LayerNormMLP( + q_networks_layer_sizes[key], activate_final=False + ), + networks.NearZeroInitializedLinear(num_dimensions), + ] + ) + + # epsilon greedy action selector + action_selector = action_selector_fn + + q_networks[key] = q_network + action_selectors[key] = action_selector + + return { + "q_networks": q_networks, + "action_selectors": action_selectors, + } + + +def main(_: Any) -> None: + + # environment + environment_factory = functools.partial( + debugging_utils.make_environment, + env_name=FLAGS.env_name, + action_space=FLAGS.action_space, + num_agents=3, + return_state_info=True, + ) + + # networks + network_factory = lp_utils.partial_kwargs(make_networks) + + # Checkpointer appends "Checkpoints" to checkpoint_dir + checkpoint_dir = f"{FLAGS.base_dir}/{FLAGS.mava_id}" + + # loggers + log_every = 10 + logger_factory = functools.partial( + logger_utils.make_logger, + directory=FLAGS.base_dir, + to_terminal=True, + to_tensorboard=True, + time_stamp=FLAGS.mava_id, + time_delta=log_every, + ) + + # distributed program + program = qmixV2.QMix( + environment_factory=environment_factory, + network_factory=network_factory, + logger_factory=logger_factory, + num_executors=2, + exploration_scheduler_fn=LinearExplorationScheduler, + epsilon_min=0.05, + epsilon_decay=5e-4, + mixer_fn=QMixer, + optimizer=snt.optimizers.Adam(learning_rate=1e-4), + checkpoint_subpath=checkpoint_dir, + ).build() + + # launch + gpu_id = -1 + env_vars = {"CUDA_VISIBLE_DEVICES": str(gpu_id)} + local_resources = { + "trainer": [], + "evaluator": PythonProcess(env=env_vars), + "executor": PythonProcess(env=env_vars), + } + lp.launch( + program, + lp.LaunchType.LOCAL_MULTI_PROCESSING, + terminal="current_terminal", + local_resources=local_resources, + ) + + +if __name__ == "__main__": + app.run(main) diff --git a/mava/components/tf/networks/mixers.py b/mava/components/tf/networks/mixers.py new file mode 100644 index 000000000..b9aab0753 --- /dev/null +++ b/mava/components/tf/networks/mixers.py @@ -0,0 +1,63 @@ +from typing import Any + +import numpy as np +import sonnet as snt +import tensorflow as tf +from tensorflow import keras + + +class AdditiveMixingNetwork(snt.Module): + """Multi-agent monotonic mixing architecture.""" + + def __init__(self) -> None: + """Initializes the mixer.""" + super(AdditiveMixingNetwork, self).__init__() + + def __call__(self, q_values: tf.Tensor) -> tf.Tensor: + """Monotonic mixing logic.""" + return tf.math.reduce_sum(q_values, keepdims=True) + + +class QMixer(snt.Module): + def __init__(self, n_agents: Any, state_shape: Any, mixing_embed_dim: Any) -> None: + super().__init__() + self.n_agents = n_agents + self.embed_dim = mixing_embed_dim + self.state_dim = int(np.prod(state_shape)) + + self.hyper_w_1 = snt.Linear(self.embed_dim * self.n_agents) + self.hyper_w_final = snt.Linear(self.embed_dim) + + # State dependent bias for hidden layer + self.hyper_b_1 = snt.Linear(self.embed_dim) + + # V(s) instead of a bias for the last layers + self.V = snt.Sequential( + [snt.Linear(self.embed_dim), tf.keras.layers.ReLU(), snt.Linear(1)] + ) + + def __call__(self, agent_qs: Any, states: Any) -> tf.Tensor: + """Forward pass for the mixer. + Args: + agent_qs: Tensor of shape [B, n_agents] + states: Tensor of shape [B, state_dim] + """ + bs = agent_qs.shape[0] + states = tf.reshape(states, (-1, self.state_dim)) + agent_qs = tf.reshape(agent_qs, (-1, 1, self.n_agents)) + # First layer + w1 = tf.math.abs(self.hyper_w_1(states)) + b1 = self.hyper_b_1(states) + w1 = tf.reshape(w1, (-1, self.n_agents, self.embed_dim)) + b1 = tf.reshape(b1, (-1, 1, self.embed_dim)) + hidden = tf.keras.activations.elu(tf.linalg.matmul(agent_qs, w1) + b1) + # Second layer + w_final = tf.math.abs(self.hyper_w_final(states)) + w_final = tf.reshape(w_final, (-1, self.embed_dim, 1)) + # State-dependent bias + v = tf.reshape(self.V(states), (-1, 1, 1)) + # Compute final output + y = tf.linalg.matmul(hidden, w_final) + v + # Reshape and return + q_tot = tf.reshape(y, (bs, -1, 1)) + return q_tot diff --git a/mava/systems/tf/qmixV2/README.md b/mava/systems/tf/qmixV2/README.md new file mode 100644 index 000000000..e69de29bb diff --git a/mava/systems/tf/qmixV2/__init__.py b/mava/systems/tf/qmixV2/__init__.py new file mode 100644 index 000000000..a74c659df --- /dev/null +++ b/mava/systems/tf/qmixV2/__init__.py @@ -0,0 +1,19 @@ +# python3 +# Copyright 2021 [...placeholder...]. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Implementations of a IDQN agent.""" + +from mava.systems.tf.qmixV2.system import QMix +from mava.systems.tf.qmixV2.training import QMixTrainer diff --git a/mava/systems/tf/qmixV2/builder.py b/mava/systems/tf/qmixV2/builder.py new file mode 100644 index 000000000..27f86c595 --- /dev/null +++ b/mava/systems/tf/qmixV2/builder.py @@ -0,0 +1,327 @@ +# python3 +# Copyright 2021 [...placeholder...]. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import dataclasses +from typing import Any, Dict, Iterator, List, Optional, Type + +import numpy as np +import reverb +import sonnet as snt +import tensorflow as tf +from acme import datasets +from acme.tf import utils as tf_utils +from acme.tf import variable_utils +from acme.utils import counting + +from mava import adders, core, specs, types +from mava.adders import reverb as reverb_adders +from mava.components.tf.modules.exploration.exploration_scheduling import ( + LinearExplorationScheduler, +) +from mava.components.tf.modules.stabilising import FingerPrintStabalisation +from mava.systems.tf import executors +from mava.systems.tf.qmixV2 import execution, training +from mava.wrappers import DetailedTrainerStatisticsWithEpsilon + + +@dataclasses.dataclass +class QMixConfig: + """Configuration options for the MADDPG system. + Args: + environment_spec: description of the actions, observations, etc. + discount: discount to use for TD updates. + batch_size: batch size for updates. + prefetch_size: size to prefetch from replay. + target_update_period: number of learner steps to perform before updating + the target networks. + min_replay_size: minimum replay size before updating. + max_replay_size: maximum replay size. + samples_per_insert: number of samples to take from replay for every insert + that is made. + n_step: number of steps to squash into a single transition. + sigma: standard deviation of zero-mean, Gaussian exploration noise. + clipping: whether to clip gradients by global norm. + replay_table_name: string indicating what name to give the replay table.""" + + environment_spec: specs.MAEnvironmentSpec + epsilon_min: float + epsilon_decay: float + shared_weights: bool + target_update_period: int + executor_variable_update_period: int + clipping: bool + min_replay_size: int + max_replay_size: int + samples_per_insert: Optional[float] + prefetch_size: int + batch_size: int + n_step: int + sequence_length: int + period: int + discount: float + checkpoint: bool + optimizer: snt.Optimizer + replay_table_name: str = reverb_adders.DEFAULT_PRIORITY_TABLE + checkpoint_subpath: str = "~/mava/" + + +class QMixBuilder: + """Builder for MADQN which constructs individual components of the system.""" + + """Defines an interface for defining the components of an RL system. + Implementations of this interface contain a complete specification of a + concrete RL system. An instance of this class can be used to build an + RL system which interacts with the environment either locally or in a + distributed setup. + """ + + def __init__( + self, + config: QMixConfig, + trainer_fn: Type[training.QMixTrainer] = training.QMixTrainer, + executor_fn: Type[core.Executor] = execution.QMixFeedForwardExecutor, + extra_specs: Dict[str, Any] = {}, + exploration_scheduler_fn: Type[ + LinearExplorationScheduler + ] = LinearExplorationScheduler, + replay_stabilisation_fn: Optional[Type[FingerPrintStabalisation]] = None, + mixer_fn: Optional[Type[snt.Module]] = None, + ): + """Args: + _config: Configuration options for the MADQN system. + _trainer_fn: Trainer module to use.""" + self._config = config + self._extra_specs = extra_specs + + self._agents = self._config.environment_spec.get_agent_ids() + self._agent_types = self._config.environment_spec.get_agent_types() + self._trainer_fn = trainer_fn + self._executor_fn = executor_fn + self._exploration_scheduler_fn = exploration_scheduler_fn + self._replay_stabiliser_fn = replay_stabilisation_fn + self._mixer_fn = mixer_fn + + def make_replay_tables( + self, + environment_spec: specs.MAEnvironmentSpec, + ) -> List[reverb.Table]: + """Create tables to insert data into.""" + + # Select adder + if issubclass(self._executor_fn, executors.FeedForwardExecutor): + # Check if we should use fingerprints + if self._replay_stabiliser_fn is not None: + self._extra_specs.update({"fingerprint": np.array([1.0, 1.0])}) + adder_sig = reverb_adders.ParallelNStepTransitionAdder.signature( + environment_spec, self._extra_specs + ) + elif issubclass(self._executor_fn, executors.RecurrentExecutor): + adder_sig = reverb_adders.ParallelSequenceAdder.signature( + environment_spec, self._extra_specs + ) + else: + raise NotImplementedError("Unknown executor type: ", self._executor_fn) + + if self._config.samples_per_insert is None: + # We will take a samples_per_insert ratio of None to mean that there is + # no limit, i.e. this only implies a min size limit. + limiter = reverb.rate_limiters.MinSize(self._config.min_replay_size) + + else: + # Create enough of an error buffer to give a 10% tolerance in rate. + samples_per_insert_tolerance = 0.1 * self._config.samples_per_insert + error_buffer = self._config.min_replay_size * samples_per_insert_tolerance + limiter = reverb.rate_limiters.SampleToInsertRatio( + min_size_to_sample=self._config.min_replay_size, + samples_per_insert=self._config.samples_per_insert, + error_buffer=error_buffer, + ) + + replay_table = reverb.Table( + name=self._config.replay_table_name, + sampler=reverb.selectors.Uniform(), + remover=reverb.selectors.Fifo(), + max_size=self._config.max_replay_size, + rate_limiter=limiter, + signature=adder_sig, + ) + + return [replay_table] + + def make_dataset_iterator( + self, replay_client: reverb.Client + ) -> Iterator[reverb.ReplaySample]: + """Create a dataset iterator to use for learning/updating the system. + Args: + replay_client: Reverb Client which points to the replay server.""" + + sequence_length = ( + self._config.sequence_length + if issubclass(self._executor_fn, executors.RecurrentExecutor) + else None + ) + + dataset = datasets.make_reverb_dataset( + table=self._config.replay_table_name, + server_address=replay_client.server_address, + batch_size=self._config.batch_size, + prefetch_size=self._config.prefetch_size, + sequence_length=sequence_length, + ) + return iter(dataset) + + def make_adder( + self, replay_client: reverb.Client + ) -> Optional[adders.ParallelAdder]: + """Create an adder which records data generated by the executor/environment. + Args: + replay_client: Reverb Client which points to the replay server.""" + + # Select adder + if issubclass(self._executor_fn, executors.FeedForwardExecutor): + adder = reverb_adders.ParallelNStepTransitionAdder( + priority_fns=None, + client=replay_client, + n_step=self._config.n_step, + discount=self._config.discount, + ) + elif issubclass(self._executor_fn, executors.RecurrentExecutor): + adder = reverb_adders.ParallelSequenceAdder( + priority_fns=None, + client=replay_client, + sequence_length=self._config.sequence_length, + period=self._config.period, + ) + else: + raise NotImplementedError("Unknown executor type: ", self._executor_fn) + + return adder + + def make_executor( + self, + q_networks: Dict[str, snt.Module], + action_selectors: Dict[str, Any], + adder: Optional[adders.ParallelAdder] = None, + variable_source: Optional[core.VariableSource] = None, + trainer: Optional[training.QMixTrainer] = None, + evaluator: bool = False, + ) -> core.Executor: + """Create an executor instance. + Args: + q_networks: A struct of instance of all + the different system q networks, + this should be a callable which takes as input observations + and returns actions. + adder: How data is recorded (e.g. added to replay). + variable_source: collection of (nested) numpy arrays. Contains + source variables as defined in mava.core. + """ + + shared_weights = self._config.shared_weights + + variable_client = None + if variable_source: + agent_keys = self._agent_types if shared_weights else self._agents + + # Create policy variables + variables = {agent: q_networks[agent].variables for agent in agent_keys} + # Get new policy variables + variable_client = variable_utils.VariableClient( + client=variable_source, + variables={"q_network": variables}, + update_period=self._config.executor_variable_update_period, + ) + + # Make sure not to use a random policy after checkpoint restoration by + # assigning variables before running the environment loop. + variable_client.update_and_wait() + + # Check if we should use fingerprints + fingerprint = True if self._replay_stabiliser_fn is not None else False + + # Create the executor which coordinates the actors. + return self._executor_fn( + q_networks=q_networks, + action_selectors=action_selectors, + shared_weights=shared_weights, + variable_client=variable_client, + adder=adder, + trainer=trainer, + evaluator=evaluator, + fingerprint=fingerprint, + ) + + def make_trainer( + self, + networks: Dict[str, Dict[str, snt.Module]], + dataset: Iterator[reverb.ReplaySample], + counter: Optional[counting.Counter] = None, + logger: Optional[types.NestedLogger] = None, + ) -> core.Trainer: + """Creates an instance of the trainer. + Args: + networks: struct describing the networks needed by the trainer; this can + be specific to the trainer in question. + dataset: iterator over samples from replay. + counter: a Counter which allows for recording of counts (trainer steps, + executor steps, etc.) distributed throughout the system. + logger: Logger object for logging metadata. + checkpoint: bool controlling whether the trainer checkpoints itself. + """ + q_networks = networks["values"] + target_q_networks = networks["target_values"] + + agents = self._config.environment_spec.get_agent_ids() + agent_types = self._config.environment_spec.get_agent_types() + + # Make epsilon scheduler + exploration_scheduler = self._exploration_scheduler_fn( + epsilon_min=self._config.epsilon_min, + epsilon_decay=self._config.epsilon_decay, + ) + + # Check if we should use fingerprints + fingerprint = True if self._replay_stabiliser_fn is not None else False + + # Mixer + if self._mixer_fn: + state_spec = self._config.environment_spec.get_extra_specs()["s_t"] + mixer = self._mixer_fn(len(agents), state_spec.shape, 256) + tf_utils.create_variables(mixer, [tf.ones(len(agents)), state_spec]) + + # The learner updates the parameters (and initializes them). + trainer = self._trainer_fn( + agents=agents, + agent_types=agent_types, + discount=self._config.discount, + q_networks=q_networks, + target_q_networks=target_q_networks, + shared_weights=self._config.shared_weights, + optimizer=self._config.optimizer, + target_update_period=self._config.target_update_period, + clipping=self._config.clipping, + exploration_scheduler=exploration_scheduler, + dataset=dataset, + counter=counter, + fingerprint=fingerprint, + logger=logger, + mixer=mixer, + checkpoint=self._config.checkpoint, + checkpoint_subpath=self._config.checkpoint_subpath, + ) + + trainer = DetailedTrainerStatisticsWithEpsilon(trainer) # type:ignore + + return trainer diff --git a/mava/systems/tf/qmixV2/execution.py b/mava/systems/tf/qmixV2/execution.py new file mode 100644 index 000000000..fd4041306 --- /dev/null +++ b/mava/systems/tf/qmixV2/execution.py @@ -0,0 +1,283 @@ +# python3 +# Copyright 2021 [...placeholder...]. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +"""Executor implementations for MADQN.""" + +from typing import Any, Dict, Optional + +import dm_env +import numpy as np +import sonnet as snt +import tensorflow as tf +from acme import types + +# Internal imports. +from acme.tf import utils as tf2_utils +from acme.tf import variable_utils as tf2_variable_utils + +from mava import adders +from mava.systems.tf.executors import FeedForwardExecutor, RecurrentExecutor +from mava.systems.tf.qmixV2.training import QMixTrainer +from mava.types import OLT + + +class QMixFeedForwardExecutor(FeedForwardExecutor): + """A feed-forward executor. + An executor based on a feed-forward policy for each agent in the system + which takes non-batched observations and outputs non-batched actions. + It also allows adding experiences to replay and updating the weights + from the policy on the learner. + """ + + def __init__( + self, + q_networks: Dict[str, snt.Module], + action_selectors: Dict[str, snt.Module], + trainer: QMixTrainer, + shared_weights: bool = True, + adder: Optional[adders.ParallelAdder] = None, + variable_client: Optional[tf2_variable_utils.VariableClient] = None, + fingerprint: bool = False, + evaluator: bool = False, + ): + """Initializes the executor. + Args: + policy_network: the policy to run for each agent in the system. + shared_weights: specify if weights are shared between agent networks. + adder: the adder object to which allows to add experiences to a + dataset/replay buffer. + variable_client: object which allows to copy weights from the trainer copy + of the policies to the executor copy (in case they are separate). + """ + + # Store these for later use. + self._adder = adder + self._variable_client = variable_client + self._q_networks = q_networks + self._action_selectors = action_selectors + self._trainer = trainer + self._shared_weights = shared_weights + self._fingerprint = fingerprint + self._evaluator = evaluator + + @tf.function + def _policy( + self, + agent: str, + observation: types.NestedTensor, + legal_actions: types.NestedTensor, + epsilon: tf.Tensor, + fingerprint: Optional[tf.Tensor] = None, + ) -> types.NestedTensor: + + # Add a dummy batch dimension and as a side effect convert numpy to TF. + batched_observation = tf2_utils.add_batch_dim(observation) + batched_legals = tf2_utils.add_batch_dim(legal_actions) + + # index network either on agent type or on agent id + agent_key = agent.split("_")[0] if self._shared_weights else agent + + # Compute the policy, conditioned on the observation and + # possibly the fingerprint. + if fingerprint is not None: + q_values = self._q_networks[agent_key](batched_observation, fingerprint) + else: + q_values = self._q_networks[agent_key](batched_observation) + + # select legal action + action = self._action_selectors[agent_key]( + q_values, batched_legals, epsilon=epsilon + ) + + return action + + def select_action( + self, agent: str, observation: types.NestedArray + ) -> types.NestedArray: + raise NotImplementedError + + def observe_first( + self, + timestep: dm_env.TimeStep, + extras: Dict[str, types.NestedArray] = {}, + ) -> None: + + if self._fingerprint and self._trainer is not None: + epsilon = self._trainer.get_epsilon() + trainer_step = self._trainer.get_trainer_steps() + fingerprint = np.array([epsilon, trainer_step]) + extras.update({"fingerprint": fingerprint}) + + if self._adder: + self._adder.add_first(timestep, extras) + + def observe( + self, + actions: Dict[str, types.NestedArray], + next_timestep: dm_env.TimeStep, + next_extras: Dict[str, types.NestedArray] = {}, + ) -> None: + if self._fingerprint and self._trainer is not None: + trainer_step = self._trainer.get_trainer_steps() + epsilon = self._trainer.get_epsilon() + fingerprint = np.array([epsilon, trainer_step]) + next_extras.update({"fingerprint": fingerprint}) + + if self._adder: + self._adder.add(actions, next_timestep, next_extras) + + def select_actions( + self, observations: Dict[str, OLT] + ) -> Dict[str, types.NestedArray]: + actions = {} + for agent, observation in observations.items(): + # Pass the observation through the policy network. + if not self._evaluator: + epsilon = self._trainer.get_epsilon() + else: + epsilon = 0.0 + + epsilon = tf.convert_to_tensor(epsilon) + + if self._fingerprint: + trainer_step = self._trainer.get_trainer_steps() + fingerprint = tf.concat([epsilon, trainer_step], axis=0) + fingerprint = tf.expand_dims(fingerprint, axis=0) + fingerprint = tf.cast(fingerprint, "float32") + else: + fingerprint = None + + action = self._policy( + agent, + observation.observation, + observation.legal_actions, + epsilon, + fingerprint, + ) + + actions[agent] = tf2_utils.to_numpy_squeeze(action) + + # Return a numpy array with squeezed out batch dimension. + return actions + + def update(self, wait: bool = False) -> None: + if self._variable_client: + self._variable_client.update(wait) + + +class MADQNRecurrentExecutor(RecurrentExecutor): + """A feed-forward executor. + An executor based on a feed-forward policy for each agent in the system + which takes non-batched observations and outputs non-batched actions. + It also allows adding experiences to replay and updating the weights + from the policy on the learner. + """ + + def __init__( + self, + q_networks: Dict[str, snt.Module], + action_selectors: Dict[str, snt.Module], + shared_weights: bool = True, + adder: Optional[adders.ParallelAdder] = None, + variable_client: Optional[tf2_variable_utils.VariableClient] = None, + store_recurrent_state: bool = True, + trainer: QMixTrainer = None, + evaluator: bool = False, + fingerprint: bool = False, + ): + """Initializes the executor. + Args: + policy_network: the policy to run for each agent in the system. + shared_weights: specify if weights are shared between agent networks. + adder: the adder object to which allows to add experiences to a + dataset/replay buffer. + variable_client: object which allows to copy weights from the trainer copy + of the policies to the executor copy (in case they are separate). + """ + + # Store these for later use. + self._adder = adder + self._variable_client = variable_client + self._q_networks = q_networks + self._policy_networks = q_networks + self._action_selectors = action_selectors + self._store_recurrent_state = store_recurrent_state + self._trainer = trainer + self._shared_weights = shared_weights + + self._states: Dict[str, Any] = {} + + @tf.function + def _policy( + self, + agent: str, + observation: types.NestedTensor, + state: types.NestedTensor, + legal_actions: types.NestedTensor, + epsilon: tf.Tensor, + ) -> types.NestedTensor: + + # Add a dummy batch dimension and as a side effect convert numpy to TF. + batched_observation = tf2_utils.add_batch_dim(observation) + batched_legals = tf2_utils.add_batch_dim(legal_actions) + + # index network either on agent type or on agent id + agent_key = agent.split("_")[0] if self._shared_weights else agent + + # Compute the policy, conditioned on the observation. + q_values, new_state = self._q_networks[agent_key](batched_observation, state) + + # select legal action + action = self._action_selectors[agent_key]( + q_values, batched_legals, epsilon=epsilon + ) + + return action, new_state + + def select_action( + self, agent: str, observation: types.NestedArray + ) -> types.NestedArray: + raise NotImplementedError + + def select_actions( + self, observations: Dict[str, OLT] + ) -> Dict[str, types.NestedArray]: + actions = {} + + for agent, observation in observations.items(): + + # Pass the observation through the policy network. + if self._trainer is not None: + epsilon = self._trainer.get_epsilon() + else: + epsilon = 0.0 + + epsilon = tf.convert_to_tensor(epsilon) + + policy_output, new_state = self._policy( + agent, + observation.observation, + self._states[agent], + observation.legal_actions, + epsilon, + ) + + self._states[agent] = new_state + + actions[agent] = tf2_utils.to_numpy_squeeze(policy_output) + + # Return a numpy array with squeezed out batch dimension. + return actions diff --git a/mava/systems/tf/qmixV2/system.py b/mava/systems/tf/qmixV2/system.py new file mode 100644 index 000000000..06097509e --- /dev/null +++ b/mava/systems/tf/qmixV2/system.py @@ -0,0 +1,437 @@ +# python3 +# Copyright 2021 [...placeholder...]. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Defines the MADQN system class.""" +import functools +from typing import Any, Callable, Dict, Optional, Type, Union + +import acme +import dm_env +import launchpad as lp +import reverb +import sonnet as snt +from acme import specs as acme_specs +from acme.tf import utils as tf2_utils +from acme.utils import counting + +import mava +from mava import core +from mava import specs as mava_specs +from mava.components.tf.architectures import DecentralisedValueActor +from mava.components.tf.modules.exploration import LinearExplorationScheduler +from mava.components.tf.modules.stabilising import FingerPrintStabalisation +from mava.environment_loop import ParallelEnvironmentLoop +from mava.systems.tf import executors +from mava.systems.tf import savers as tf2_savers +from mava.systems.tf.qmixV2 import builder, execution, training +from mava.utils import lp_utils +from mava.utils.loggers import MavaLogger, logger_utils +from mava.wrappers import DetailedPerAgentStatistics + + +class QMix: + """MADQN system. + This implements a single-process QMIX system. + Args: + environment_factory: Callable to instantiate an environment on a compute node. + network_factory: Callable to instantiate system networks on a compute node. + logger_factory: Callable to instantiate a system logger on a compute node. + architecture: system architecture, e.g. decentralised or centralised. + trainer_fn: training type associated with executor and architecture, + e.g. centralised training. + executor_fn: executor type for example feedforward or recurrent. + num_executors: number of executor processes to run in parallel. + num_caches: number of trainer node caches. + environment_spec: description of the actions, observations, etc. + q_networks: the online Q network (the one being optimized) + epsilon: probability of taking a random action; ignored if a policy + network is given. + trainer_fn: the class used for training the agent and mixing networks. + shared_weights: boolean determining whether shared weights is used. + target_update_period: number of learner steps to perform before updating + the target networks. + clipping: whether to clip gradients by global norm. + replay_table_name: string indicating what name to give the replay table. + max_replay_size: maximum replay size. + samples_per_insert: number of samples to take from replay for every insert + that is made. + prefetch_size: size to prefetch from replay. + batch_size: batch size for updates. + n_step: number of steps to squash into a single transition. + discount: discount to use for TD updates. + counter: counter object used to keep track of steps. + checkpoint: boolean indicating whether to checkpoint the learner. + """ + + def __init__( + self, + environment_factory: Callable[[bool], dm_env.Environment], + network_factory: Callable[[acme_specs.BoundedArray], Dict[str, snt.Module]], + logger_factory: Callable[[str], MavaLogger] = None, + architecture: Type[DecentralisedValueActor] = DecentralisedValueActor, + trainer_fn: Type[training.QMixTrainer] = training.QMixTrainer, + executor_fn: Type[core.Executor] = execution.QMixFeedForwardExecutor, + exploration_scheduler_fn: Type[ + LinearExplorationScheduler + ] = LinearExplorationScheduler, + replay_stabilisation_fn: Optional[Type[FingerPrintStabalisation]] = None, + mixer_fn: Optional[Type[snt.Module]] = None, + epsilon_min: float = 0.05, + epsilon_decay: float = 1e-4, + num_executors: int = 1, + num_caches: int = 0, + environment_spec: mava_specs.MAEnvironmentSpec = None, + shared_weights: bool = True, + batch_size: int = 256, + prefetch_size: int = 4, + min_replay_size: int = 1000, + max_replay_size: int = 1000000, + samples_per_insert: Optional[float] = 32.0, + n_step: int = 5, + sequence_length: int = 20, + period: int = 20, + clipping: bool = True, + discount: float = 0.99, + optimizer: snt.Optimizer = snt.optimizers.Adam(learning_rate=1e-4), + target_update_period: int = 100, + executor_variable_update_period: int = 1000, + max_executor_steps: int = None, + checkpoint: bool = True, + checkpoint_subpath: str = "~/mava/", + logger_config: Dict = {}, + train_loop_fn: Callable = ParallelEnvironmentLoop, + eval_loop_fn: Callable = ParallelEnvironmentLoop, + train_loop_fn_kwargs: Dict = {}, + eval_loop_fn_kwargs: Dict = {}, + ): + + if not environment_spec: + environment_spec = mava_specs.MAEnvironmentSpec( + environment_factory(evaluation=False) # type:ignore + ) + + # set default logger if no logger provided + if not logger_factory: + logger_factory = functools.partial( + logger_utils.make_logger, + directory="~/mava", + to_terminal=True, + time_delta=10, + ) + + self._architecture = architecture + self._environment_factory = environment_factory + self._network_factory = network_factory + self._logger_factory = logger_factory + self._environment_spec = environment_spec + self._shared_weights = shared_weights + self._num_exectors = num_executors + self._num_caches = num_caches + self._max_executor_steps = max_executor_steps + self._checkpoint_subpath = checkpoint_subpath + self._checkpoint = checkpoint + self._logger_config = logger_config + self._train_loop_fn = train_loop_fn + self._train_loop_fn_kwargs = train_loop_fn_kwargs + self._eval_loop_fn = eval_loop_fn + self._eval_loop_fn_kwargs = eval_loop_fn_kwargs + + if issubclass(executor_fn, executors.RecurrentExecutor): + extra_specs = self._get_extra_specs() + else: + extra_specs = {} + + self._builder = builder.QMixBuilder( + builder.QMixConfig( + environment_spec=environment_spec, + epsilon_min=epsilon_min, + epsilon_decay=epsilon_decay, + shared_weights=shared_weights, + discount=discount, + batch_size=batch_size, + prefetch_size=prefetch_size, + target_update_period=target_update_period, + executor_variable_update_period=executor_variable_update_period, + min_replay_size=min_replay_size, + max_replay_size=max_replay_size, + samples_per_insert=samples_per_insert, + n_step=n_step, + sequence_length=sequence_length, + period=period, + clipping=clipping, + checkpoint=checkpoint, + optimizer=optimizer, + checkpoint_subpath=checkpoint_subpath, + ), + trainer_fn=trainer_fn, + executor_fn=executor_fn, + extra_specs=extra_specs, + exploration_scheduler_fn=exploration_scheduler_fn, + replay_stabilisation_fn=replay_stabilisation_fn, + mixer_fn=mixer_fn, + ) + + def _get_extra_specs(self) -> Any: + agents = self._environment_spec.get_agent_ids() + core_state_specs = {} + networks = self._network_factory( # type: ignore + environment_spec=self._environment_spec + ) + for agent in agents: + agent_type = agent.split("_")[0] + core_state_specs[agent] = ( + tf2_utils.squeeze_batch_dim( + networks["q_networks"][agent_type].initial_state(1) + ), + ) + return {"core_states": core_state_specs} + + def replay(self) -> Any: + """The replay storage.""" + return self._builder.make_replay_tables(self._environment_spec) + + def counter(self) -> Any: + return tf2_savers.CheckpointingRunner( + counting.Counter(), + time_delta_minutes=15, + directory=self._checkpoint_subpath, + subdirectory="counter", + ) + + def coordinator(self, counter: counting.Counter) -> Any: + return lp_utils.StepsLimiter(counter, self._max_executor_steps) # type: ignore + + def trainer( + self, + replay: reverb.Client, + counter: counting.Counter, + ) -> mava.core.Trainer: + """The Trainer part of the system.""" + + # Create the networks to optimize (online) + networks = self._network_factory( # type: ignore + environment_spec=self._environment_spec, shared_weights=self._shared_weights + ) + + # Create system architecture with target networks. + architecture = self._architecture( + environment_spec=self._environment_spec, + value_networks=networks["q_networks"], + shared_weights=self._shared_weights, + ) + + if self._builder._replay_stabiliser_fn is not None: + architecture = self._builder._replay_stabiliser_fn( # type: ignore + architecture + ) + + system_networks = architecture.create_system() + + # create logger + trainer_logger_config = {} + if self._logger_config and "trainer" in self._logger_config: + trainer_logger_config = self._logger_config["trainer"] + trainer_logger = self._logger_factory( # type: ignore + "trainer", **trainer_logger_config + ) + + dataset = self._builder.make_dataset_iterator(replay) + counter = counting.Counter(counter, "trainer") + + return self._builder.make_trainer( + networks=system_networks, + dataset=dataset, + counter=counter, + logger=trainer_logger, + ) + + def executor( + self, + executor_id: str, + replay: reverb.Client, + variable_source: acme.VariableSource, + counter: counting.Counter, + trainer: Optional[training.QMixTrainer] = None, + ) -> mava.ParallelEnvironmentLoop: + """The executor process.""" + + # Create the behavior policy. + networks = self._network_factory( # type: ignore + environment_spec=self._environment_spec, shared_weights=self._shared_weights + ) + + # Create system architecture with target networks. + architecture = self._architecture( + environment_spec=self._environment_spec, + value_networks=networks["q_networks"], + shared_weights=self._shared_weights, + ) + + if self._builder._replay_stabiliser_fn is not None: + architecture = self._builder._replay_stabiliser_fn( # type: ignore + architecture + ) + + system_networks = architecture.create_system() + + # Create the executor. + executor = self._builder.make_executor( + q_networks=system_networks["values"], + action_selectors=networks["action_selectors"], + adder=self._builder.make_adder(replay), + variable_source=variable_source, + trainer=trainer, + ) + + # TODO (Arnu): figure out why factory function are giving type errors + # Create the environment. + environment = self._environment_factory(evaluation=False) # type: ignore + + # Create logger and counter; actors will not spam bigtable. + counter = counting.Counter(counter, "executor") + + # Create executor logger + executor_logger_config = {} + if self._logger_config and "executor" in self._logger_config: + executor_logger_config = self._logger_config["executor"] + exec_logger = self._logger_factory( # type: ignore + f"executor_{executor_id}", **executor_logger_config + ) + + # Create the loop to connect environment and executor. + train_loop = self._train_loop_fn( + environment, + executor, + counter=counter, + logger=exec_logger, + **self._train_loop_fn_kwargs, + ) + + train_loop = DetailedPerAgentStatistics(train_loop) + + return train_loop + + def evaluator( + self, + variable_source: acme.VariableSource, + counter: counting.Counter, + trainer: training.QMixTrainer, + ) -> Any: + """The evaluation process.""" + + # Create the behavior policy. + networks = self._network_factory( # type: ignore + environment_spec=self._environment_spec, shared_weights=self._shared_weights + ) + + # Create system architecture with target networks. + architecture = self._architecture( + environment_spec=self._environment_spec, + value_networks=networks["q_networks"], + shared_weights=self._shared_weights, + ) + + if self._builder._replay_stabiliser_fn is not None: + architecture = self._builder._replay_stabiliser_fn( # type: ignore + architecture + ) + + system_networks = architecture.create_system() + + # Create the agent. + executor = self._builder.make_executor( + q_networks=system_networks["values"], + action_selectors=networks["action_selectors"], + variable_source=variable_source, + trainer=trainer, + evaluator=True, + ) + + # Make the environment. + environment = self._environment_factory(evaluation=True) # type: ignore + + # Create logger and counter. + counter = counting.Counter(counter, "evaluator") + evaluator_logger_config = {} + if self._logger_config and "evaluator" in self._logger_config: + evaluator_logger_config = self._logger_config["evaluator"] + eval_logger = self._logger_factory( # type: ignore + "evaluator", **evaluator_logger_config + ) + + # Create the run loop and return it. + # Create the loop to connect environment and executor. + eval_loop = self._eval_loop_fn( + environment, + executor, + counter=counter, + logger=eval_logger, + **self._eval_loop_fn_kwargs, + ) + + eval_loop = DetailedPerAgentStatistics(eval_loop) + return eval_loop + + def build(self, name: str = "madqn") -> Any: + """Build the distributed system topology.""" + program = lp.Program(name=name) + + with program.group("replay"): + replay = program.add_node(lp.ReverbNode(self.replay)) + + with program.group("counter"): + counter = program.add_node(lp.CourierNode(self.counter)) + + if self._max_executor_steps: + with program.group("coordinator"): + _ = program.add_node(lp.CourierNode(self.coordinator, counter)) + + with program.group("trainer"): + trainer = program.add_node(lp.CourierNode(self.trainer, replay, counter)) + + with program.group("evaluator"): + program.add_node(lp.CourierNode(self.evaluator, trainer, counter, trainer)) + + if not self._num_caches: + # Use the trainer as a single variable source. + sources = [trainer] + else: + with program.group("cacher"): + # Create a set of trainer caches. + sources = [] + for _ in range(self._num_caches): + cacher = program.add_node( + lp.CacherNode( + trainer, refresh_interval_ms=2000, stale_after_ms=4000 + ) + ) + sources.append(cacher) + + with program.group("executor"): + # Add executors which pull round-robin from our variable sources. + for executor_id in range(self._num_exectors): + source = sources[executor_id % len(sources)] + program.add_node( + lp.CourierNode( + self.executor, + executor_id, + replay, + source, + counter, + trainer, + ) + ) + + return program diff --git a/mava/systems/tf/qmixV2/training.py b/mava/systems/tf/qmixV2/training.py new file mode 100644 index 000000000..0976c04fc --- /dev/null +++ b/mava/systems/tf/qmixV2/training.py @@ -0,0 +1,367 @@ +# python3 +# Copyright 2021 [...placeholder...]. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy +import time +from typing import Any, Dict, List, Optional, Sequence, Tuple + +import numpy as np +import sonnet as snt +import tensorflow as tf +import tree +import trfl +from acme.tf import utils as tf2_utils +from acme.types import NestedArray +from acme.utils import counting, loggers +from trfl.indexing_ops import batched_index + +import mava +from mava.components.tf.modules.exploration.exploration_scheduling import ( + LinearExplorationScheduler, +) +from mava.systems.tf import savers as tf2_savers +from mava.utils import training_utils as train_utils + + +class QMixTrainer(mava.Trainer): + """MADQN trainer. + This is the trainer component of a MADQN system. IE it takes a dataset as input + and implements update functionality to learn from this dataset. + """ + + def __init__( + self, + agents: List[str], + agent_types: List[str], + q_networks: Dict[str, snt.Module], + target_q_networks: Dict[str, snt.Module], + target_update_period: int, + dataset: tf.data.Dataset, + optimizer: snt.Optimizer, + discount: float, + shared_weights: bool, + exploration_scheduler: LinearExplorationScheduler, + clipping: bool = True, + fingerprint: bool = False, + mixer: Optional[snt.Module] = None, + counter: counting.Counter = None, + logger: loggers.Logger = None, + checkpoint: bool = True, + checkpoint_subpath: str = "~/mava/", + ): + + self._agents = agents + self._agent_types = agent_types + self._shared_weights = shared_weights + self._optimizer = optimizer + self._checkpoint = checkpoint + + # Store online and target q-networks. + self._q_networks = q_networks + self._target_q_networks = target_q_networks + + # General learner book-keeping and loggers. + self._counter = counter or counting.Counter() + self._logger = logger + + # Other learner parameters. + self._discount = discount + self._clipping = clipping + self._fingerprint = fingerprint + + # Necessary to track when to update target networks. + self._num_steps = tf.Variable(0, dtype=tf.int32) + self._target_update_period = target_update_period + + # Create an iterator to go through the dataset. + self._iterator = dataset + + # Store the exploration scheduler + self._exploration_scheduler = exploration_scheduler + + # mixer + self._mixer = mixer + self._target_mixer = copy.deepcopy(mixer) + + # Dictionary with network keys for each agent. + self.agent_net_keys = {agent: agent for agent in self._agents} + if self._shared_weights: + self.agent_net_keys = {agent: agent.split("_")[0] for agent in self._agents} + + self.unique_net_keys = self._agent_types if shared_weights else self._agents + + # Expose the variables. + q_networks_to_expose = {} + self._system_network_variables: Dict[str, Dict[str, snt.Module]] = { + "q_network": {}, + } + for agent_key in self.unique_net_keys: + q_network_to_expose = self._target_q_networks[agent_key] + + q_networks_to_expose[agent_key] = q_network_to_expose + + self._system_network_variables["q_network"][ + agent_key + ] = q_network_to_expose.variables + + # Checkpointer + self._system_checkpointer = {} + if checkpoint: + for agent_key in self.unique_net_keys: + + checkpointer = tf2_savers.Checkpointer( + directory=checkpoint_subpath, + time_delta_minutes=15, + objects_to_save={ + "counter": self._counter, + "q_network": self._q_networks[agent_key], + "target_q_network": self._target_q_networks[agent_key], + "optimizer": self._optimizer, + "num_steps": self._num_steps, + }, + enable_checkpointing=checkpoint, + ) + + self._system_checkpointer[agent_key] = checkpointer + + # Do not record timestamps until after the first learning step is done. + # This is to avoid including the time it takes for actors to come online and + # fill the replay buffer. + + self._timestamp: Optional[float] = None + + def get_epsilon(self) -> float: + return self._exploration_scheduler.get_epsilon() + + def get_trainer_steps(self) -> float: + return self._num_steps.numpy() + + def _decrement_epsilon(self) -> None: + self._exploration_scheduler.decrement_epsilon() + + def _update_target_networks(self) -> None: + for key in self.unique_net_keys: + # Update target network. + online_variables = [ + *self._q_networks[key].variables, + ] + + target_variables = [ + *self._target_q_networks[key].variables, + ] + + if self._mixer and self._target_mixer: + online_variables += self._mixer.variables + target_variables += self._target_mixer.variables + + # Make online -> target network update ops. + if tf.math.mod(self._num_steps, self._target_update_period) == 0: + for src, dest in zip(online_variables, target_variables): + dest.assign(src) + self._num_steps.assign_add(1) + + def _get_feed( + self, + o_tm1_trans: Dict[str, np.ndarray], + o_t_trans: Dict[str, np.ndarray], + a_tm1: Dict[str, np.ndarray], + agent: str, + ) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor]: + + # Decentralised + o_tm1_feed = o_tm1_trans[agent].observation + o_t_feed = o_t_trans[agent].observation + a_tm1_feed = a_tm1[agent] + + return o_tm1_feed, o_t_feed, a_tm1_feed + + def step(self) -> None: + # Run the learning step. + fetches = self._step() + + # Compute elapsed time. + timestamp = time.time() + if self._timestamp: + elapsed_time = timestamp - self._timestamp + else: + elapsed_time = 0 + self._timestamp = timestamp # type: ignore + + # Update our counts and record it. + counts = self._counter.increment(steps=1, walltime=elapsed_time) + fetches.update(counts) + + # Checkpoint and attempt to write the logs. + if self._checkpoint: + train_utils.checkpoint_networks(self._system_checkpointer) + + # Log and decrement epsilon + epsilon = self.get_epsilon() + fetches["epsilon"] = epsilon + self._decrement_epsilon() + + if self._logger: + self._logger.write(fetches) + + @tf.function + def _step( + self, + ) -> Dict[str, Dict[str, Any]]: + + # Update the target networks + self._update_target_networks() + + # Get data from replay (dropping extras if any). Note there is no + # extra data here because we do not insert any into Reverb. + inputs = next(self._iterator) + + self._forward(inputs) + + self._backward() + + # Log losses per agent + return {agent: {"q_value_loss": self.loss} for agent in self._agents} + + def _forward(self, inputs: Any) -> None: + # Unpack input data as follows: + # o_tm1 = dictionary of observations one for each agent + # a_tm1 = dictionary of actions taken from obs in o_tm1 + # r_t = dictionary of rewards or rewards sequences + # (if using N step transitions) ensuing from actions a_tm1 + # d_t = environment discount ensuing from actions a_tm1. + # This discount is applied to future rewards after r_t. + # o_t = dictionary of next observations or next observation sequences + # e_t [Optional] = extra data that the agents persist in replay. + o_tm1, a_tm1, e_tm1, r_t, d_t, o_t, e_t = inputs.data + s_tm1 = e_tm1["s_t"] + s_t = e_t["s_t"] + with tf.GradientTape(persistent=True) as tape: + q_network_losses: Dict[str, NestedArray] = {} + q_acts = [] + q_targets = [] + for agent in self._agents: + agent_key = self.agent_net_keys[agent] + + # Cast the additional discount to match the environment discount dtype. + discount = tf.cast(self._discount, dtype=d_t[agent].dtype) + + # Maybe transform the observation before feeding into policy and critic. + # Transforming the observations this way at the start of the learning + # step effectively means that the policy and critic share observation + # network weights. + + o_tm1_feed, o_t_feed, a_tm1_feed = self._get_feed( + o_tm1, o_t, a_tm1, agent + ) + q_tm1 = self._q_networks[agent_key](o_tm1_feed) + q_t_value = self._target_q_networks[agent_key](o_t_feed) + q_t_selector = self._q_networks[agent_key](o_t_feed) + best_action = tf.argmax(q_t_selector, axis=1, output_type=tf.int32) + + # TODO Make use of q_t_selector for fingerprinting. Speak to Claude. + q_act = batched_index(q_tm1, a_tm1_feed, keepdims=True) # [B, 1] + q_target = batched_index( + q_t_value, best_action, keepdims=True + ) # [B, 1] + + q_acts.append(q_act) + q_targets.append(q_target) + + if self._mixer and self._target_mixer: + # NOTE we expect a cooperative environment where agents share + # the same scalar reward and discount. + rewards = tf.concat( + [tf.reshape(val, (-1, 1)) for val in list(r_t.values())], axis=1 + ) + rewards = tf.reduce_mean(rewards, axis=1) # [B] + + pcont = tf.concat( + [tf.reshape(val, (-1, 1)) for val in list(d_t.values())], axis=1 + ) + pcont = tf.reduce_mean(pcont, axis=1) + discount = tf.cast(self._discount, list(d_t.values())[0].dtype) + pcont = discount * pcont # [B] + + q_acts = tf.concat(q_acts, axis=1) # [B, num_agents] + q_targets = tf.concat(q_targets, axis=1) # [B, num_agents] + + # q_tot_mixed = self._mixer(q_acts, s_tm1) # [B, 1, 1] + # q_tot_target_mixed = self._target_mixer(q_targets, s_t) # [B, 1, 1] + + q_tot_mixed = tf.reduce_sum(q_acts, axis=1) # [B, 1, 1] + q_tot_target_mixed = tf.reduce_sum(q_targets, axis=1) # [B, 1, 1] + + q_tot_mixed = tf.reshape(q_tot_mixed, (-1,)) + q_tot_target_mixed = tf.reshape(q_tot_target_mixed, (-1,)) + + # Calculate Q loss. + targets = rewards + pcont * q_tot_target_mixed + targets = tf.stop_gradient(targets) + td_error = targets - q_tot_mixed + + # Loss is MSE scaled by 0.5, so the gradient is equal to the TD error. + self.loss = 0.5 * tf.reduce_mean(tf.square(td_error)) + self.tape = tape + else: + # Q-network learning + loss, _ = trfl.double_qlearning( + q_tm1, + a_tm1_feed, + r_t[agent], + discount * d_t[agent], + q_t_value, + q_t_selector, + ) + + loss = tf.reduce_mean(loss) + + q_network_losses[agent] = {"q_value_loss": loss} + self._q_network_losses = q_network_losses + self.tape = tape + + def _backward(self) -> None: + loss = self.loss + tape = self.tape + variables = [] + for agent in self._agents: + agent_key = self.agent_net_keys[agent] + + # Get trainable variables + variables = self._q_networks[agent_key].trainable_variables + if self._mixer: + variables += self._mixer.trainable_variables + + # Compute gradients + gradients = tape.gradient(loss, variables) + + # Maybe clip gradients. + if self._clipping: + gradients = tf.clip_by_global_norm(gradients, 40.0)[0] + + # Apply gradients. + self._optimizer.apply(gradients, variables) + + train_utils.safe_del(self, "tape") + + def get_variables(self, names: Sequence[str]) -> Dict[str, Dict[str, np.ndarray]]: + variables: Dict[str, Dict[str, np.ndarray]] = {} + for network_type in names: + variables[network_type] = { + agent: tf2_utils.to_numpy( + self._system_network_variables[network_type][agent] + ) + for agent in self.unique_net_keys + } + return variables diff --git a/mava/wrappers/__init__.py b/mava/wrappers/__init__.py index b0f47b4b8..66657ed37 100644 --- a/mava/wrappers/__init__.py +++ b/mava/wrappers/__init__.py @@ -24,7 +24,9 @@ PettingZooAECEnvWrapper, PettingZooParallelEnvWrapper, ) -from mava.wrappers.smac import SMACEnvWrapper + +# TODO only import smac if the user needs it. +# from mava.wrappers.smac import SMACEnvWrapper from mava.wrappers.system_trainer_statistics import ( DetailedTrainerStatistics, DetailedTrainerStatisticsWithEpsilon, diff --git a/mava/wrappers/smac.py b/mava/wrappers/smac.py index 104831b0f..0bf9b5f41 100644 --- a/mava/wrappers/smac.py +++ b/mava/wrappers/smac.py @@ -26,12 +26,15 @@ from acme.wrappers.gym_wrapper import _convert_to_spec from gym.spaces import Box, Discrete from pettingzoo.utils.env import ParallelEnv -from smac.env import StarCraft2Env # type:ignore from mava import types from mava.utils.wrapper_utils import convert_np_type, parameterized_restart from mava.wrappers.env_wrappers import ParallelEnvWrapper +# TODO should not be imported unless user wants the smax env. +# from smac.env import StarCraft2Env # type:ignore + + class SMACEnvWrapper(ParallelEnvWrapper): """ From 5ba25ffa2a7f49a00f0e48c9a9d1727885906c88 Mon Sep 17 00:00:00 2001 From: Kale-ab Date: Mon, 24 May 2021 18:13:01 +0200 Subject: [PATCH 066/188] fix: Work with shared weights false. --- ...ntralised_feedforward_mad4pg_continous.py} | 0 ...centralised_feedforward_mad4pg_discrete.py | 206 ++++++++++++++++++ mava/systems/tf/mad4pg/training.py | 96 ++++---- mava/systems/tf/maddpg/builder.py | 12 +- mava/systems/tf/maddpg/training.py | 122 ++++++----- mava/systems/tf/mappo/builder.py | 12 +- mava/systems/tf/mappo/training.py | 33 +-- 7 files changed, 343 insertions(+), 138 deletions(-) rename examples/petting_zoo/{run_decentralised_feedforward_mad4pg.py => run_decentralised_feedforward_mad4pg_continous.py} (100%) create mode 100644 examples/petting_zoo/run_decentralised_feedforward_mad4pg_discrete.py diff --git a/examples/petting_zoo/run_decentralised_feedforward_mad4pg.py b/examples/petting_zoo/run_decentralised_feedforward_mad4pg_continous.py similarity index 100% rename from examples/petting_zoo/run_decentralised_feedforward_mad4pg.py rename to examples/petting_zoo/run_decentralised_feedforward_mad4pg_continous.py diff --git a/examples/petting_zoo/run_decentralised_feedforward_mad4pg_discrete.py b/examples/petting_zoo/run_decentralised_feedforward_mad4pg_discrete.py new file mode 100644 index 000000000..6b44bedcd --- /dev/null +++ b/examples/petting_zoo/run_decentralised_feedforward_mad4pg_discrete.py @@ -0,0 +1,206 @@ +# python3 +# Copyright 2021 [...placeholder...]. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Example running MAD4PG on PZ environments.""" + +import functools +from datetime import datetime +from typing import Any, Dict, Mapping, Sequence, Union + +import launchpad as lp +import numpy as np +import sonnet as snt +from absl import app, flags +from acme import types +from acme.specs import BoundedArray, DiscreteArray +from acme.tf import networks +from acme.tf import utils as tf2_utils +from launchpad.nodes.python.local_multi_processing import PythonProcess + +from mava import specs as mava_specs +from mava.components.tf.networks.mad4pg import DiscreteValuedHead +from mava.systems.tf import mad4pg +from mava.utils import lp_utils +from mava.utils.environments import pettingzoo_utils +from mava.utils.loggers import logger_utils + +FLAGS = flags.FLAGS + +flags.DEFINE_string( + "env_class", + "mpe", + "Pettingzoo environment class, e.g. atari (str).", +) + +flags.DEFINE_string( + "env_name", + "simple_speaker_listener_v3", + "Pettingzoo environment name, e.g. pong (str).", +) +flags.DEFINE_string( + "mava_id", + str(datetime.now()), + "Experiment identifier that can be used to continue experiments.", +) +flags.DEFINE_string("base_dir", "~/mava/", "Base dir to store experiments.") + + +def make_networks( + environment_spec: mava_specs.MAEnvironmentSpec, + policy_networks_layer_sizes: Union[Dict[str, Sequence], Sequence] = ( + 256, + 256, + 256, + ), + critic_networks_layer_sizes: Union[Dict[str, Sequence], Sequence] = (512, 512, 256), + shared_weights: bool = True, + sigma: float = 0.3, + vmin: float = -150.0, + vmax: float = 150.0, + num_atoms: int = 51, +) -> Mapping[str, types.TensorTransformation]: + """Creates networks used by the agents.""" + specs = environment_spec.get_agent_specs() + + # Create agent_type specs + if shared_weights: + type_specs = {key.split("_")[0]: specs[key] for key in specs.keys()} + specs = type_specs + + if isinstance(policy_networks_layer_sizes, Sequence): + policy_networks_layer_sizes = { + key: policy_networks_layer_sizes for key in specs.keys() + } + if isinstance(critic_networks_layer_sizes, Sequence): + critic_networks_layer_sizes = { + key: critic_networks_layer_sizes for key in specs.keys() + } + + observation_networks = {} + policy_networks = {} + critic_networks = {} + for key in specs.keys(): + # TODO (dries): Make specs[key].actions + # return a list of specs for hybrid action space + # Get total number of action dimensions from action spec. + agent_act_spec = specs[key].actions + if type(specs[key].actions) == DiscreteArray: + num_actions = agent_act_spec.num_values + minimum = [-1.0] * num_actions + maximum = [1.0] * num_actions + agent_act_spec = BoundedArray( + shape=(num_actions,), + minimum=minimum, + maximum=maximum, + dtype="float32", + name="actions", + ) + + # Get total number of action dimensions from action spec. + num_dimensions = np.prod(agent_act_spec.shape, dtype=int) + + # Create the shared observation network; here simply a state-less operation. + observation_network = tf2_utils.to_sonnet_module(tf2_utils.batch_concat) + + # Create the policy network. + policy_network = snt.Sequential( + [ + networks.LayerNormMLP( + policy_networks_layer_sizes[key], activate_final=True + ), + networks.NearZeroInitializedLinear(num_dimensions), + networks.TanhToSpec(agent_act_spec), + networks.ClippedGaussian(sigma), + networks.ClipToSpec(agent_act_spec), + ] + ) + + # Create the critic network. + critic_network = snt.Sequential( + [ + # The multiplexer concatenates the observations/actions. + networks.CriticMultiplexer(), + networks.LayerNormMLP( + critic_networks_layer_sizes[key], activate_final=False + ), + DiscreteValuedHead(vmin, vmax, num_atoms), + ] + ) + observation_networks[key] = observation_network + policy_networks[key] = policy_network + critic_networks[key] = critic_network + + return { + "policies": policy_networks, + "critics": critic_networks, + "observations": observation_networks, + } + + +def main(_: Any) -> None: + + # environment + environment_factory = functools.partial( + pettingzoo_utils.make_environment, + env_name=FLAGS.env_name, + env_class=FLAGS.env_class, + ) + + # networks + network_factory = lp_utils.partial_kwargs(make_networks) + + # Checkpointer appends "Checkpoints" to checkpoint_dir + checkpoint_dir = f"{FLAGS.base_dir}/{FLAGS.mava_id}" + + # loggers + log_every = 10 + logger_factory = functools.partial( + logger_utils.make_logger, + directory=FLAGS.base_dir, + to_terminal=True, + to_tensorboard=True, + time_stamp=FLAGS.mava_id, + time_delta=log_every, + ) + + # distributed program + program = mad4pg.MAD4PG( + environment_factory=environment_factory, + network_factory=network_factory, + logger_factory=logger_factory, + num_executors=2, + policy_optimizer=snt.optimizers.Adam(learning_rate=1e-4), + critic_optimizer=snt.optimizers.Adam(learning_rate=1e-4), + checkpoint_subpath=checkpoint_dir, + ).build() + + # launch + gpu_id = -1 + env_vars = {"CUDA_VISIBLE_DEVICES": str(gpu_id)} + local_resources = { + "trainer": [], + "evaluator": PythonProcess(env=env_vars), + "executor": PythonProcess(env=env_vars), + } + lp.launch( + program, + lp.LaunchType.LOCAL_MULTI_PROCESSING, + terminal="current_terminal", + local_resources=local_resources, + ) + + +if __name__ == "__main__": + app.run(main) diff --git a/mava/systems/tf/mad4pg/training.py b/mava/systems/tf/mad4pg/training.py index 33e5875e9..fcfbe8448 100644 --- a/mava/systems/tf/mad4pg/training.py +++ b/mava/systems/tf/mad4pg/training.py @@ -52,8 +52,8 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizers: Dict[str, snt.Optimizer], - critic_optimizers: Dict[str, snt.Optimizer], + policy_optimizer: snt.Optimizer, + critic_optimizer: snt.Optimizer, discount: float, target_averaging: bool, target_update_period: int, @@ -83,8 +83,8 @@ def __init__( observation_network: an optional online network to process observations before the policy and the critic. target_observation_network: the target observation network. - policy_optimizers: the optimizer to be applied to the DPG (policy) loss. - critic_optimizers: the optimizer to be applied to the critic loss. + policy_optimizer: the optimizer to be applied to the DPG (policy) loss. + critic_optimizer: the optimizer to be applied to the critic loss. clipping: whether to clip gradients by global norm. counter: counter object used to keep track of steps. logger: logger object to be used by learner. @@ -98,8 +98,8 @@ def __init__( critic_networks=critic_networks, target_policy_networks=target_policy_networks, target_critic_networks=target_critic_networks, - policy_optimizers=policy_optimizers, - critic_optimizers=critic_optimizers, + policy_optimizer=policy_optimizer, + critic_optimizer=critic_optimizer, discount=discount, target_averaging=target_averaging, target_update_period=target_update_period, @@ -203,8 +203,8 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizers: Dict[str, snt.Optimizer], - critic_optimizers: Dict[str, snt.Optimizer], + policy_optimizer: Dict[str, snt.Optimizer], + critic_optimizer: Dict[str, snt.Optimizer], discount: float, target_averaging: bool, target_update_period: int, @@ -234,8 +234,8 @@ def __init__( observation_network: an optional online network to process observations before the policy and the critic. target_observation_network: the target observation network. - policy_optimizers: the optimizer to be applied to the DPG (policy) loss. - critic_optimizers: the optimizer to be applied to the critic loss. + policy_optimizer: the optimizer to be applied to the DPG (policy) loss. + critic_optimizer: the optimizer to be applied to the critic loss. clipping: whether to clip gradients by global norm. counter: counter object used to keep track of steps. logger: logger object to be used by learner. @@ -257,8 +257,8 @@ def __init__( observation_networks=observation_networks, target_observation_networks=target_observation_networks, shared_weights=shared_weights, - policy_optimizers=policy_optimizers, - critic_optimizers=critic_optimizers, + policy_optimizer=policy_optimizer, + critic_optimizer=critic_optimizer, max_gradient_norm=max_gradient_norm, counter=counter, logger=logger, @@ -281,8 +281,8 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizers: Dict[str, snt.Optimizer], - critic_optimizers: Dict[str, snt.Optimizer], + policy_optimizer: Dict[str, snt.Optimizer], + critic_optimizer: Dict[str, snt.Optimizer], discount: float, target_averaging: bool, target_update_period: int, @@ -312,8 +312,8 @@ def __init__( observation_network: an optional online network to process observations before the policy and the critic. target_observation_network: the target observation network. - policy_optimizers: the optimizer to be applied to the DPG (policy) loss. - critic_optimizers: the optimizer to be applied to the critic loss. + policy_optimizer: the optimizer to be applied to the DPG (policy) loss. + critic_optimizer: the optimizer to be applied to the critic loss. clipping: whether to clip gradients by global norm. counter: counter object used to keep track of steps. logger: logger object to be used by learner. @@ -335,8 +335,8 @@ def __init__( observation_networks=observation_networks, target_observation_networks=target_observation_networks, shared_weights=shared_weights, - policy_optimizers=policy_optimizers, - critic_optimizers=critic_optimizers, + policy_optimizer=policy_optimizer, + critic_optimizer=critic_optimizer, max_gradient_norm=max_gradient_norm, counter=counter, logger=logger, @@ -359,8 +359,8 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizers: Dict[str, snt.Optimizer], - critic_optimizers: Dict[str, snt.Optimizer], + policy_optimizer: Dict[str, snt.Optimizer], + critic_optimizer: Dict[str, snt.Optimizer], discount: float, target_averaging: bool, target_update_period: int, @@ -390,8 +390,8 @@ def __init__( observation_network: an optional online network to process observations before the policy and the critic. target_observation_network: the target observation network. - policy_optimizers: the optimizer to be applied to the DPG (policy) loss. - critic_optimizers: the optimizer to be applied to the critic loss. + policy_optimizer: the optimizer to be applied to the DPG (policy) loss. + critic_optimizer: the optimizer to be applied to the critic loss. clipping: whether to clip gradients by global norm. counter: counter object used to keep track of steps. logger: logger object to be used by learner. @@ -413,8 +413,8 @@ def __init__( observation_networks=observation_networks, target_observation_networks=target_observation_networks, shared_weights=shared_weights, - policy_optimizers=policy_optimizers, - critic_optimizers=critic_optimizers, + policy_optimizer=policy_optimizer, + critic_optimizer=critic_optimizer, max_gradient_norm=max_gradient_norm, counter=counter, logger=logger, @@ -437,8 +437,8 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizers: Dict[str, snt.Optimizer], - critic_optimizers: Dict[str, snt.Optimizer], + policy_optimizer: Dict[str, snt.Optimizer], + critic_optimizer: Dict[str, snt.Optimizer], discount: float, target_averaging: bool, target_update_period: int, @@ -469,8 +469,8 @@ def __init__( observation_network: an optional online network to process observations before the policy and the critic. target_observation_network: the target observation network. - policy_optimizers: the optimizer to be applied to the DPG (policy) loss. - critic_optimizers: the optimizer to be applied to the critic loss. + policy_optimizer: the optimizer to be applied to the DPG (policy) loss. + critic_optimizer: the optimizer to be applied to the critic loss. clipping: whether to clip gradients by global norm. counter: counter object used to keep track of steps. logger: logger object to be used by learner. @@ -492,8 +492,8 @@ def __init__( observation_networks=observation_networks, target_observation_networks=target_observation_networks, shared_weights=shared_weights, - policy_optimizers=policy_optimizers, - critic_optimizers=critic_optimizers, + policy_optimizer=policy_optimizer, + critic_optimizer=critic_optimizer, max_gradient_norm=max_gradient_norm, counter=counter, logger=logger, @@ -643,8 +643,8 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizers: Dict[str, snt.Optimizer], - critic_optimizers: Dict[str, snt.Optimizer], + policy_optimizer: Dict[str, snt.Optimizer], + critic_optimizer: Dict[str, snt.Optimizer], discount: float, target_averaging: bool, target_update_period: int, @@ -675,8 +675,8 @@ def __init__( observation_network: an optional online network to process observations before the policy and the critic. target_observation_network: the target observation network. - policy_optimizers: the optimizer to be applied to the DPG (policy) loss. - critic_optimizers: the optimizer to be applied to the critic loss. + policy_optimizer: the optimizer to be applied to the DPG (policy) loss. + critic_optimizer: the optimizer to be applied to the critic loss. clipping: whether to clip gradients by global norm. counter: counter object used to keep track of steps. logger: logger object to be used by learner. @@ -698,8 +698,8 @@ def __init__( observation_networks=observation_networks, target_observation_networks=target_observation_networks, shared_weights=shared_weights, - policy_optimizers=policy_optimizers, - critic_optimizers=critic_optimizers, + policy_optimizer=policy_optimizer, + critic_optimizer=critic_optimizer, max_gradient_norm=max_gradient_norm, counter=counter, logger=logger, @@ -725,8 +725,8 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizers: Dict[str, snt.Optimizer], - critic_optimizers: Dict[str, snt.Optimizer], + policy_optimizer: Dict[str, snt.Optimizer], + critic_optimizer: Dict[str, snt.Optimizer], discount: float, target_averaging: bool, target_update_period: int, @@ -757,8 +757,8 @@ def __init__( observation_network: an optional online network to process observations before the policy and the critic. target_observation_network: the target observation network. - policy_optimizers: the optimizer to be applied to the DPG (policy) loss. - critic_optimizers: the optimizer to be applied to the critic loss. + policy_optimizer: the optimizer to be applied to the DPG (policy) loss. + critic_optimizer: the optimizer to be applied to the critic loss. clipping: whether to clip gradients by global norm. counter: counter object used to keep track of steps. logger: logger object to be used by learner. @@ -780,8 +780,8 @@ def __init__( observation_networks=observation_networks, target_observation_networks=target_observation_networks, shared_weights=shared_weights, - policy_optimizers=policy_optimizers, - critic_optimizers=critic_optimizers, + policy_optimizer=policy_optimizer, + critic_optimizer=critic_optimizer, max_gradient_norm=max_gradient_norm, counter=counter, logger=logger, @@ -807,8 +807,8 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizers: Dict[str, snt.Optimizer], - critic_optimizers: Dict[str, snt.Optimizer], + policy_optimizer: Dict[str, snt.Optimizer], + critic_optimizer: Dict[str, snt.Optimizer], discount: float, target_averaging: bool, target_update_period: int, @@ -839,8 +839,8 @@ def __init__( observation_network: an optional online network to process observations before the policy and the critic. target_observation_network: the target observation network. - policy_optimizers: the optimizer to be applied to the DPG (policy) loss. - critic_optimizers: the optimizer to be applied to the critic loss. + policy_optimizer: the optimizer to be applied to the DPG (policy) loss. + critic_optimizer: the optimizer to be applied to the critic loss. clipping: whether to clip gradients by global norm. counter: counter object used to keep track of steps. logger: logger object to be used by learner. @@ -862,8 +862,8 @@ def __init__( observation_networks=observation_networks, target_observation_networks=target_observation_networks, shared_weights=shared_weights, - policy_optimizers=policy_optimizers, - critic_optimizers=critic_optimizers, + policy_optimizer=policy_optimizer, + critic_optimizer=critic_optimizer, max_gradient_norm=max_gradient_norm, counter=counter, logger=logger, diff --git a/mava/systems/tf/maddpg/builder.py b/mava/systems/tf/maddpg/builder.py index 3929a42b9..a40bb92eb 100644 --- a/mava/systems/tf/maddpg/builder.py +++ b/mava/systems/tf/maddpg/builder.py @@ -323,14 +323,6 @@ def make_trainer( target_averaging = self._config.target_averaging target_update_rate = self._config.target_update_rate - # Create optimizers for different agent types. - # TODO(Kale-ab): Allow this to be passed as a system param. - policy_optimizers: Dict[str, snt.Optimizer] = {} - critic_optimizers: Dict[str, snt.Optimizer] = {} - for agent in self._agent_types: - policy_optimizers[agent] = copy.deepcopy(self._config.policy_optimizer) - critic_optimizers[agent] = copy.deepcopy(self._config.critic_optimizer) - # trainer args trainer_config: Dict[str, Any] = { "agents": agents, @@ -342,8 +334,8 @@ def make_trainer( "target_critic_networks": networks["target_critics"], "target_observation_networks": networks["target_observations"], "shared_weights": shared_weights, - "policy_optimizers": policy_optimizers, - "critic_optimizers": critic_optimizers, + "policy_optimizer": self._config.policy_optimizer, + "critic_optimizer": self._config.critic_optimizer, "max_gradient_norm": max_gradient_norm, "discount": discount, "target_averaging": target_averaging, diff --git a/mava/systems/tf/maddpg/training.py b/mava/systems/tf/maddpg/training.py index 89676c09b..367ba6ae8 100644 --- a/mava/systems/tf/maddpg/training.py +++ b/mava/systems/tf/maddpg/training.py @@ -51,8 +51,8 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizers: Dict[str, snt.Optimizer], - critic_optimizers: Dict[str, snt.Optimizer], + policy_optimizer: snt.Optimizer, + critic_optimizer: snt.Optimizer, discount: float, target_averaging: bool, target_update_period: int, @@ -86,8 +86,8 @@ def __init__( observation_network: an optional online network to process observations before the policy and the critic. target_observation_network: the target observation network. - policy_optimizers: the optimizer to be applied to the DPG (policy) loss. - critic_optimizers: the optimizer to be applied to the critic loss. + policy_optimizer: the optimizer to be applied to the DPG (policy) loss. + critic_optimizer: the optimizer to be applied to the critic loss. clipping: whether to clip gradients by global norm. counter: counter object used to keep track of steps. logger: logger object to be used by learner. @@ -136,9 +136,6 @@ def __init__( # Create an iterator to go through the dataset. self._iterator = iter(dataset) # pytype: disable=wrong-arg-types - self._critic_optimizers = critic_optimizers - self._policy_optimizers = policy_optimizers - # Dictionary with network keys for each agent. self.agent_net_keys = {agent: agent for agent in self._agents} if self._shared_weights: @@ -146,6 +143,14 @@ def __init__( self.unique_net_keys = self._agent_types if shared_weights else self._agents + # Create optimizers for different agent types. + # TODO(Kale-ab): Allow this to be passed as a system param. + self._policy_optimizers: snt.Optimizer = {} + self._critic_optimizers: snt.Optimizer = {} + for agent in self.unique_net_keys: + self._policy_optimizers[agent] = copy.deepcopy(policy_optimizer) + self._critic_optimizers[agent] = copy.deepcopy(critic_optimizer) + # Expose the variables. policy_networks_to_expose = {} self._system_network_variables: Dict[str, Dict[str, snt.Module]] = { @@ -462,8 +467,8 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizers: Dict[str, snt.Optimizer], - critic_optimizers: Dict[str, snt.Optimizer], + policy_optimizer: snt.Optimizer, + critic_optimizer: snt.Optimizer, discount: float, target_averaging: bool, target_update_period: int, @@ -493,8 +498,8 @@ def __init__( observation_network: an optional online network to process observations before the policy and the critic. target_observation_network: the target observation network. - policy_optimizers: the optimizers to be applied to the DPG (policy) loss. - critic_optimizers: the optimizers to be applied to the critic loss. + policy_optimizer: the optimizers to be applied to the DPG (policy) loss. + critic_optimizer: the optimizers to be applied to the critic loss. clipping: whether to clip gradients by global norm. counter: counter object used to keep track of steps. logger: logger object to be used by learner. @@ -516,8 +521,8 @@ def __init__( observation_networks=observation_networks, target_observation_networks=target_observation_networks, shared_weights=shared_weights, - policy_optimizers=policy_optimizers, - critic_optimizers=critic_optimizers, + policy_optimizer=policy_optimizer, + critic_optimizer=critic_optimizer, max_gradient_norm=max_gradient_norm, counter=counter, logger=logger, @@ -540,8 +545,8 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizers: Dict[str, snt.Optimizer], - critic_optimizers: Dict[str, snt.Optimizer], + policy_optimizer: snt.Optimizer, + critic_optimizer: snt.Optimizer, discount: float, target_averaging: bool, target_update_period: int, @@ -571,8 +576,8 @@ def __init__( observation_network: an optional online network to process observations before the policy and the critic. target_observation_network: the target observation network. - policy_optimizers: the optimizer to be applied to the DPG (policy) loss. - critic_optimizers: the optimizer to be applied to the critic loss. + policy_optimizer: the optimizer to be applied to the DPG (policy) loss. + critic_optimizer: the optimizer to be applied to the critic loss. clipping: whether to clip gradients by global norm. counter: counter object used to keep track of steps. logger: logger object to be used by learner. @@ -594,8 +599,8 @@ def __init__( observation_networks=observation_networks, target_observation_networks=target_observation_networks, shared_weights=shared_weights, - policy_optimizers=policy_optimizers, - critic_optimizers=critic_optimizers, + policy_optimizer=policy_optimizer, + critic_optimizer=critic_optimizer, max_gradient_norm=max_gradient_norm, counter=counter, logger=logger, @@ -656,8 +661,8 @@ def __init__( target_update_period: int, target_update_rate: float, dataset: tf.data.Dataset, - policy_optimizers: Dict[str, snt.Optimizer], - critic_optimizers: Dict[str, snt.Optimizer], + policy_optimizer: snt.Optimizer, + critic_optimizer: snt.Optimizer, observation_networks: Dict[str, snt.Module], target_observation_networks: Dict[str, snt.Module], shared_weights: bool = False, @@ -682,8 +687,8 @@ def __init__( observation_network: an optional online network to process observations before the policy and the critic. target_observation_network: the target observation network. - policy_optimizers: the optimizer to be applied to the DPG (policy) loss. - critic_optimizers: the optimizer to be applied to the critic loss. + policy_optimizer: the optimizer to be applied to the DPG (policy) loss. + critic_optimizer: the optimizer to be applied to the critic loss. clipping: whether to clip gradients by global norm. counter: counter object used to keep track of steps. logger: logger object to be used by learner. @@ -705,8 +710,8 @@ def __init__( observation_networks=observation_networks, target_observation_networks=target_observation_networks, shared_weights=shared_weights, - policy_optimizers=policy_optimizers, - critic_optimizers=critic_optimizers, + policy_optimizer=policy_optimizer, + critic_optimizer=critic_optimizer, max_gradient_norm=max_gradient_norm, counter=counter, logger=logger, @@ -774,8 +779,8 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizers: Dict[str, snt.Optimizer], - critic_optimizers: Dict[str, snt.Optimizer], + policy_optimizer: snt.Optimizer, + critic_optimizer: snt.Optimizer, discount: float, target_averaging: bool, target_update_period: int, @@ -805,8 +810,8 @@ def __init__( observation_network: an optional online network to process observations before the policy and the critic. target_observation_network: the target observation network. - policy_optimizers: the optimizer to be applied to the DPG (policy) loss. - critic_optimizers: the optimizer to be applied to the critic loss. + policy_optimizer: the optimizer to be applied to the DPG (policy) loss. + critic_optimizer: the optimizer to be applied to the critic loss. clipping: whether to clip gradients by global norm. counter: counter object used to keep track of steps. logger: logger object to be used by learner. @@ -828,8 +833,8 @@ def __init__( observation_networks=observation_networks, target_observation_networks=target_observation_networks, shared_weights=shared_weights, - policy_optimizers=policy_optimizers, - critic_optimizers=critic_optimizers, + policy_optimizer=policy_optimizer, + critic_optimizer=critic_optimizer, max_gradient_norm=max_gradient_norm, counter=counter, logger=logger, @@ -884,8 +889,8 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizers: Dict[str, snt.Optimizer], - critic_optimizers: Dict[str, snt.Optimizer], + policy_optimizer: snt.Optimizer, + critic_optimizer: snt.Optimizer, discount: float, target_averaging: bool, target_update_period: int, @@ -916,8 +921,8 @@ def __init__( observation_network: an optional online network to process observations before the policy and the critic. target_observation_network: the target observation network. - policy_optimizers: the optimizer to be applied to the DPG (policy) loss. - critic_optimizers: the optimizer to be applied to the critic loss. + policy_optimizer: the optimizer to be applied to the DPG (policy) loss. + critic_optimizer: the optimizer to be applied to the critic loss. clipping: whether to clip gradients by global norm. counter: counter object used to keep track of steps. logger: logger object to be used by learner. @@ -967,9 +972,6 @@ def __init__( # Create an iterator to go through the dataset. self._iterator = iter(dataset) # pytype: disable=wrong-arg-types - self._critic_optimizers = critic_optimizers - self._policy_optimizers = policy_optimizers - # Dictionary with network keys for each agent. self.agent_net_keys = {agent: agent for agent in self._agents} if self._shared_weights: @@ -977,6 +979,14 @@ def __init__( self.unique_net_keys = self._agent_types if shared_weights else self._agents + # Create optimizers for different agent types. + # TODO(Kale-ab): Allow this to be passed as a system param. + self._policy_optimizers: snt.Optimizer = {} + self._critic_optimizers: snt.Optimizer = {} + for agent in self.unique_net_keys: + self._policy_optimizers[agent] = copy.deepcopy(policy_optimizer) + self._critic_optimizers[agent] = copy.deepcopy(critic_optimizer) + # Expose the variables. policy_networks_to_expose = {} self._system_network_variables: Dict[str, Dict[str, snt.Module]] = { @@ -1374,8 +1384,8 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizers: Dict[str, snt.Optimizer], - critic_optimizers: Dict[str, snt.Optimizer], + policy_optimizer: snt.Optimizer, + critic_optimizer: snt.Optimizer, discount: float, target_averaging: bool, target_update_period: int, @@ -1406,8 +1416,8 @@ def __init__( observation_network: an optional online network to process observations before the policy and the critic. target_observation_network: the target observation network. - policy_optimizers: the optimizer to be applied to the DPG (policy) loss. - critic_optimizers: the optimizer to be applied to the critic loss. + policy_optimizer: the optimizer to be applied to the DPG (policy) loss. + critic_optimizer: the optimizer to be applied to the critic loss. clipping: whether to clip gradients by global norm. counter: counter object used to keep track of steps. logger: logger object to be used by learner. @@ -1429,8 +1439,8 @@ def __init__( observation_networks=observation_networks, target_observation_networks=target_observation_networks, shared_weights=shared_weights, - policy_optimizers=policy_optimizers, - critic_optimizers=critic_optimizers, + policy_optimizer=policy_optimizer, + critic_optimizer=critic_optimizer, max_gradient_norm=max_gradient_norm, counter=counter, logger=logger, @@ -1454,8 +1464,8 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizers: Dict[str, snt.Optimizer], - critic_optimizers: Dict[str, snt.Optimizer], + policy_optimizer: snt.Optimizer, + critic_optimizer: snt.Optimizer, discount: float, target_averaging: bool, target_update_period: int, @@ -1486,8 +1496,8 @@ def __init__( observation_network: an optional online network to process observations before the policy and the critic. target_observation_network: the target observation network. - policy_optimizers: the optimizer to be applied to the DPG (policy) loss. - critic_optimizers: the optimizer to be applied to the critic loss. + policy_optimizer: the optimizer to be applied to the DPG (policy) loss. + critic_optimizer: the optimizer to be applied to the critic loss. clipping: whether to clip gradients by global norm. counter: counter object used to keep track of steps. logger: logger object to be used by learner. @@ -1508,8 +1518,8 @@ def __init__( observation_networks=observation_networks, target_observation_networks=target_observation_networks, shared_weights=shared_weights, - policy_optimizers=policy_optimizers, - critic_optimizers=critic_optimizers, + policy_optimizer=policy_optimizer, + critic_optimizer=critic_optimizer, max_gradient_norm=max_gradient_norm, counter=counter, logger=logger, @@ -1567,8 +1577,8 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizers: Dict[str, snt.Optimizer], - critic_optimizers: Dict[str, snt.Optimizer], + policy_optimizer: snt.Optimizer, + critic_optimizer: snt.Optimizer, discount: float, target_averaging: bool, target_update_period: int, @@ -1599,8 +1609,8 @@ def __init__( observation_network: an optional online network to process observations before the policy and the critic. target_observation_network: the target observation network. - policy_optimizers: the optimizer to be applied to the DPG (policy) loss. - critic_optimizers: the optimizer to be applied to the critic loss. + policy_optimizer: the optimizer to be applied to the DPG (policy) loss. + critic_optimizer: the optimizer to be applied to the critic loss. clipping: whether to clip gradients by global norm. counter: counter object used to keep track of steps. logger: logger object to be used by learner. @@ -1621,8 +1631,8 @@ def __init__( observation_networks=observation_networks, target_observation_networks=target_observation_networks, shared_weights=shared_weights, - policy_optimizers=policy_optimizers, - critic_optimizers=critic_optimizers, + policy_optimizer=policy_optimizer, + critic_optimizer=critic_optimizer, max_gradient_norm=max_gradient_norm, counter=counter, logger=logger, diff --git a/mava/systems/tf/mappo/builder.py b/mava/systems/tf/mappo/builder.py index f3c7e9f6e..431ecdeee 100644 --- a/mava/systems/tf/mappo/builder.py +++ b/mava/systems/tf/mappo/builder.py @@ -14,7 +14,6 @@ # limitations under the License. """MAPPO builder and config.""" -import copy import dataclasses from typing import Dict, Iterator, List, Optional, Type @@ -243,13 +242,6 @@ def make_trainer( policy_networks = networks["policies"] critic_networks = networks["critics"] - # Create optimizers for different agent types. - policy_optimizers: Dict[str, snt.Optimizer] = {} - critic_optimizers: Dict[str, snt.Optimizer] = {} - for agent in self._agent_types: - policy_optimizers[agent] = copy.deepcopy(self._config.policy_optimizer) - critic_optimizers[agent] = copy.deepcopy(self._config.critic_optimizer) - # The learner updates the parameters (and initializes them). trainer = self._trainer_fn( agents=agents, @@ -259,8 +251,8 @@ def make_trainer( critic_networks=critic_networks, dataset=dataset, shared_weights=shared_weights, - critic_optimizers=critic_optimizers, - policy_optimizers=policy_optimizers, + critic_optimizer=self._config.critic_optimizer, + policy_optimizer=self._config.policy_optimizer, discount=self._config.discount, lambda_gae=self._config.lambda_gae, entropy_cost=self._config.entropy_cost, diff --git a/mava/systems/tf/mappo/training.py b/mava/systems/tf/mappo/training.py index 08b92049b..793600218 100644 --- a/mava/systems/tf/mappo/training.py +++ b/mava/systems/tf/mappo/training.py @@ -14,6 +14,7 @@ # limitations under the License. """MAPPO trainer implementation.""" +import copy import os import time from typing import Any, Dict, List, Optional, Sequence @@ -47,8 +48,8 @@ def __init__( policy_networks: Dict[str, snt.Module], critic_networks: Dict[str, snt.Module], dataset: tf.data.Dataset, - policy_optimizers: Dict[str, snt.Optimizer], - critic_optimizers: Dict[str, snt.Optimizer], + policy_optimizer: snt.Optimizer, + critic_optimizer: snt.Optimizer, shared_weights: bool, discount: float = 0.99, lambda_gae: float = 1.0, @@ -65,8 +66,8 @@ def __init__( Args: policy_networks: ... critic_networks: ... - policy_optimizers: policy optimizer, - critic_optimizers: critic optimizer, + policy_optimizer: policy optimizer, + critic_optimizer: critic optimizer, shared_weights: ... discount: discount to use for TD updates. dataset: dataset to learn from, whether fixed or from a replay buffer @@ -95,10 +96,6 @@ def __init__( self._policy_networks = policy_networks self._critic_networks = critic_networks - # Get optimizers - self._policy_optimizers = policy_optimizers - self._critic_optimizers = critic_optimizers - # Dictionary with network keys for each agent. self.agent_net_keys = {agent: agent for agent in self._agents} if self._shared_weights: @@ -106,6 +103,14 @@ def __init__( self.unique_net_keys = self._agent_types if shared_weights else self._agents + # Create optimizers for different agent types. + # TODO(Kale-ab): Allow this to be passed as a system param. + self._policy_optimizers: snt.Optimizer = {} + self._critic_optimizers: snt.Optimizer = {} + for agent in self.unique_net_keys: + self._policy_optimizers[agent] = copy.deepcopy(policy_optimizer) + self._critic_optimizers[agent] = copy.deepcopy(critic_optimizer) + # Expose the variables. policy_networks_to_expose = {} self._system_network_variables: Dict[str, Dict[str, snt.Module]] = { @@ -156,8 +161,8 @@ def __init__( "policy": self._policy_networks[agent_key], "critic": self._critic_networks[agent_key], "observation": self._observation_networks[agent_key], - "policy_optimizers": self._policy_optimizers, - "critic_optimizers": self._critic_optimizers, + "policy_optimizer": self._policy_optimizers, + "critic_optimizer": self._critic_optimizers, } subdir = os.path.join("trainer", agent_key) @@ -412,8 +417,8 @@ def __init__( policy_networks: Dict[str, snt.Module], critic_networks: Dict[str, snt.Module], dataset: tf.data.Dataset, - policy_optimizers: Dict[str, snt.Optimizer], - critic_optimizers: Dict[str, snt.Optimizer], + policy_optimizer: snt.Optimizer, + critic_optimizer: snt.Optimizer, shared_weights: bool, discount: float = 0.99, lambda_gae: float = 1.0, @@ -435,8 +440,8 @@ def __init__( observation_networks=observation_networks, dataset=dataset, shared_weights=shared_weights, - policy_optimizers=policy_optimizers, - critic_optimizers=critic_optimizers, + policy_optimizer=policy_optimizer, + critic_optimizer=critic_optimizer, discount=discount, lambda_gae=lambda_gae, entropy_cost=entropy_cost, From 01e3ff2e7f6973c6da0f2864a1b020d2ac39335c Mon Sep 17 00:00:00 2001 From: Kale-ab Date: Mon, 24 May 2021 18:20:47 +0200 Subject: [PATCH 067/188] feat: Updated dqn, qmix and vdn trainers. --- ...dqn.py => run_decentralised_feedforward_madqn.py} | 2 +- mava/systems/tf/madqn/training.py | 12 +++++++++--- mava/systems/tf/qmix/training.py | 2 +- mava/systems/tf/vdn/training.py | 2 +- 4 files changed, 12 insertions(+), 6 deletions(-) rename examples/petting_zoo/{run_feedforward_madqn.py => run_decentralised_feedforward_madqn.py} (98%) diff --git a/examples/petting_zoo/run_feedforward_madqn.py b/examples/petting_zoo/run_decentralised_feedforward_madqn.py similarity index 98% rename from examples/petting_zoo/run_feedforward_madqn.py rename to examples/petting_zoo/run_decentralised_feedforward_madqn.py index 6ae205151..1ef4a1aed 100644 --- a/examples/petting_zoo/run_feedforward_madqn.py +++ b/examples/petting_zoo/run_decentralised_feedforward_madqn.py @@ -50,7 +50,7 @@ str(datetime.now()), "Experiment identifier that can be used to continue experiments.", ) -flags.DEFINE_string("base_dir", "./logs/", "Base dir to store experiments.") +flags.DEFINE_string("base_dir", "~/mava/", "Base dir to store experiments.") def make_networks( diff --git a/mava/systems/tf/madqn/training.py b/mava/systems/tf/madqn/training.py index ffd1293ee..8456dae82 100644 --- a/mava/systems/tf/madqn/training.py +++ b/mava/systems/tf/madqn/training.py @@ -13,6 +13,7 @@ # See the License for the specific language governing permissions and # limitations under the License. +import copy import time from typing import Any, Dict, List, Optional, Sequence, Tuple @@ -62,7 +63,6 @@ def __init__( self._agents = agents self._agent_types = agent_types self._shared_weights = shared_weights - self._optimizer = optimizer self._checkpoint = checkpoint # Store online and target q-networks. @@ -95,6 +95,12 @@ def __init__( self.unique_net_keys = self._agent_types if shared_weights else self._agents + # Create optimizers for different agent types. + # TODO(Kale-ab): Allow this to be passed as a system param. + self._optimizers: snt.Optimizer = {} + for agent in self.unique_net_keys: + self._optimizers[agent] = copy.deepcopy(optimizer) + # Expose the variables. q_networks_to_expose = {} self._system_network_variables: Dict[str, Dict[str, snt.Module]] = { @@ -121,7 +127,7 @@ def __init__( "counter": self._counter, "q_network": self._q_networks[agent_key], "target_q_network": self._target_q_networks[agent_key], - "optimizer": self._optimizer, + "optimizer": self._optimizers, "num_steps": self._num_steps, }, enable_checkpointing=checkpoint, @@ -298,7 +304,7 @@ def _backward(self) -> None: gradients = tf.clip_by_global_norm(gradients, 40.0)[0] # Apply gradients. - self._optimizer.apply(gradients, q_network_variables) + self._optimizers[agent_key].apply(gradients, q_network_variables) train_utils.safe_del(self, "tape") diff --git a/mava/systems/tf/qmix/training.py b/mava/systems/tf/qmix/training.py index 252828182..212aad6df 100644 --- a/mava/systems/tf/qmix/training.py +++ b/mava/systems/tf/qmix/training.py @@ -229,7 +229,7 @@ def _backward(self) -> None: gradients = tf.clip_by_global_norm(gradients, 40.0)[0] # Apply gradients. - self._optimizer.apply(gradients, trainable_variables) + self._optimizers[agent_key].apply(gradients, trainable_variables) # Delete the tape manually because of the persistent=True flag. train_utils.safe_del(self, "tape") diff --git a/mava/systems/tf/vdn/training.py b/mava/systems/tf/vdn/training.py index d48080bb2..951c14099 100644 --- a/mava/systems/tf/vdn/training.py +++ b/mava/systems/tf/vdn/training.py @@ -169,7 +169,7 @@ def _backward(self) -> None: gradients = tf.clip_by_global_norm(gradients, 40.0)[0] # Apply gradients. - self._optimizer.apply(gradients, trainable_variables) + self._optimizers[agent_key].apply(gradients, trainable_variables) # Delete the tape manually because of the persistent=True flag. train_utils.safe_del(self, "tape") From fb6c50a779896bca33bff854d3bc79a01c55c674 Mon Sep 17 00:00:00 2001 From: Kale-ab Date: Mon, 24 May 2021 18:33:43 +0200 Subject: [PATCH 068/188] fix: Updated types. --- mava/systems/tf/mad4pg/training.py | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/mava/systems/tf/mad4pg/training.py b/mava/systems/tf/mad4pg/training.py index fcfbe8448..c70a50373 100644 --- a/mava/systems/tf/mad4pg/training.py +++ b/mava/systems/tf/mad4pg/training.py @@ -203,8 +203,8 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizer: Dict[str, snt.Optimizer], - critic_optimizer: Dict[str, snt.Optimizer], + policy_optimizer: snt.Optimizer, + critic_optimizer: snt.Optimizer, discount: float, target_averaging: bool, target_update_period: int, @@ -281,8 +281,8 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizer: Dict[str, snt.Optimizer], - critic_optimizer: Dict[str, snt.Optimizer], + policy_optimizer: snt.Optimizer, + critic_optimizer: snt.Optimizer, discount: float, target_averaging: bool, target_update_period: int, @@ -359,8 +359,8 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizer: Dict[str, snt.Optimizer], - critic_optimizer: Dict[str, snt.Optimizer], + policy_optimizer: snt.Optimizer, + critic_optimizer: snt.Optimizer, discount: float, target_averaging: bool, target_update_period: int, @@ -437,8 +437,8 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizer: Dict[str, snt.Optimizer], - critic_optimizer: Dict[str, snt.Optimizer], + policy_optimizer: snt.Optimizer, + critic_optimizer: snt.Optimizer, discount: float, target_averaging: bool, target_update_period: int, @@ -643,8 +643,8 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizer: Dict[str, snt.Optimizer], - critic_optimizer: Dict[str, snt.Optimizer], + policy_optimizer: snt.Optimizer, + critic_optimizer: snt.Optimizer, discount: float, target_averaging: bool, target_update_period: int, @@ -725,8 +725,8 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizer: Dict[str, snt.Optimizer], - critic_optimizer: Dict[str, snt.Optimizer], + policy_optimizer: snt.Optimizer, + critic_optimizer: snt.Optimizer, discount: float, target_averaging: bool, target_update_period: int, @@ -807,8 +807,8 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizer: Dict[str, snt.Optimizer], - critic_optimizer: Dict[str, snt.Optimizer], + policy_optimizer: snt.Optimizer, + critic_optimizer: snt.Optimizer, discount: float, target_averaging: bool, target_update_period: int, From aaaca0f70e60cd7d4b57d70d585c1b406466238b Mon Sep 17 00:00:00 2001 From: Dries Date: Mon, 24 May 2021 19:01:29 +0200 Subject: [PATCH 069/188] fix: Nothing. --- mava/components/tf/networks/mixers.py | 56 +++++++++++++++++++++++++++ mava/systems/tf/qmixV2/builder.py | 2 +- mava/systems/tf/qmixV2/training.py | 9 +++-- 3 files changed, 62 insertions(+), 5 deletions(-) diff --git a/mava/components/tf/networks/mixers.py b/mava/components/tf/networks/mixers.py index b9aab0753..6267776ee 100644 --- a/mava/components/tf/networks/mixers.py +++ b/mava/components/tf/networks/mixers.py @@ -36,6 +36,10 @@ def __init__(self, n_agents: Any, state_shape: Any, mixing_embed_dim: Any) -> No [snt.Linear(self.embed_dim), tf.keras.layers.ReLU(), snt.Linear(1)] ) + self.linear_layer = snt.Sequential( + [snt.Linear(self.n_agents)]#, tf.keras.layers.ReLU()] + ) + def __call__(self, agent_qs: Any, states: Any) -> tf.Tensor: """Forward pass for the mixer. Args: @@ -57,7 +61,59 @@ def __call__(self, agent_qs: Any, states: Any) -> tf.Tensor: # State-dependent bias v = tf.reshape(self.V(states), (-1, 1, 1)) # Compute final output + + # print("hidden: ", hidden.shape) + # print("w_final: ", w_final.shape) + # print("v: ", v.shape) + y = tf.linalg.matmul(hidden, w_final) + v + # print("tf.linalg.matmul(hidden, w_final): ", tf.linalg.matmul(hidden, w_final).shape) + # print("y: ", y.shape) + # Reshape and return q_tot = tf.reshape(y, (bs, -1, 1)) + # print("q_tot: ", q_tot.shape) return q_tot + + # hidden: (256, 1, 32) + # w_final: (256, 32, 1) + # tf.linalg.matmul(hidden, w_final): (256, 1, 1) + + + # v: (256, 1, 1) + + # y: (256, 1, 1) + # q_tot: (256, 1, 1) + + # def __call__(self, agent_qs: Any, states: Any) -> tf.Tensor: + # """Forward pass for the mixer. + # Args: + # agent_qs: Tensor of shape [B, n_agents] + # states: Tensor of shape [B, state_dim] + # """ + # bs = agent_qs.shape[0] + # states = tf.reshape(states, (-1, self.state_dim)) + # # First layer + # # print("states: ", states.shape) # 1, 19 + # w = self.linear_layer(states) + # + # w = tf.math.abs(w) + # + # # print("agent_qs: ", agent_qs.shape) + # # print("w: ", w.shape) # 1, 3 + # + # # out = agent_qs*w + # + # # y: (256, 1, 1) + # # q_tot: (256, 1, 1) + # # hidden: (256, 1, 256) + # # w_final: (256, 256, 1) + # # v: (256, 1, 1) + # # tf.linalg.matmul(hidden, w_final): (256, 1, 1) + # # y: (256, 1, 1) + # # q_tot: (256, 1, 1) + # + # y = tf.linalg.matmul(hidden, w_final) # + v + # q_tot = tf.reshape(y, (bs, -1, 1)) + # return q_tot + diff --git a/mava/systems/tf/qmixV2/builder.py b/mava/systems/tf/qmixV2/builder.py index 27f86c595..398b0f1b8 100644 --- a/mava/systems/tf/qmixV2/builder.py +++ b/mava/systems/tf/qmixV2/builder.py @@ -298,7 +298,7 @@ def make_trainer( # Mixer if self._mixer_fn: state_spec = self._config.environment_spec.get_extra_specs()["s_t"] - mixer = self._mixer_fn(len(agents), state_spec.shape, 256) + mixer = self._mixer_fn(len(agents), state_spec.shape, 32) tf_utils.create_variables(mixer, [tf.ones(len(agents)), state_spec]) # The learner updates the parameters (and initializes them). diff --git a/mava/systems/tf/qmixV2/training.py b/mava/systems/tf/qmixV2/training.py index 0976c04fc..20fc18d7e 100644 --- a/mava/systems/tf/qmixV2/training.py +++ b/mava/systems/tf/qmixV2/training.py @@ -297,11 +297,12 @@ def _forward(self, inputs: Any) -> None: q_acts = tf.concat(q_acts, axis=1) # [B, num_agents] q_targets = tf.concat(q_targets, axis=1) # [B, num_agents] - # q_tot_mixed = self._mixer(q_acts, s_tm1) # [B, 1, 1] - # q_tot_target_mixed = self._target_mixer(q_targets, s_t) # [B, 1, 1] - q_tot_mixed = tf.reduce_sum(q_acts, axis=1) # [B, 1, 1] - q_tot_target_mixed = tf.reduce_sum(q_targets, axis=1) # [B, 1, 1] + q_tot_mixed = self._mixer(q_acts, s_tm1) # [B, 1, 1] + q_tot_target_mixed = self._target_mixer(q_targets, s_t) # [B, 1, 1] + + # q_tot_mixed = tf.reduce_sum(q_acts, axis=1) # [B, 1, 1] + # q_tot_target_mixed = tf.reduce_sum(q_targets, axis=1) # [B, 1, 1] q_tot_mixed = tf.reshape(q_tot_mixed, (-1,)) q_tot_target_mixed = tf.reshape(q_tot_target_mixed, (-1,)) From 6749a61efd8b2a2d5c8a3d7e1579e28ccb933b9b Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Mon, 24 May 2021 19:23:01 +0200 Subject: [PATCH 070/188] debug/small changes --- mava/components/tf/networks/mixers.py | 29 ++++++++++++++++++--------- 1 file changed, 19 insertions(+), 10 deletions(-) diff --git a/mava/components/tf/networks/mixers.py b/mava/components/tf/networks/mixers.py index 6267776ee..f7dc075f9 100644 --- a/mava/components/tf/networks/mixers.py +++ b/mava/components/tf/networks/mixers.py @@ -37,7 +37,7 @@ def __init__(self, n_agents: Any, state_shape: Any, mixing_embed_dim: Any) -> No ) self.linear_layer = snt.Sequential( - [snt.Linear(self.n_agents)]#, tf.keras.layers.ReLU()] + [snt.Linear(self.n_agents)] # , tf.keras.layers.ReLU()] ) def __call__(self, agent_qs: Any, states: Any) -> tf.Tensor: @@ -48,30 +48,41 @@ def __call__(self, agent_qs: Any, states: Any) -> tf.Tensor: """ bs = agent_qs.shape[0] states = tf.reshape(states, (-1, self.state_dim)) - agent_qs = tf.reshape(agent_qs, (-1, 1, self.n_agents)) + print("states reshape: ", states.shape) + agent_qs = tf.reshape(agent_qs, (-1, self.n_agents)) + print("agent_qs: ", agent_qs.shape) # First layer w1 = tf.math.abs(self.hyper_w_1(states)) + print("w1: ", w1.shape) b1 = self.hyper_b_1(states) + print("b1: ", b1.shape) w1 = tf.reshape(w1, (-1, self.n_agents, self.embed_dim)) - b1 = tf.reshape(b1, (-1, 1, self.embed_dim)) + print("w1: ", w1.shape) + b1 = tf.reshape(b1, (-1, self.embed_dim)) + print("b1: ", b1.shape) hidden = tf.keras.activations.elu(tf.linalg.matmul(agent_qs, w1) + b1) + print("hidden: ", hidden.shape) + hidden = tf.reshape(hidden, (-1, self.embed_dim)) # Second layer w_final = tf.math.abs(self.hyper_w_final(states)) - w_final = tf.reshape(w_final, (-1, self.embed_dim, 1)) + print("w_final: ", w_final.shape) + w_final = tf.reshape(w_final, (-1, self.embed_dim)) + print("w_final: ", w_final.shape) # State-dependent bias - v = tf.reshape(self.V(states), (-1, 1, 1)) + v = tf.reshape(self.V(states), (-1, 1)) + print("v: ", v.shape) # Compute final output # print("hidden: ", hidden.shape) # print("w_final: ", w_final.shape) # print("v: ", v.shape) - y = tf.linalg.matmul(hidden, w_final) + v + y = tf.linalg.matmul(hidden, w_final, transpose_b=True) + v # print("tf.linalg.matmul(hidden, w_final): ", tf.linalg.matmul(hidden, w_final).shape) - # print("y: ", y.shape) + print("y: ", y.shape) # Reshape and return - q_tot = tf.reshape(y, (bs, -1, 1)) + q_tot = tf.reshape(y, (bs, -1)) # print("q_tot: ", q_tot.shape) return q_tot @@ -79,7 +90,6 @@ def __call__(self, agent_qs: Any, states: Any) -> tf.Tensor: # w_final: (256, 32, 1) # tf.linalg.matmul(hidden, w_final): (256, 1, 1) - # v: (256, 1, 1) # y: (256, 1, 1) @@ -116,4 +126,3 @@ def __call__(self, agent_qs: Any, states: Any) -> tf.Tensor: # y = tf.linalg.matmul(hidden, w_final) # + v # q_tot = tf.reshape(y, (bs, -1, 1)) # return q_tot - From 0765401ca14dddff8df21518c4a9f6b6fc6ac4ad Mon Sep 17 00:00:00 2001 From: Dries Date: Mon, 24 May 2021 21:25:20 +0200 Subject: [PATCH 071/188] fix: Small changes. --- .../debugging_envs/run_feedforward_qmix_v2.py | 4 +- mava/components/tf/networks/mixers.py | 76 +++++++------------ mava/systems/tf/qmixV2/system.py | 1 - 3 files changed, 31 insertions(+), 50 deletions(-) diff --git a/examples/debugging_envs/run_feedforward_qmix_v2.py b/examples/debugging_envs/run_feedforward_qmix_v2.py index b4bb850b5..2dcc6dc88 100644 --- a/examples/debugging_envs/run_feedforward_qmix_v2.py +++ b/examples/debugging_envs/run_feedforward_qmix_v2.py @@ -149,9 +149,9 @@ def main(_: Any) -> None: num_executors=2, exploration_scheduler_fn=LinearExplorationScheduler, epsilon_min=0.05, - epsilon_decay=5e-4, + epsilon_decay=3e-4, mixer_fn=QMixer, - optimizer=snt.optimizers.Adam(learning_rate=1e-4), + optimizer=snt.optimizers.RMSProp(learning_rate=1e-4), checkpoint_subpath=checkpoint_dir, ).build() diff --git a/mava/components/tf/networks/mixers.py b/mava/components/tf/networks/mixers.py index f7dc075f9..f953a3931 100644 --- a/mava/components/tf/networks/mixers.py +++ b/mava/components/tf/networks/mixers.py @@ -25,7 +25,7 @@ def __init__(self, n_agents: Any, state_shape: Any, mixing_embed_dim: Any) -> No self.embed_dim = mixing_embed_dim self.state_dim = int(np.prod(state_shape)) - self.hyper_w_1 = snt.Linear(self.embed_dim * self.n_agents) + self.hyper_w_1 = snt.Linear(self.embed_dim*self.n_agents) self.hyper_w_final = snt.Linear(self.embed_dim) # State dependent bias for hidden layer @@ -36,9 +36,9 @@ def __init__(self, n_agents: Any, state_shape: Any, mixing_embed_dim: Any) -> No [snt.Linear(self.embed_dim), tf.keras.layers.ReLU(), snt.Linear(1)] ) - self.linear_layer = snt.Sequential( - [snt.Linear(self.n_agents)] # , tf.keras.layers.ReLU()] - ) + # self.linear_layer = snt.Sequential( + # [snt.Linear(self.n_agents)] # , tf.keras.layers.ReLU()] + # ) def __call__(self, agent_qs: Any, states: Any) -> tf.Tensor: """Forward pass for the mixer. @@ -48,41 +48,27 @@ def __call__(self, agent_qs: Any, states: Any) -> tf.Tensor: """ bs = agent_qs.shape[0] states = tf.reshape(states, (-1, self.state_dim)) - print("states reshape: ", states.shape) - agent_qs = tf.reshape(agent_qs, (-1, self.n_agents)) - print("agent_qs: ", agent_qs.shape) + agent_qs = tf.reshape(agent_qs, (-1, 1, self.n_agents)) # First layer w1 = tf.math.abs(self.hyper_w_1(states)) - print("w1: ", w1.shape) b1 = self.hyper_b_1(states) - print("b1: ", b1.shape) w1 = tf.reshape(w1, (-1, self.n_agents, self.embed_dim)) - print("w1: ", w1.shape) - b1 = tf.reshape(b1, (-1, self.embed_dim)) - print("b1: ", b1.shape) + b1 = tf.reshape(b1, (-1, 1, self.embed_dim)) hidden = tf.keras.activations.elu(tf.linalg.matmul(agent_qs, w1) + b1) - print("hidden: ", hidden.shape) - hidden = tf.reshape(hidden, (-1, self.embed_dim)) # Second layer w_final = tf.math.abs(self.hyper_w_final(states)) - print("w_final: ", w_final.shape) - w_final = tf.reshape(w_final, (-1, self.embed_dim)) - print("w_final: ", w_final.shape) + w_final = tf.reshape(w_final, (-1, self.embed_dim, 1)) # State-dependent bias - v = tf.reshape(self.V(states), (-1, 1)) - print("v: ", v.shape) + v = tf.reshape(self.V(states), (-1, 1, 1)) # Compute final output - # print("hidden: ", hidden.shape) # print("w_final: ", w_final.shape) # print("v: ", v.shape) - - y = tf.linalg.matmul(hidden, w_final, transpose_b=True) + v + y = tf.linalg.matmul(hidden, w_final) + v # print("tf.linalg.matmul(hidden, w_final): ", tf.linalg.matmul(hidden, w_final).shape) - print("y: ", y.shape) - + # print("y: ", y.shape) # Reshape and return - q_tot = tf.reshape(y, (bs, -1)) + q_tot = tf.reshape(y, (bs, -1, 1)) # print("q_tot: ", q_tot.shape) return q_tot @@ -102,27 +88,23 @@ def __call__(self, agent_qs: Any, states: Any) -> tf.Tensor: # states: Tensor of shape [B, state_dim] # """ # bs = agent_qs.shape[0] - # states = tf.reshape(states, (-1, self.state_dim)) + # states = tf.reshape(states, (-1, self.state_dim)) # (1, num_states) + # print("states: ", states.shape) + # agent_qs = tf.reshape(agent_qs, (-1, 1, self.n_agents)) + # print("agent_qs: ", agent_qs.shape) # # First layer - # # print("states: ", states.shape) # 1, 19 - # w = self.linear_layer(states) - # - # w = tf.math.abs(w) - # - # # print("agent_qs: ", agent_qs.shape) - # # print("w: ", w.shape) # 1, 3 - # - # # out = agent_qs*w - # - # # y: (256, 1, 1) - # # q_tot: (256, 1, 1) - # # hidden: (256, 1, 256) - # # w_final: (256, 256, 1) - # # v: (256, 1, 1) - # # tf.linalg.matmul(hidden, w_final): (256, 1, 1) - # # y: (256, 1, 1) - # # q_tot: (256, 1, 1) - # - # y = tf.linalg.matmul(hidden, w_final) # + v + # w1 = tf.math.abs(self.hyper_w_1(states)) # bs, n_agents + # b1 = self.hyper_b_1(states) + # print("w1 before: ", w1.shape) + # w1 = tf.reshape(w1, (-1, self.n_agents, 1)) + # print("w1: ", w1.shape) + # b1 = tf.reshape(b1, (-1, 1, 1)) + # print("b1: ", b1.shape) + # # Compute final output + # print("agent_qs: ", agent_qs.shape) + # print("tf.transpose(w1): ", w1.shape) + # y = tf.linalg.matmul(agent_qs, w1) + b1 + # print("y: ", y.shape) # q_tot = tf.reshape(y, (bs, -1, 1)) - # return q_tot + # print("q_tot: ", q_tot.shape) + # return q_tot \ No newline at end of file diff --git a/mava/systems/tf/qmixV2/system.py b/mava/systems/tf/qmixV2/system.py index 06097509e..319308146 100644 --- a/mava/systems/tf/qmixV2/system.py +++ b/mava/systems/tf/qmixV2/system.py @@ -116,7 +116,6 @@ def __init__( train_loop_fn_kwargs: Dict = {}, eval_loop_fn_kwargs: Dict = {}, ): - if not environment_spec: environment_spec = mava_specs.MAEnvironmentSpec( environment_factory(evaluation=False) # type:ignore From 381490e7c77bd446607a84d93bbe221f866f4f2e Mon Sep 17 00:00:00 2001 From: Dries Date: Tue, 25 May 2021 05:41:09 +0200 Subject: [PATCH 072/188] fix: Fix decentralised maddpg example. --- examples/petting_zoo/run_decentralised_recurrent_maddpg.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/petting_zoo/run_decentralised_recurrent_maddpg.py b/examples/petting_zoo/run_decentralised_recurrent_maddpg.py index de588cad6..2f6e7f046 100644 --- a/examples/petting_zoo/run_decentralised_recurrent_maddpg.py +++ b/examples/petting_zoo/run_decentralised_recurrent_maddpg.py @@ -31,7 +31,7 @@ from mava import specs as mava_specs from mava.systems.tf import maddpg from mava.systems.tf.maddpg.execution import MADDPGRecurrentExecutor -from mava.systems.tf.maddpg.training import StateBasedRecurrentMADDPGTrainer +from mava.systems.tf.maddpg.training import DecentralisedRecurrentMADDPGTrainer from mava.utils import lp_utils from mava.utils.environments import pettingzoo_utils from mava.utils.loggers import logger_utils @@ -163,7 +163,7 @@ def main(_: Any) -> None: network_factory=network_factory, logger_factory=logger_factory, num_executors=2, - trainer_fn=StateBasedRecurrentMADDPGTrainer, + trainer_fn=DecentralisedRecurrentMADDPGTrainer, executor_fn=MADDPGRecurrentExecutor, policy_optimizer=snt.optimizers.Adam(learning_rate=1e-4), critic_optimizer=snt.optimizers.Adam(learning_rate=1e-4), From 6bc7bdeca6c617512b4407e9850408e213dfeffa Mon Sep 17 00:00:00 2001 From: Dries Date: Tue, 25 May 2021 05:44:26 +0200 Subject: [PATCH 073/188] fix: mypy. --- examples/README.md | 5 ++--- .../debugging_envs/run_state_based_recurrent_maddpg.py | 3 ++- mava/systems/tf/mad4pg/training.py | 6 ++++-- mava/systems/tf/maddpg/training.py | 10 +++++++--- 4 files changed, 15 insertions(+), 9 deletions(-) diff --git a/examples/README.md b/examples/README.md index b48f814dd..b99aac4ce 100644 --- a/examples/README.md +++ b/examples/README.md @@ -29,7 +29,7 @@ We include a number of systems running on continuous control tasks. an MA-DDPG system (with video wrapping) on the continuous action space simple_spread MPE debugging environment. - [MA-DDPG (networked, feedforward)](debugging_envs/run_networked_feedforward_maddpg.py): an MA-D4PG system on the continuous action space simple_spread MPE debugging environment. - + - [MA-DDPG (state-based, recurrent)](debugging_envs/run_state_based_recurrent_maddpg.py): an MA-D4PG system on the continuous action space simple_spread MPE debugging environment. - [PPO (centralised, feedforward)](petting_zoo/run_centralised_feedforward_mappo.py): @@ -49,7 +49,7 @@ We include a number of systems running on continuous control tasks. We also include a number of systems running on discrete action space environments. - [MA-D4PG (decentralised, feedforward)](debugging_envs/run_decentralised_feedforward_discrete_mad4pg.py): - an MA-D4PG system on the discrete action space simple_spread MPE debugging environment. + an MA-D4PG system on the discrete action space simple_spread MPE debugging environment. - [MA-DDPG (decentralised, feedforward)](debugging_envs/run_decentralised_feedforward_discrete_maddpg.py): an MA-DDPG system on the discrete action space simple_spread MPE debugging environment. - [MA-DQN (feedforward)](debugging_envs/run_feedforward_madqn.py): @@ -74,4 +74,3 @@ We also include a number of systems running on discrete action space environment a QMIX system on the two step custom environment. - [VDN](petting_zoo/run_feedforward_vdn.py): a VDN system on the two step custom environment. - diff --git a/examples/debugging_envs/run_state_based_recurrent_maddpg.py b/examples/debugging_envs/run_state_based_recurrent_maddpg.py index 419749d4f..c92322d1a 100644 --- a/examples/debugging_envs/run_state_based_recurrent_maddpg.py +++ b/examples/debugging_envs/run_state_based_recurrent_maddpg.py @@ -28,9 +28,9 @@ from launchpad.nodes.python.local_multi_processing import PythonProcess from mava import specs as mava_specs +from mava.components.tf.architectures import StateBasedQValueCritic from mava.systems.tf import maddpg from mava.systems.tf.maddpg.execution import MADDPGRecurrentExecutor -from mava.components.tf.architectures import StateBasedQValueCritic from mava.systems.tf.maddpg.training import StateBasedRecurrentMADDPGTrainer from mava.utils import lp_utils from mava.utils.environments import debugging_utils @@ -184,5 +184,6 @@ def main(_: Any) -> None: local_resources=local_resources, ) + if __name__ == "__main__": app.run(main) diff --git a/mava/systems/tf/mad4pg/training.py b/mava/systems/tf/mad4pg/training.py index c2b123776..a6f82728a 100644 --- a/mava/systems/tf/mad4pg/training.py +++ b/mava/systems/tf/mad4pg/training.py @@ -601,11 +601,13 @@ def _forward(self, inputs: Any) -> None: dpg_actions = tf2_utils.batch_to_sequence(outputs) - # Note (dries): This is done to so that losses.dpg can verify using gradient.tape that there is a + # Note (dries): This is done to so that losses.dpg can verify + # using gradient.tape that there is a # gradient relationship between dpg_q_values and dpg_actions_comb. dpg_actions_comb, dim = train_utils.combine_dim(dpg_actions) - # Note (dries): This seemingly useless line is is important! Don't remove it. See above note. + # Note (dries): This seemingly useless line is is important! + # Don't remove it. See above note. dpg_actions = train_utils.extract_dim(dpg_actions_comb, dim) # Get dpg actions diff --git a/mava/systems/tf/maddpg/training.py b/mava/systems/tf/maddpg/training.py index 209399eab..0bfd43387 100644 --- a/mava/systems/tf/maddpg/training.py +++ b/mava/systems/tf/maddpg/training.py @@ -1272,11 +1272,13 @@ def _forward(self, inputs: Any) -> None: dpg_actions = tf2_utils.batch_to_sequence(outputs) - # Note (dries): This is done to so that losses.dpg can verify using gradient.tape that there is a + # Note (dries): This is done to so that losses.dpg + # can verify using gradient.tape that there is a # gradient relationship between dpg_q_values and dpg_actions_comb. dpg_actions_comb, dim = train_utils.combine_dim(dpg_actions) - # Note (dries): This seemingly useless line is is important! Don't remove it. See above note. + # Note (dries): This seemingly useless line is is important! + # Don't remove it. See above note. dpg_actions = train_utils.extract_dim(dpg_actions_comb, dim) # Get dpg actions @@ -1288,7 +1290,9 @@ def _forward(self, inputs: Any) -> None: obs_comb, _ = train_utils.combine_dim(target_obs_trans_feed) act_comb, _ = train_utils.combine_dim(dpg_actions_feed) - dpg_q_values = tf.squeeze(self._critic_networks[agent_key](obs_comb, act_comb)) + dpg_q_values = tf.squeeze( + self._critic_networks[agent_key](obs_comb, act_comb) + ) # Actor loss. If clipping is true use dqda clipping and clip the norm. dqda_clipping = 1.0 if self._max_gradient_norm is not None else None From 3ad1cf0233a77a6e4f7d9c7c14d8a64e881b3fa4 Mon Sep 17 00:00:00 2001 From: Dries Date: Tue, 25 May 2021 05:49:19 +0200 Subject: [PATCH 074/188] fix: Small fixes. --- mava/systems/tf/mad4pg/training.py | 2 +- mava/systems/tf/maddpg/training.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/mava/systems/tf/mad4pg/training.py b/mava/systems/tf/mad4pg/training.py index a6f82728a..aa31b8362 100644 --- a/mava/systems/tf/mad4pg/training.py +++ b/mava/systems/tf/mad4pg/training.py @@ -606,7 +606,7 @@ def _forward(self, inputs: Any) -> None: # gradient relationship between dpg_q_values and dpg_actions_comb. dpg_actions_comb, dim = train_utils.combine_dim(dpg_actions) - # Note (dries): This seemingly useless line is is important! + # Note (dries): This seemingly useless line is important! # Don't remove it. See above note. dpg_actions = train_utils.extract_dim(dpg_actions_comb, dim) diff --git a/mava/systems/tf/maddpg/training.py b/mava/systems/tf/maddpg/training.py index 0bfd43387..c727b3769 100644 --- a/mava/systems/tf/maddpg/training.py +++ b/mava/systems/tf/maddpg/training.py @@ -1277,7 +1277,7 @@ def _forward(self, inputs: Any) -> None: # gradient relationship between dpg_q_values and dpg_actions_comb. dpg_actions_comb, dim = train_utils.combine_dim(dpg_actions) - # Note (dries): This seemingly useless line is is important! + # Note (dries): This seemingly useless line is important! # Don't remove it. See above note. dpg_actions = train_utils.extract_dim(dpg_actions_comb, dim) From 79addbe8732433144f1f519ab6e1a260d80e8952 Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Tue, 25 May 2021 09:31:45 +0200 Subject: [PATCH 075/188] make q net smaller --- examples/debugging_envs/run_feedforward_qmix_v2.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/examples/debugging_envs/run_feedforward_qmix_v2.py b/examples/debugging_envs/run_feedforward_qmix_v2.py index 2dcc6dc88..1a59cee6c 100644 --- a/examples/debugging_envs/run_feedforward_qmix_v2.py +++ b/examples/debugging_envs/run_feedforward_qmix_v2.py @@ -57,9 +57,8 @@ def make_networks( environment_spec: mava_specs.MAEnvironmentSpec, q_networks_layer_sizes: Union[Dict[str, Sequence], Sequence] = ( - 512, - 512, - 256, + 64, + 64, ), shared_weights: bool = True, ) -> Mapping[str, types.TensorTransformation]: From 3df0196cd4286cbd4dda6d5dd37ee6c4f378f40e Mon Sep 17 00:00:00 2001 From: Kevin Eloff Date: Tue, 25 May 2021 10:16:06 +0200 Subject: [PATCH 076/188] cleanup --- examples/debugging_envs/run_recurrent_dial.py | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/examples/debugging_envs/run_recurrent_dial.py b/examples/debugging_envs/run_recurrent_dial.py index a6fb93ef2..5bf98bc9b 100644 --- a/examples/debugging_envs/run_recurrent_dial.py +++ b/examples/debugging_envs/run_recurrent_dial.py @@ -35,9 +35,6 @@ epsilon_greedy_action_selector, ) from mava.systems.tf import dial - -# from mava.systems.tf.madqn.execution import MADQNRecurrentCommExecutor -# from mava.systems.tf.madqn.training import RecurrentCommMADQNTrainer from mava.utils import lp_utils from mava.utils.environments import debugging_utils from mava.utils.loggers import logger_utils @@ -162,13 +159,11 @@ def main(_: Any) -> None: num_executors=2, trainer_fn=dial.DIALSwitchTrainer, executor_fn=dial.DIALSwitchExecutor, - # trainer_fn=RecurrentCommMADQNTrainer, - # executor_fn=MADQNRecurrentCommExecutor, exploration_scheduler_fn=LinearExplorationScheduler, communication_module=BroadcastedCommunication, sequence_length=6, epsilon_min=0.05, - epsilon_decay=5e-4, + epsilon_decay=2.5e-4, # optimizer=snt.optimizers.Adam(learning_rate=1e-4), optimizer=snt.optimizers.RMSProp(learning_rate=1e-4, momentum=0.95), checkpoint_subpath=checkpoint_dir, From a7adc13d713a166ae2fb86e6333fb5b08f793bd5 Mon Sep 17 00:00:00 2001 From: Kevin Eloff Date: Tue, 25 May 2021 11:34:02 +0200 Subject: [PATCH 077/188] pr comments --- examples/debugging_envs/run_recurrent_comm_madqn.py | 4 +--- examples/debugging_envs/run_recurrent_dial.py | 5 +---- mava/systems/tf/executors.py | 2 +- mava/systems/tf/madqn/execution.py | 4 ++-- 4 files changed, 5 insertions(+), 10 deletions(-) diff --git a/examples/debugging_envs/run_recurrent_comm_madqn.py b/examples/debugging_envs/run_recurrent_comm_madqn.py index e8c1169c3..c518e7c54 100644 --- a/examples/debugging_envs/run_recurrent_comm_madqn.py +++ b/examples/debugging_envs/run_recurrent_comm_madqn.py @@ -26,9 +26,7 @@ from launchpad.nodes.python.local_multi_processing import PythonProcess from mava import specs as mava_specs -from mava.components.tf.modules.communication.broadcasted import ( - BroadcastedCommunication, -) +from mava.components.tf.modules.communication import BroadcastedCommunication from mava.components.tf.modules.exploration import LinearExplorationScheduler from mava.components.tf.networks import ( CommunicationNetwork, diff --git a/examples/debugging_envs/run_recurrent_dial.py b/examples/debugging_envs/run_recurrent_dial.py index 5bf98bc9b..fa456fbbc 100644 --- a/examples/debugging_envs/run_recurrent_dial.py +++ b/examples/debugging_envs/run_recurrent_dial.py @@ -26,9 +26,7 @@ from launchpad.nodes.python.local_multi_processing import PythonProcess from mava import specs as mava_specs -from mava.components.tf.modules.communication.broadcasted import ( - BroadcastedCommunication, -) +from mava.components.tf.modules.communication import BroadcastedCommunication from mava.components.tf.modules.exploration import LinearExplorationScheduler from mava.components.tf.networks import ( CommunicationNetwork, @@ -164,7 +162,6 @@ def main(_: Any) -> None: sequence_length=6, epsilon_min=0.05, epsilon_decay=2.5e-4, - # optimizer=snt.optimizers.Adam(learning_rate=1e-4), optimizer=snt.optimizers.RMSProp(learning_rate=1e-4, momentum=0.95), checkpoint_subpath=checkpoint_dir, n_step=1, diff --git a/mava/systems/tf/executors.py b/mava/systems/tf/executors.py index 43c3d4b95..64606c886 100644 --- a/mava/systems/tf/executors.py +++ b/mava/systems/tf/executors.py @@ -272,7 +272,7 @@ def update(self, wait: bool = False) -> None: self._variable_client.update(wait) -class RecurrentExecutorWithComms(RecurrentExecutor): +class RecurrentCommExecutor(RecurrentExecutor): """Recurrent executor where agents can communicate.""" def __init__( diff --git a/mava/systems/tf/madqn/execution.py b/mava/systems/tf/madqn/execution.py index 4358d150b..16c0f665d 100644 --- a/mava/systems/tf/madqn/execution.py +++ b/mava/systems/tf/madqn/execution.py @@ -32,8 +32,8 @@ from mava.components.tf.modules.communication import BaseCommunicationModule from mava.systems.tf.executors import ( FeedForwardExecutor, + RecurrentCommExecutor, RecurrentExecutor, - RecurrentExecutorWithComms, ) from mava.systems.tf.madqn.training import MADQNTrainer from mava.types import OLT @@ -290,7 +290,7 @@ def select_actions( return actions -class MADQNRecurrentCommExecutor(RecurrentExecutorWithComms): +class MADQNRecurrentCommExecutor(RecurrentCommExecutor): """A feed-forward executor. An executor based on a feed-forward policy for each agent in the system which takes non-batched observations and outputs non-batched actions. From cc69a31488a8a2b4268858d4c69ffe3eeba123ef Mon Sep 17 00:00:00 2001 From: Dries Date: Tue, 25 May 2021 12:06:45 +0200 Subject: [PATCH 078/188] fix: Update trainers and examples. --- .../run_centralised_feedforward_maddpg.py | 1 + .../debugging_envs/run_state_based_recurrent_maddpg.py | 1 + mava/systems/tf/maddpg/training.py | 10 +++++----- 3 files changed, 7 insertions(+), 5 deletions(-) diff --git a/examples/debugging_envs/run_centralised_feedforward_maddpg.py b/examples/debugging_envs/run_centralised_feedforward_maddpg.py index fb06e4f1e..98b829731 100644 --- a/examples/debugging_envs/run_centralised_feedforward_maddpg.py +++ b/examples/debugging_envs/run_centralised_feedforward_maddpg.py @@ -184,6 +184,7 @@ def main(_: Any) -> None: checkpoint_subpath=checkpoint_dir, max_gradient_norm=40.0, trainer_fn=maddpg.CentralisedMADDPGTrainer, + shared_weights=False, ).build() # launch diff --git a/examples/debugging_envs/run_state_based_recurrent_maddpg.py b/examples/debugging_envs/run_state_based_recurrent_maddpg.py index c92322d1a..4237dacd6 100644 --- a/examples/debugging_envs/run_state_based_recurrent_maddpg.py +++ b/examples/debugging_envs/run_state_based_recurrent_maddpg.py @@ -167,6 +167,7 @@ def main(_: Any) -> None: trainer_fn=StateBasedRecurrentMADDPGTrainer, executor_fn=MADDPGRecurrentExecutor, checkpoint_subpath=checkpoint_dir, + shared_weights=False, ).build() # launch diff --git a/mava/systems/tf/maddpg/training.py b/mava/systems/tf/maddpg/training.py index c727b3769..dbf0f7381 100644 --- a/mava/systems/tf/maddpg/training.py +++ b/mava/systems/tf/maddpg/training.py @@ -636,8 +636,8 @@ def _get_dpg_feed( # Centralised and StateBased DPG # Note (dries): Copy has to be made because the input # variables cannot be changed. + tree.map_structure(tf.stop_gradient, a_t) dpg_a_t_feed = copy.copy(a_t) - tree.map_structure(tf.stop_gradient, dpg_a_t_feed) dpg_a_t_feed[agent] = dpg_a_t return dpg_a_t_feed @@ -760,8 +760,8 @@ def _get_dpg_feed( # Centralised and StateBased DPG # Note (dries): Copy has to be made because the input # variables cannot be changed. + tree.map_structure(tf.stop_gradient, a_t) dpg_a_t_feed = copy.copy(a_t) - tree.map_structure(tf.stop_gradient, dpg_a_t_feed) dpg_a_t_feed[agent] = dpg_a_t return dpg_a_t_feed @@ -869,8 +869,8 @@ def _get_dpg_feed( # Centralised and StateBased DPG # Note (dries): Copy has to be made because the input # variables cannot be changed. + tree.map_structure(tf.stop_gradient, a_t) dpg_a_t_feed = copy.copy(a_t) - tree.map_structure(tf.stop_gradient, dpg_a_t_feed) dpg_a_t_feed[agent] = dpg_a_t return dpg_a_t_feed @@ -1565,8 +1565,8 @@ def _get_dpg_feed( # Centralised and StateBased DPG # Note (dries): Copy has to be made because the input # variables cannot be changed. + tree.map_structure(tf.stop_gradient, actions) dpg_actions_feed = copy.copy(actions) - tree.map_structure(tf.stop_gradient, dpg_actions_feed) dpg_actions_feed[agent] = dpg_actions dpg_actions_feed = tf.squeeze( tf.stack([x for x in dpg_actions_feed.values()], -1) @@ -1678,8 +1678,8 @@ def _get_dpg_feed( # Centralised and StateBased DPG # Note (dries): Copy has to be made because the input # variables cannot be changed. + tree.map_structure(tf.stop_gradient, actions) dpg_actions_feed = copy.copy(actions) - tree.map_structure(tf.stop_gradient, dpg_actions_feed) dpg_actions_feed[agent] = dpg_actions dpg_actions_feed = tf.squeeze( tf.stack([x for x in dpg_actions_feed.values()], -1) From b008db5bcaa717a76c3461ca76cc025eb32cccc2 Mon Sep 17 00:00:00 2001 From: Kevin Eloff Date: Tue, 25 May 2021 12:28:10 +0200 Subject: [PATCH 079/188] feat: rename trainer functions --- examples/debugging_envs/run_recurrent_comm_madqn.py | 4 ++-- examples/debugging_envs/run_recurrent_madqn.py | 4 ++-- examples/smac/run_recurrent_madqn.py | 4 ++-- mava/systems/tf/dial/builder.py | 6 +++--- mava/systems/tf/dial/system.py | 8 ++++---- mava/systems/tf/dial/training.py | 4 ++-- mava/systems/tf/madqn/__init__.py | 2 +- mava/systems/tf/madqn/system.py | 2 +- mava/systems/tf/madqn/training.py | 4 ++-- 9 files changed, 19 insertions(+), 19 deletions(-) diff --git a/examples/debugging_envs/run_recurrent_comm_madqn.py b/examples/debugging_envs/run_recurrent_comm_madqn.py index c518e7c54..9dbb17bda 100644 --- a/examples/debugging_envs/run_recurrent_comm_madqn.py +++ b/examples/debugging_envs/run_recurrent_comm_madqn.py @@ -34,7 +34,7 @@ ) from mava.systems.tf import madqn from mava.systems.tf.madqn.execution import MADQNRecurrentCommExecutor -from mava.systems.tf.madqn.training import RecurrentCommMADQNTrainer +from mava.systems.tf.madqn.training import MADQNRecurrentCommTrainer from mava.utils import lp_utils from mava.utils.environments import debugging_utils from mava.utils.loggers import logger_utils @@ -157,7 +157,7 @@ def main(_: Any) -> None: network_factory=network_factory, logger_factory=logger_factory, num_executors=2, - trainer_fn=RecurrentCommMADQNTrainer, + trainer_fn=MADQNRecurrentCommTrainer, executor_fn=MADQNRecurrentCommExecutor, exploration_scheduler_fn=LinearExplorationScheduler, communication_module=BroadcastedCommunication, diff --git a/examples/debugging_envs/run_recurrent_madqn.py b/examples/debugging_envs/run_recurrent_madqn.py index 897056799..3f755744e 100644 --- a/examples/debugging_envs/run_recurrent_madqn.py +++ b/examples/debugging_envs/run_recurrent_madqn.py @@ -30,7 +30,7 @@ from mava.components.tf.networks import epsilon_greedy_action_selector from mava.systems.tf import madqn from mava.systems.tf.madqn.execution import MADQNRecurrentExecutor -from mava.systems.tf.madqn.training import RecurrentMADQNTrainer +from mava.systems.tf.madqn.training import MADQNRecurrentTrainer from mava.utils import lp_utils from mava.utils.environments import debugging_utils from mava.utils.loggers import logger_utils @@ -148,7 +148,7 @@ def main(_: Any) -> None: network_factory=network_factory, logger_factory=logger_factory, num_executors=2, - trainer_fn=RecurrentMADQNTrainer, + trainer_fn=MADQNRecurrentTrainer, executor_fn=MADQNRecurrentExecutor, exploration_scheduler_fn=LinearExplorationScheduler, epsilon_min=0.05, diff --git a/examples/smac/run_recurrent_madqn.py b/examples/smac/run_recurrent_madqn.py index 049624851..731f24005 100644 --- a/examples/smac/run_recurrent_madqn.py +++ b/examples/smac/run_recurrent_madqn.py @@ -32,7 +32,7 @@ from mava.components.tf.networks import epsilon_greedy_action_selector from mava.systems.tf import madqn from mava.systems.tf.madqn.execution import MADQNRecurrentExecutor -from mava.systems.tf.madqn.training import RecurrentMADQNTrainer +from mava.systems.tf.madqn.training import MADQNRecurrentTrainer from mava.utils import lp_utils from mava.utils.environments import smac_utils from mava.utils.loggers import logger_utils @@ -143,7 +143,7 @@ def main(_: Any) -> None: network_factory=network_factory, logger_factory=logger_factory, num_executors=1, - trainer_fn=RecurrentMADQNTrainer, + trainer_fn=MADQNRecurrentTrainer, executor_fn=MADQNRecurrentExecutor, exploration_scheduler_fn=LinearExplorationScheduler, epsilon_min=0.05, diff --git a/mava/systems/tf/dial/builder.py b/mava/systems/tf/dial/builder.py index 6ecd93f77..c7e5d0e21 100644 --- a/mava/systems/tf/dial/builder.py +++ b/mava/systems/tf/dial/builder.py @@ -89,8 +89,8 @@ def __init__( self, config: DIALConfig, trainer_fn: Type[ - training.RecurrentCommMADQNTrainer - ] = training.RecurrentCommMADQNTrainer, + training.MADQNRecurrentCommTrainer + ] = training.MADQNRecurrentCommTrainer, executor_fn: Type[core.Executor] = execution.MADQNRecurrentCommExecutor, extra_specs: Dict[str, Any] = {}, exploration_scheduler_fn: Type[ @@ -222,7 +222,7 @@ def make_executor( communication_module: BaseCommunicationModule, adder: Optional[adders.ParallelAdder] = None, variable_source: Optional[core.VariableSource] = None, - trainer: Optional[training.RecurrentCommMADQNTrainer] = None, + trainer: Optional[training.MADQNRecurrentCommTrainer] = None, evaluator: bool = False, ) -> core.Executor: """Create an executor instance. diff --git a/mava/systems/tf/dial/system.py b/mava/systems/tf/dial/system.py index 00b62284d..8edcdb697 100644 --- a/mava/systems/tf/dial/system.py +++ b/mava/systems/tf/dial/system.py @@ -87,8 +87,8 @@ def __init__( logger_factory: Callable[[str], MavaLogger] = None, architecture: Type[DecentralisedValueActor] = DecentralisedValueActor, trainer_fn: Type[ - training.RecurrentCommMADQNTrainer - ] = training.RecurrentCommMADQNTrainer, + training.MADQNRecurrentCommTrainer + ] = training.MADQNRecurrentCommTrainer, communication_module: Type[BaseCommunicationModule] = BroadcastedCommunication, executor_fn: Type[core.Executor] = execution.MADQNFeedForwardExecutor, exploration_scheduler_fn: Type[ @@ -294,7 +294,7 @@ def executor( replay: reverb.Client, variable_source: acme.VariableSource, counter: counting.Counter, - trainer: Optional[training.RecurrentCommMADQNTrainer] = None, + trainer: Optional[training.MADQNRecurrentCommTrainer] = None, ) -> mava.ParallelEnvironmentLoop: """The executor process.""" @@ -369,7 +369,7 @@ def evaluator( self, variable_source: acme.VariableSource, counter: counting.Counter, - trainer: training.RecurrentCommMADQNTrainer, + trainer: training.MADQNRecurrentCommTrainer, ) -> Any: """The evaluation process.""" diff --git a/mava/systems/tf/dial/training.py b/mava/systems/tf/dial/training.py index 10732930e..5146d57b5 100644 --- a/mava/systems/tf/dial/training.py +++ b/mava/systems/tf/dial/training.py @@ -30,10 +30,10 @@ from mava.components.tf.modules.exploration.exploration_scheduling import ( LinearExplorationScheduler, ) -from mava.systems.tf.madqn.training import RecurrentCommMADQNTrainer +from mava.systems.tf.madqn.training import MADQNRecurrentCommTrainer -class DIALSwitchTrainer(RecurrentCommMADQNTrainer): +class DIALSwitchTrainer(MADQNRecurrentCommTrainer): """Recurrent Comm DIAL Switch trainer. This is the trainer component of a DIAL system. IE it takes a dataset as input and implements update functionality to learn from this dataset. diff --git a/mava/systems/tf/madqn/__init__.py b/mava/systems/tf/madqn/__init__.py index 73a036e12..274426e19 100644 --- a/mava/systems/tf/madqn/__init__.py +++ b/mava/systems/tf/madqn/__init__.py @@ -16,4 +16,4 @@ """Implementations of a IDQN agent.""" from mava.systems.tf.madqn.system import MADQN -from mava.systems.tf.madqn.training import MADQNTrainer, RecurrentMADQNTrainer +from mava.systems.tf.madqn.training import MADQNRecurrentTrainer, MADQNTrainer diff --git a/mava/systems/tf/madqn/system.py b/mava/systems/tf/madqn/system.py index 18637215a..e37769324 100644 --- a/mava/systems/tf/madqn/system.py +++ b/mava/systems/tf/madqn/system.py @@ -83,7 +83,7 @@ def __init__( logger_factory: Callable[[str], MavaLogger] = None, architecture: Type[DecentralisedValueActor] = DecentralisedValueActor, trainer_fn: Union[ - Type[training.MADQNTrainer], Type[training.RecurrentMADQNTrainer] + Type[training.MADQNTrainer], Type[training.MADQNRecurrentTrainer] ] = training.MADQNTrainer, communication_module: Type[BaseCommunicationModule] = None, executor_fn: Type[core.Executor] = execution.MADQNFeedForwardExecutor, diff --git a/mava/systems/tf/madqn/training.py b/mava/systems/tf/madqn/training.py index d16fad401..448b1eefb 100644 --- a/mava/systems/tf/madqn/training.py +++ b/mava/systems/tf/madqn/training.py @@ -322,7 +322,7 @@ def get_variables(self, names: Sequence[str]) -> Dict[str, Dict[str, np.ndarray] return variables -class RecurrentMADQNTrainer(MADQNTrainer): +class MADQNRecurrentTrainer(MADQNTrainer): """Recurrent MADQN trainer. This is the trainer component of a MADQN system. IE it takes a dataset as input and implements update functionality to learn from this dataset. @@ -417,7 +417,7 @@ def _forward(self, inputs: Any) -> None: self.tape = tape -class RecurrentCommMADQNTrainer(MADQNTrainer): +class MADQNRecurrentCommTrainer(MADQNTrainer): """Recurrent MADQN trainer. This is the trainer component of a MADQN system. IE it takes a dataset as input and implements update functionality to learn from this dataset. From 7965de275a7b7fa5873dac40fb9a9cf72d33f950 Mon Sep 17 00:00:00 2001 From: St John Grimbly Date: Tue, 25 May 2021 16:34:55 +0200 Subject: [PATCH 080/188] fix: VDN and update files. --- mava/systems/tf/qmix/training.py | 115 +++++++++++++++---------------- 1 file changed, 57 insertions(+), 58 deletions(-) diff --git a/mava/systems/tf/qmix/training.py b/mava/systems/tf/qmix/training.py index 212aad6df..5cd719a7d 100644 --- a/mava/systems/tf/qmix/training.py +++ b/mava/systems/tf/qmix/training.py @@ -15,7 +15,7 @@ """Qmix trainer implementation.""" -from typing import Any, Dict, List, Sequence +from typing import Any, Dict, List, Sequence, Optional import numpy as np import sonnet as snt @@ -24,6 +24,7 @@ from acme.utils import counting, loggers from trfl.indexing_ops import batched_index +from mava.components.tf.modules.communication import BaseCommunicationModule from mava.components.tf.modules.exploration.exploration_scheduling import ( LinearExplorationScheduler, ) @@ -53,8 +54,10 @@ def __init__( discount: float, shared_weights: bool, exploration_scheduler: LinearExplorationScheduler, + communication_module: Optional[BaseCommunicationModule] = None, clipping: bool = True, counter: counting.Counter = None, + fingerprint: bool = False, logger: loggers.Logger = None, checkpoint: bool = True, checkpoint_subpath: str = "~/mava/", @@ -74,8 +77,10 @@ def __init__( discount=discount, shared_weights=shared_weights, exploration_scheduler=exploration_scheduler, + communication_module=communication_module, clipping=clipping, counter=counter, + fingerprint=fingerprint, logger=logger, checkpoint=checkpoint, checkpoint_subpath=checkpoint_subpath, @@ -104,34 +109,40 @@ def __init__( # ) def _update_target_networks(self) -> None: - online_variables = [] - target_variables = [] + # Update target networks (incl. mixing networks). for key in self.unique_net_keys: - # Update target networks (incl. mixing networks). - online_variables += self._q_networks[key].variables - target_variables += self._target_q_networks[key].variables + online_variables = [*self._q_networks[key].variables,] + target_variables = [*self._target_q_networks[key].variables,] - online_variables += self._mixing_network.variables - target_variables += self._target_mixing_network.variables + # NOTE These shouldn't really be in the agent for loop. + online_variables += self._mixing_network.variables + target_variables += self._target_mixing_network.variables - # Make online -> target network update ops. - if tf.math.mod(self._num_steps, self._target_update_period) == 0: - for src, dest in zip(online_variables, target_variables): - dest.assign(src) + # Make online -> target network update ops. + if tf.math.mod(self._num_steps, self._target_update_period) == 0: + for src, dest in zip(online_variables, target_variables): + dest.assign(src) self._num_steps.assign_add(1) @tf.function - def _step(self) -> Dict[str, Dict[str, Any]]: + def _step( + self, + ) -> Dict[str, Dict[str, Any]]: + # Update the target networks self._update_target_networks() + # Get data from replay (dropping extras if any). Note there is no + # extra data here because we do not insert any into Reverb. inputs = next(self._iterator) self._forward(inputs) + self._backward() - return {"system": {"q_value_loss": self.loss}} # Return total system loss + # Log losses per agent + return {agent: {"q_value_loss": self.loss} for agent in self._agents} def _forward(self, inputs: Any) -> None: # Unpack input data as follows: @@ -145,68 +156,56 @@ def _forward(self, inputs: Any) -> None: # o_t = dictionary of next observations or next observation sequences # e_t = [Optional] = extra data that the agents persist in replay. o_tm1, a_tm1, e_tm1, r_t, d_t, o_t, e_t = inputs.data - - # Global state (for hypernetwork) - # TODO Switch between one-hot for discrete state space and no one-hot - # for continuous state spaces. `depth` param should be taken from - # state specs - or somewhere else in a cleaner way? - - # One-hot if discrete states - # s_tm1 = tf.one_hot(e_tm1["s_t"], depth=3) - # s_t = tf.one_hot(e_t["s_t"], depth=3) - - # Don't one-hot for continuous states s_tm1 = e_tm1["s_t"] s_t = e_t["s_t"] # Do forward passes through the networks and calculate the losses with tf.GradientTape(persistent=True) as tape: - q_tm1 = [] # Q vals - q_t = [] # Target Q vals + q_acts = [] # Q vals + q_targets = [] # Target Q vals for agent in self._agents: agent_key = self.agent_net_keys[agent] o_tm1_feed, o_t_feed, a_tm1_feed = self._get_feed( o_tm1, o_t, a_tm1, agent ) + q_tm1 = self._q_networks[agent_key](o_tm1_feed) + q_t_value = self._target_q_networks[agent_key](o_t_feed) + q_t_selector = self._q_networks[agent_key](o_t_feed) + best_action = tf.argmax(q_t_selector, axis=1, output_type=tf.int32) + + # TODO Make use of q_t_selector for fingerprinting. Speak to Claude. + q_act = batched_index(q_tm1, a_tm1_feed, keepdims=True) # [B, 1] + q_target = batched_index( + q_t_value, best_action, keepdims=True + ) # [B, 1] + + q_acts.append(q_act) + q_targets.append(q_target) + + rewards = tf.concat( + [tf.reshape(val, (-1, 1)) for val in list(r_t.values())], axis=1 + ) + rewards = tf.reduce_mean(rewards, axis=1) # [B] - q_tm1_agent = self._q_networks[agent_key](o_tm1_feed) # [B, n_actions] - q_act = batched_index(q_tm1_agent, a_tm1_feed, keepdims=True) # [B, 1] - - q_t_agent = self._target_q_networks[agent_key]( - o_t_feed - ) # [B, n_actions] - q_target_max = tf.reduce_max(q_t_agent, axis=1, keepdims=True) # [B, 1] - - q_tm1.append(q_act) - q_t.append(q_target_max) - - num_agents = len(self._agents) - - rewards = [tf.reshape(val, (-1, 1)) for val in list(r_t.values())] - rewards = tf.reshape( - tf.concat(rewards, axis=1), (-1, 1, num_agents) - ) # [B, 1, num_agents] - - dones = [tf.reshape(val.terminal, (-1, 1)) for val in list(o_tm1.values())] - dones = tf.reshape( - tf.concat(dones, axis=1), (-1, 1, num_agents) - ) # [B, 1, num_agents] + pcont = tf.concat( + [tf.reshape(val, (-1, 1)) for val in list(d_t.values())], axis=1 + ) + pcont = tf.reduce_mean(pcont, axis=1) + discount = tf.cast(self._discount, list(d_t.values())[0].dtype) + pcont = discount * pcont # [B] - q_tm1 = tf.concat(q_tm1, axis=1) # [B, num_agents] - q_t = tf.concat(q_t, axis=1) # [B, num_agents] + q_acts = tf.concat(q_acts, axis=1) # [B, num_agents] + q_targets = tf.concat(q_targets, axis=1) # [B, num_agents] - q_tot_mixed = self._mixing_network(q_tm1, s_tm1) # [B, 1, 1] - q_tot_target_mixed = self._target_mixing_network(q_t, s_t) # [B, 1, 1] + q_tot_mixed = self._mixing_network(q_acts, s_tm1) # [B, 1, 1] + q_tot_target_mixed = self._target_mixing_network(q_targets, s_t) # [B, 1, 1] # Calculate Q loss. - targets = ( - rewards - + self._discount * (tf.constant(1.0) - dones) * q_tot_target_mixed - ) + targets = rewards + pcont * q_tot_target_mixed targets = tf.stop_gradient(targets) td_error = targets - q_tot_mixed - + # Loss is MSE scaled by 0.5, so the gradient is equal to the TD error. self.loss = 0.5 * tf.reduce_mean(tf.square(td_error)) self.tape = tape From cbcae80aaeec18a2e433284a250895f6f2b73665 Mon Sep 17 00:00:00 2001 From: St John Grimbly Date: Tue, 25 May 2021 16:35:18 +0200 Subject: [PATCH 081/188] fix: VDN and update files. --- Makefile | 4 + .../debugging_envs/run_feedforward_qmix.py | 10 +-- .../debugging_envs/run_feedforward_qmix_v2.py | 5 +- .../debugging_envs/run_feedforward_vdn.py | 9 +- mava/components/tf/networks/additive.py | 2 +- mava/components/tf/networks/hypernetwork.py | 4 +- mava/components/tf/networks/mixers.py | 6 +- mava/systems/tf/qmix/builder.py | 10 +++ mava/systems/tf/qmix/execution.py | 5 ++ mava/systems/tf/qmix/system.py | 5 +- mava/systems/tf/qmixV2/system.py | 2 +- mava/systems/tf/qmixV2/training.py | 10 +-- mava/systems/tf/vdn/builder.py | 9 ++ mava/systems/tf/vdn/execution.py | 5 ++ mava/systems/tf/vdn/system.py | 2 +- mava/systems/tf/vdn/training.py | 85 +++++++++++-------- mava/utils/environments/smac_utils.py | 2 +- mava/wrappers/smac.py | 7 +- 18 files changed, 115 insertions(+), 67 deletions(-) diff --git a/Makefile b/Makefile index b56c8a67a..af24c30c6 100755 --- a/Makefile +++ b/Makefile @@ -20,6 +20,7 @@ MADDPG=examples/debugging_envs/run_decentralised_feedforward_maddpg_continous.py MAPPO=examples/debugging_envs/run_decentralised_feedforward_mappo.py MADQN=examples/debugging_envs/run_feedforward_madqn.py QMIX=examples/debugging_envs/run_feedforward_qmix.py +QMIXv2=examples/debugging_envs/run_feedforward_qmix_V2.py VDN=examples/debugging_envs/run_feedforward_vdn.py MADDPG-PZ=examples/petting_zoo/run_decentralised_feedforward_maddpg_continous.py @@ -44,6 +45,9 @@ run-mappo: run-qmix: $(DOCKER_RUN) python $(QMIX) --base_dir /home/app/mava/logs/ +run-qmix-v2: + $(DOCKER_RUN) python $(QMIXv2) --base_dir /home/app/mava/logs/ + run-vdn: $(DOCKER_RUN) python $(VDN) --base_dir /home/app/mava/logs/ diff --git a/examples/debugging_envs/run_feedforward_qmix.py b/examples/debugging_envs/run_feedforward_qmix.py index ccfbd3beb..64d3fa1d4 100644 --- a/examples/debugging_envs/run_feedforward_qmix.py +++ b/examples/debugging_envs/run_feedforward_qmix.py @@ -63,7 +63,7 @@ def make_networks( environment_spec: mava_specs.MAEnvironmentSpec, q_networks_layer_sizes: Union[Dict[str, Sequence], Sequence] = (512, 512, 256), - shared_weights: bool = False, + shared_weights: bool = True, ) -> Mapping[str, types.TensorTransformation]: """Creates networks used by the agents.""" @@ -120,7 +120,7 @@ def main(_: Any) -> None: debugging_utils.make_environment, env_name=FLAGS.env_name, action_space=FLAGS.action_space, - num_agents=2, + num_agents=3, return_state_info=True, ) @@ -148,9 +148,9 @@ def main(_: Any) -> None: logger_factory=logger_factory, num_executors=2, exploration_scheduler_fn=LinearExplorationScheduler, - epsilon_min=0.01, - epsilon_decay=1e-4, - optimizer=snt.optimizers.Adam(learning_rate=1e-4), + epsilon_min=0.05, + epsilon_decay=3e-4, + optimizer=snt.optimizers.RMSProp(learning_rate=1e-4), checkpoint_subpath=checkpoint_dir, ).build() diff --git a/examples/debugging_envs/run_feedforward_qmix_v2.py b/examples/debugging_envs/run_feedforward_qmix_v2.py index 1a59cee6c..2dcc6dc88 100644 --- a/examples/debugging_envs/run_feedforward_qmix_v2.py +++ b/examples/debugging_envs/run_feedforward_qmix_v2.py @@ -57,8 +57,9 @@ def make_networks( environment_spec: mava_specs.MAEnvironmentSpec, q_networks_layer_sizes: Union[Dict[str, Sequence], Sequence] = ( - 64, - 64, + 512, + 512, + 256, ), shared_weights: bool = True, ) -> Mapping[str, types.TensorTransformation]: diff --git a/examples/debugging_envs/run_feedforward_vdn.py b/examples/debugging_envs/run_feedforward_vdn.py index b0ca1df91..4d2652bdf 100644 --- a/examples/debugging_envs/run_feedforward_vdn.py +++ b/examples/debugging_envs/run_feedforward_vdn.py @@ -62,7 +62,7 @@ def make_networks( environment_spec: mava_specs.MAEnvironmentSpec, - q_networks_layer_sizes: Union[Dict[str, Sequence], Sequence] = (64,), + q_networks_layer_sizes: Union[Dict[str, Sequence], Sequence] = (512, 512, 256), shared_weights: bool = False, ) -> Mapping[str, types.TensorTransformation]: """Creates networks used by the agents.""" @@ -148,9 +148,10 @@ def main(_: Any) -> None: logger_factory=logger_factory, num_executors=2, exploration_scheduler_fn=LinearExplorationScheduler, - epsilon_min=0.01, - epsilon_decay=1e-4, - optimizer=snt.optimizers.Adam(learning_rate=1e-4), + epsilon_min=0.05, + epsilon_decay=3e-4, + max_replay_size=1000000, + optimizer=snt.optimizers.RMSProp(learning_rate=1e-4), checkpoint_subpath=checkpoint_dir, ).build() diff --git a/mava/components/tf/networks/additive.py b/mava/components/tf/networks/additive.py index ceab813cc..db827f0a5 100644 --- a/mava/components/tf/networks/additive.py +++ b/mava/components/tf/networks/additive.py @@ -26,4 +26,4 @@ def __init__(self) -> None: def __call__(self, q_values: tf.Tensor) -> tf.Tensor: """Monotonic mixing logic.""" - return tf.math.reduce_sum(q_values, keepdims=True) + return tf.math.reduce_sum(q_values, axis=1) diff --git a/mava/components/tf/networks/hypernetwork.py b/mava/components/tf/networks/hypernetwork.py index 8c23f6a44..f0462efcb 100644 --- a/mava/components/tf/networks/hypernetwork.py +++ b/mava/components/tf/networks/hypernetwork.py @@ -53,7 +53,9 @@ def __init__( # Set up hypernetwork configuration if self._num_hypernet_layers == 1: - self.hyper_w1 = snt.nets.MLP(output_sizes=[self._qmix_hidden_dim]) + self.hyper_w1 = snt.nets.MLP( + output_sizes=[self._qmix_hidden_dim * self._n_agents] + ) self.hyper_w2 = snt.nets.MLP(output_sizes=[self._qmix_hidden_dim]) # Default diff --git a/mava/components/tf/networks/mixers.py b/mava/components/tf/networks/mixers.py index f953a3931..cc1884abf 100644 --- a/mava/components/tf/networks/mixers.py +++ b/mava/components/tf/networks/mixers.py @@ -3,7 +3,6 @@ import numpy as np import sonnet as snt import tensorflow as tf -from tensorflow import keras class AdditiveMixingNetwork(snt.Module): @@ -25,7 +24,7 @@ def __init__(self, n_agents: Any, state_shape: Any, mixing_embed_dim: Any) -> No self.embed_dim = mixing_embed_dim self.state_dim = int(np.prod(state_shape)) - self.hyper_w_1 = snt.Linear(self.embed_dim*self.n_agents) + self.hyper_w_1 = snt.Linear(self.embed_dim * self.n_agents) self.hyper_w_final = snt.Linear(self.embed_dim) # State dependent bias for hidden layer @@ -65,7 +64,6 @@ def __call__(self, agent_qs: Any, states: Any) -> tf.Tensor: # print("w_final: ", w_final.shape) # print("v: ", v.shape) y = tf.linalg.matmul(hidden, w_final) + v - # print("tf.linalg.matmul(hidden, w_final): ", tf.linalg.matmul(hidden, w_final).shape) # print("y: ", y.shape) # Reshape and return q_tot = tf.reshape(y, (bs, -1, 1)) @@ -107,4 +105,4 @@ def __call__(self, agent_qs: Any, states: Any) -> tf.Tensor: # print("y: ", y.shape) # q_tot = tf.reshape(y, (bs, -1, 1)) # print("q_tot: ", q_tot.shape) - # return q_tot \ No newline at end of file + # return q_tot diff --git a/mava/systems/tf/qmix/builder.py b/mava/systems/tf/qmix/builder.py index 4d4806313..4fdafbd19 100644 --- a/mava/systems/tf/qmix/builder.py +++ b/mava/systems/tf/qmix/builder.py @@ -25,6 +25,7 @@ from mava.components.tf.modules.exploration.exploration_scheduling import ( LinearExplorationScheduler, ) +from mava.components.tf.modules.stabilising import FingerPrintStabalisation from mava.systems.tf.madqn.builder import MADQNBuilder, MADQNConfig from mava.systems.tf.qmix import execution, training from mava.wrappers import DetailedTrainerStatisticsWithEpsilon @@ -34,6 +35,7 @@ @dataclasses.dataclass class QMIXConfig(MADQNConfig): + # TODO Match documentation with MADQNConfig once cleaned. """Configuration options for the QMIX system. Args: environment_spec: description of the actions, observations, etc. @@ -71,6 +73,7 @@ def __init__( exploration_scheduler_fn: Type[ LinearExplorationScheduler ] = LinearExplorationScheduler, + replay_stabilisation_fn: Optional[Type[FingerPrintStabalisation]] = None, ) -> None: """Args: _config: Configuration options for the QMIX system. @@ -82,6 +85,7 @@ def __init__( executor_fn=executor_fn, extra_specs=extra_specs, exploration_scheduler_fn=exploration_scheduler_fn, + replay_stabilisation_fn=replay_stabilisation_fn, ) def make_trainer( @@ -114,6 +118,10 @@ def make_trainer( epsilon_min=self._config.epsilon_min, epsilon_decay=self._config.epsilon_decay, ) + + # Check if we should use fingerprints + fingerprint = True if self._replay_stabiliser_fn is not None else False + # The learner updates the parameters (and initializes them). trainer = self._trainer_fn( # type:ignore agents=agents, @@ -128,8 +136,10 @@ def make_trainer( target_update_period=self._config.target_update_period, clipping=self._config.clipping, exploration_scheduler=exploration_scheduler, + communication_module=communication_module, dataset=dataset, counter=counter, + fingerprint=fingerprint, logger=logger, checkpoint=self._config.checkpoint, checkpoint_subpath=self._config.checkpoint_subpath, diff --git a/mava/systems/tf/qmix/execution.py b/mava/systems/tf/qmix/execution.py index 1f11d34c5..bd320ea90 100644 --- a/mava/systems/tf/qmix/execution.py +++ b/mava/systems/tf/qmix/execution.py @@ -21,6 +21,7 @@ from acme.tf import variable_utils as tf2_variable_utils from mava import adders +from mava.components.tf.modules.communication import BaseCommunicationModule from mava.systems.tf.madqn.execution import MADQNFeedForwardExecutor from mava.systems.tf.madqn.training import MADQNTrainer @@ -41,6 +42,8 @@ def __init__( shared_weights: bool = True, adder: Optional[adders.ParallelAdder] = None, variable_client: Optional[tf2_variable_utils.VariableClient] = None, + communication_module: Optional[BaseCommunicationModule] = None, + fingerprint: bool = False, evaluator: bool = False, ): """Initializes the executor. @@ -58,6 +61,8 @@ def __init__( shared_weights=shared_weights, adder=adder, variable_client=variable_client, + communication_module=communication_module, + fingerprint=fingerprint, trainer=trainer, evaluator=evaluator, ) diff --git a/mava/systems/tf/qmix/system.py b/mava/systems/tf/qmix/system.py index c1197da49..76cd44e83 100644 --- a/mava/systems/tf/qmix/system.py +++ b/mava/systems/tf/qmix/system.py @@ -80,7 +80,7 @@ def __init__( architecture: Type[DecentralisedValueActor] = DecentralisedValueActor, trainer_fn: Type[training.QMIXTrainer] = training.QMIXTrainer, executor_fn: Type[core.Executor] = execution.QMIXFeedForwardExecutor, - mixer: Type[mixing.BaseMixingModule] = mixing.MonotonicMixing, + mixer: Type[mixing.MonotonicMixing] = mixing.MonotonicMixing, exploration_scheduler_fn: Type[ LinearExplorationScheduler ] = LinearExplorationScheduler, @@ -93,7 +93,7 @@ def __init__( batch_size: int = 256, prefetch_size: int = 4, min_replay_size: int = 1000, - max_replay_size: int = 1000, + max_replay_size: int = 1000000, samples_per_insert: Optional[float] = 32.0, n_step: int = 5, sequence_length: int = 20, @@ -205,6 +205,7 @@ def trainer( architecture=architecture, environment_spec=self._environment_spec, agent_networks=agent_networks, + num_hypernet_layers=1, ).create_system() # create logger diff --git a/mava/systems/tf/qmixV2/system.py b/mava/systems/tf/qmixV2/system.py index 319308146..85de21e64 100644 --- a/mava/systems/tf/qmixV2/system.py +++ b/mava/systems/tf/qmixV2/system.py @@ -15,7 +15,7 @@ """Defines the MADQN system class.""" import functools -from typing import Any, Callable, Dict, Optional, Type, Union +from typing import Any, Callable, Dict, Optional, Type import acme import dm_env diff --git a/mava/systems/tf/qmixV2/training.py b/mava/systems/tf/qmixV2/training.py index 20fc18d7e..c54e7017b 100644 --- a/mava/systems/tf/qmixV2/training.py +++ b/mava/systems/tf/qmixV2/training.py @@ -20,7 +20,6 @@ import numpy as np import sonnet as snt import tensorflow as tf -import tree import trfl from acme.tf import utils as tf2_utils from acme.types import NestedArray @@ -297,15 +296,14 @@ def _forward(self, inputs: Any) -> None: q_acts = tf.concat(q_acts, axis=1) # [B, num_agents] q_targets = tf.concat(q_targets, axis=1) # [B, num_agents] - q_tot_mixed = self._mixer(q_acts, s_tm1) # [B, 1, 1] q_tot_target_mixed = self._target_mixer(q_targets, s_t) # [B, 1, 1] - # q_tot_mixed = tf.reduce_sum(q_acts, axis=1) # [B, 1, 1] - # q_tot_target_mixed = tf.reduce_sum(q_targets, axis=1) # [B, 1, 1] + q_tot_mixed = tf.reduce_sum(q_acts, axis=1) # [B, 1, 1] + q_tot_target_mixed = tf.reduce_sum(q_targets, axis=1) # [B, 1, 1] - q_tot_mixed = tf.reshape(q_tot_mixed, (-1,)) - q_tot_target_mixed = tf.reshape(q_tot_target_mixed, (-1,)) + # q_tot_mixed = tf.reshape(q_tot_mixed, (-1,)) + # q_tot_target_mixed = tf.reshape(q_tot_target_mixed, (-1,)) # Calculate Q loss. targets = rewards + pcont * q_tot_target_mixed diff --git a/mava/systems/tf/vdn/builder.py b/mava/systems/tf/vdn/builder.py index 972aa9272..61ecce3cb 100644 --- a/mava/systems/tf/vdn/builder.py +++ b/mava/systems/tf/vdn/builder.py @@ -25,6 +25,7 @@ from mava.components.tf.modules.exploration.exploration_scheduling import ( LinearExplorationScheduler, ) +from mava.components.tf.modules.stabilising import FingerPrintStabalisation from mava.systems.tf.madqn.builder import MADQNBuilder, MADQNConfig from mava.systems.tf.vdn import execution, training from mava.wrappers import DetailedTrainerStatisticsWithEpsilon @@ -72,6 +73,7 @@ def __init__( exploration_scheduler_fn: Type[ LinearExplorationScheduler ] = LinearExplorationScheduler, + replay_stabilisation_fn: Optional[Type[FingerPrintStabalisation]] = None, ) -> None: """Args: _config: Configuration options for the QMIX system. @@ -83,6 +85,7 @@ def __init__( executor_fn=executor_fn, extra_specs=extra_specs, exploration_scheduler_fn=exploration_scheduler_fn, + replay_stabilisation_fn=replay_stabilisation_fn, ) def make_trainer( @@ -115,6 +118,10 @@ def make_trainer( epsilon_min=self._config.epsilon_min, epsilon_decay=self._config.epsilon_decay, ) + + # Check if we should use fingerprints + fingerprint = True if self._replay_stabiliser_fn is not None else False + # The learner updates the parameters (and initializes them). trainer = self._trainer_fn( # type:ignore agents=agents, @@ -129,8 +136,10 @@ def make_trainer( target_update_period=self._config.target_update_period, clipping=self._config.clipping, exploration_scheduler=exploration_scheduler, + communication_module=communication_module, dataset=dataset, counter=counter, + fingerprint=fingerprint, logger=logger, checkpoint=self._config.checkpoint, checkpoint_subpath=self._config.checkpoint_subpath, diff --git a/mava/systems/tf/vdn/execution.py b/mava/systems/tf/vdn/execution.py index bcd9d7da9..22dd62fa3 100644 --- a/mava/systems/tf/vdn/execution.py +++ b/mava/systems/tf/vdn/execution.py @@ -21,6 +21,7 @@ from acme.tf import variable_utils as tf2_variable_utils from mava import adders +from mava.components.tf.modules.communication import BaseCommunicationModule from mava.systems.tf.madqn.execution import MADQNFeedForwardExecutor from mava.systems.tf.madqn.training import MADQNTrainer @@ -41,6 +42,8 @@ def __init__( shared_weights: bool = True, adder: Optional[adders.ParallelAdder] = None, variable_client: Optional[tf2_variable_utils.VariableClient] = None, + communication_module: Optional[BaseCommunicationModule] = None, + fingerprint: bool = False, evaluator: bool = False, ): """Initializes the executor. @@ -58,6 +61,8 @@ def __init__( shared_weights=shared_weights, adder=adder, variable_client=variable_client, + communication_module=communication_module, + fingerprint=fingerprint, trainer=trainer, evaluator=evaluator, ) diff --git a/mava/systems/tf/vdn/system.py b/mava/systems/tf/vdn/system.py index fa8603445..0d5e0eb29 100644 --- a/mava/systems/tf/vdn/system.py +++ b/mava/systems/tf/vdn/system.py @@ -93,7 +93,7 @@ def __init__( batch_size: int = 256, prefetch_size: int = 4, min_replay_size: int = 1000, - max_replay_size: int = 1000, + max_replay_size: int = 1000000, samples_per_insert: Optional[float] = 32.0, n_step: int = 5, sequence_length: int = 20, diff --git a/mava/systems/tf/vdn/training.py b/mava/systems/tf/vdn/training.py index 951c14099..b7b1380d6 100644 --- a/mava/systems/tf/vdn/training.py +++ b/mava/systems/tf/vdn/training.py @@ -15,13 +15,14 @@ """VDN trainer implementation.""" -from typing import Any, Dict, List +from typing import Any, Dict, List, Optional import sonnet as snt import tensorflow as tf from acme.utils import counting, loggers from trfl.indexing_ops import batched_index +from mava.components.tf.modules.communication import BaseCommunicationModule from mava.components.tf.modules.exploration.exploration_scheduling import ( LinearExplorationScheduler, ) @@ -49,8 +50,10 @@ def __init__( discount: float, shared_weights: bool, exploration_scheduler: LinearExplorationScheduler, + communication_module: Optional[BaseCommunicationModule] = None, clipping: bool = True, counter: counting.Counter = None, + fingerprint: bool = False, logger: loggers.Logger = None, checkpoint: bool = True, checkpoint_subpath: str = "~/mava/", @@ -70,24 +73,33 @@ def __init__( discount=discount, shared_weights=shared_weights, exploration_scheduler=exploration_scheduler, + communication_module=communication_module, clipping=clipping, counter=counter, + fingerprint=fingerprint, logger=logger, checkpoint=checkpoint, checkpoint_subpath=checkpoint_subpath, ) @tf.function - def _step(self) -> Dict[str, Dict[str, Any]]: + def _step( + self, + ) -> Dict[str, Dict[str, Any]]: + # Update the target networks self._update_target_networks() + # Get data from replay (dropping extras if any). Note there is no + # extra data here because we do not insert any into Reverb. inputs = next(self._iterator) self._forward(inputs) + self._backward() - return {"system": {"q_value_loss": self.loss}} # Return total system loss + # Log losses per agent + return {agent: {"q_value_loss": self.loss} for agent in self._agents} def _forward(self, inputs: Any) -> None: # Unpack input data as follows: @@ -104,49 +116,51 @@ def _forward(self, inputs: Any) -> None: # Do forward passes through the networks and calculate the losses with tf.GradientTape(persistent=True) as tape: - q_tm1 = [] # Q vals - q_t = [] # Target Q vals + q_acts = [] # Q vals + q_targets = [] # Target Q vals for agent in self._agents: agent_key = self.agent_net_keys[agent] o_tm1_feed, o_t_feed, a_tm1_feed = self._get_feed( o_tm1, o_t, a_tm1, agent ) + q_tm1 = self._q_networks[agent_key](o_tm1_feed) + q_t_value = self._target_q_networks[agent_key](o_t_feed) + q_t_selector = self._q_networks[agent_key](o_t_feed) + best_action = tf.argmax(q_t_selector, axis=1, output_type=tf.int32) + + # TODO Make use of q_t_selector for fingerprinting. Speak to Claude. + q_act = batched_index(q_tm1, a_tm1_feed, keepdims=True) # [B, 1] + q_target = batched_index( + q_t_value, best_action, keepdims=True + ) # [B, 1] + + q_acts.append(q_act) + q_targets.append(q_target) + + rewards = tf.concat( + [tf.reshape(val, (-1, 1)) for val in list(r_t.values())], axis=1 + ) + rewards = tf.reduce_mean(rewards, axis=1) # [B] - q_tm1_agent = self._q_networks[agent_key](o_tm1_feed) # [B, n_actions] - q_act = batched_index(q_tm1_agent, a_tm1_feed, keepdims=True) # [B, 1] - - q_t_agent = self._target_q_networks[agent_key]( - o_t_feed - ) # [B, n_actions] - q_target_max = tf.reduce_max(q_t_agent, axis=1, keepdims=True) # [B, 1] - - q_tm1.append(q_act) - q_t.append(q_target_max) - - num_agents = len(self._agents) - - rewards = [tf.reshape(val, (-1, 1)) for val in list(r_t.values())] - rewards = tf.reshape( - tf.concat(rewards, axis=1), (-1, 1, num_agents) - ) # [B, 1, num_agents] + pcont = tf.concat( + [tf.reshape(val, (-1, 1)) for val in list(d_t.values())], axis=1 + ) + pcont = tf.reduce_mean(pcont, axis=1) + discount = tf.cast(self._discount, list(d_t.values())[0].dtype) + pcont = discount * pcont # [B] - dones = [tf.reshape(val.terminal, (-1, 1)) for val in list(o_tm1.values())] - dones = tf.reshape( - tf.concat(dones, axis=1), (-1, 1, num_agents) - ) # [B, 1, num_agents] + q_acts = tf.concat(q_acts, axis=1) # [B, num_agents] + q_targets = tf.concat(q_targets, axis=1) # [B, num_agents] - q_tm1 = tf.concat(q_tm1, axis=1) # [B, num_agents] - q_t = tf.concat(q_t, axis=1) # [B, num_agents] + q_tot_mixed = self._mixing_network(q_acts) # [B, 1, 1] + q_tot_target_mixed = self._target_mixing_network(q_targets) # [B, 1, 1] - q_tot_mixed = self._mixing_network(q_tm1) # [B, 1, 1] - q_tot_target_mixed = self._target_mixing_network(q_t) # [B, 1, 1] + # q_tot_mixed = tf.reduce_sum(q_acts, axis=1) # [B, 1, 1] + # q_tot_target_mixed = tf.reduce_sum(q_targets, axis=1) # [B, 1, 1] # Calculate Q loss. - targets = ( - rewards - + self._discount * (tf.constant(1.0) - dones) * q_tot_target_mixed - ) + targets = rewards + pcont * q_tot_target_mixed targets = tf.stop_gradient(targets) td_error = targets - q_tot_mixed @@ -156,10 +170,11 @@ def _forward(self, inputs: Any) -> None: def _backward(self) -> None: # Calculate the gradients and update the networks + trainable_variables = [] for agent in self._agents: agent_key = self.agent_net_keys[agent] # Get trainable variables. - trainable_variables = self._q_networks[agent_key].trainable_variables + trainable_variables += self._q_networks[agent_key].trainable_variables # Compute gradients. gradients = self.tape.gradient(self.loss, trainable_variables) diff --git a/mava/utils/environments/smac_utils.py b/mava/utils/environments/smac_utils.py index 4db2fa554..d2cacba16 100644 --- a/mava/utils/environments/smac_utils.py +++ b/mava/utils/environments/smac_utils.py @@ -20,7 +20,7 @@ import dm_env from smac.env import StarCraft2Env -from mava.wrappers import SMACEnvWrapper +from mava.wrappers import SMACEnvWrapper # type:ignore def load_smac_env(env_config: Dict[str, Any]) -> StarCraft2Env: diff --git a/mava/wrappers/smac.py b/mava/wrappers/smac.py index 0bf9b5f41..c6c5784be 100644 --- a/mava/wrappers/smac.py +++ b/mava/wrappers/smac.py @@ -27,14 +27,13 @@ from gym.spaces import Box, Discrete from pettingzoo.utils.env import ParallelEnv +# TODO should not be imported unless user wants the smac env. +from smac.env import StarCraft2Env # type:ignore + from mava import types from mava.utils.wrapper_utils import convert_np_type, parameterized_restart from mava.wrappers.env_wrappers import ParallelEnvWrapper -# TODO should not be imported unless user wants the smax env. -# from smac.env import StarCraft2Env # type:ignore - - class SMACEnvWrapper(ParallelEnvWrapper): """ From edeeefd83f4f8dd82f6ab27ea51801acf3aa1c25 Mon Sep 17 00:00:00 2001 From: St John Grimbly Date: Tue, 25 May 2021 17:36:34 +0200 Subject: [PATCH 082/188] fix: bug in VDN. --- mava/systems/tf/qmix/training.py | 61 +++++++++++++++++++------------- mava/systems/tf/vdn/training.py | 17 +++++---- 2 files changed, 44 insertions(+), 34 deletions(-) diff --git a/mava/systems/tf/qmix/training.py b/mava/systems/tf/qmix/training.py index 5cd719a7d..99ca0cfc7 100644 --- a/mava/systems/tf/qmix/training.py +++ b/mava/systems/tf/qmix/training.py @@ -15,7 +15,7 @@ """Qmix trainer implementation.""" -from typing import Any, Dict, List, Sequence, Optional +from typing import Any, Dict, List, Optional, Sequence import numpy as np import sonnet as snt @@ -65,6 +65,7 @@ def __init__( self._mixing_network = mixing_network self._target_mixing_network = target_mixing_network + self._optimizer = optimizer super(QMIXTrainer, self).__init__( agents=agents, @@ -110,18 +111,29 @@ def __init__( def _update_target_networks(self) -> None: # Update target networks (incl. mixing networks). - for key in self.unique_net_keys: - online_variables = [*self._q_networks[key].variables,] - target_variables = [*self._target_q_networks[key].variables,] + if tf.math.mod(self._num_steps, self._target_update_period) == 0: + for key in self.unique_net_keys: + online_variables = [ + *self._q_networks[key].variables, + ] + target_variables = [ + *self._target_q_networks[key].variables, + ] + + # Make online -> target network update ops. + for src, dest in zip(online_variables, target_variables): + dest.assign(src) # NOTE These shouldn't really be in the agent for loop. - online_variables += self._mixing_network.variables - target_variables += self._target_mixing_network.variables - + online_variables = [ + *self._mixing_network.variables, + ] + target_variables = [ + *self._target_mixing_network.variables, + ] # Make online -> target network update ops. - if tf.math.mod(self._num_steps, self._target_update_period) == 0: - for src, dest in zip(online_variables, target_variables): - dest.assign(src) + for src, dest in zip(online_variables, target_variables): + dest.assign(src) self._num_steps.assign_add(1) @@ -199,38 +211,37 @@ def _forward(self, inputs: Any) -> None: q_targets = tf.concat(q_targets, axis=1) # [B, num_agents] q_tot_mixed = self._mixing_network(q_acts, s_tm1) # [B, 1, 1] - q_tot_target_mixed = self._target_mixing_network(q_targets, s_t) # [B, 1, 1] + q_tot_target_mixed = self._target_mixing_network( + q_targets, s_t + ) # [B, 1, 1] # Calculate Q loss. targets = rewards + pcont * q_tot_target_mixed targets = tf.stop_gradient(targets) td_error = targets - q_tot_mixed - + # Loss is MSE scaled by 0.5, so the gradient is equal to the TD error. self.loss = 0.5 * tf.reduce_mean(tf.square(td_error)) self.tape = tape def _backward(self) -> None: - # Calculate the gradients and update the networks - trainable_variables = [] for agent in self._agents: agent_key = self.agent_net_keys[agent] - # Get trainable variables. - trainable_variables += self._q_networks[agent_key].trainable_variables - trainable_variables += self._mixing_network.trainable_variables + # Update agent networks + variables = self._q_networks[agent_key].trainable_variables + gradients = self.tape.gradient(self.loss, variables) + if self._clipping: + gradients = tf.clip_by_global_norm(gradients, 40.0)[0] + self._optimizers[agent_key].apply(gradients, variables) - # Compute gradients. - gradients = self.tape.gradient(self.loss, trainable_variables) - - # Maybe clip gradients. + # Update mixing network + variables = self._mixing_network.trainable_variables + gradients = self.tape.gradient(self.loss, variables) if self._clipping: gradients = tf.clip_by_global_norm(gradients, 40.0)[0] + self._optimizer.apply(gradients, variables) - # Apply gradients. - self._optimizers[agent_key].apply(gradients, trainable_variables) - - # Delete the tape manually because of the persistent=True flag. train_utils.safe_del(self, "tape") def get_variables(self, names: Sequence[str]) -> Dict[str, Dict[str, np.ndarray]]: diff --git a/mava/systems/tf/vdn/training.py b/mava/systems/tf/vdn/training.py index b7b1380d6..63e58ee40 100644 --- a/mava/systems/tf/vdn/training.py +++ b/mava/systems/tf/vdn/training.py @@ -170,21 +170,20 @@ def _forward(self, inputs: Any) -> None: def _backward(self) -> None: # Calculate the gradients and update the networks - trainable_variables = [] for agent in self._agents: agent_key = self.agent_net_keys[agent] # Get trainable variables. - trainable_variables += self._q_networks[agent_key].trainable_variables + trainable_variables = self._q_networks[agent_key].trainable_variables - # Compute gradients. - gradients = self.tape.gradient(self.loss, trainable_variables) + # Compute gradients. + gradients = self.tape.gradient(self.loss, trainable_variables) - # Maybe clip gradients. - if self._clipping: - gradients = tf.clip_by_global_norm(gradients, 40.0)[0] + # Maybe clip gradients. + if self._clipping: + gradients = tf.clip_by_global_norm(gradients, 40.0)[0] - # Apply gradients. - self._optimizers[agent_key].apply(gradients, trainable_variables) + # Apply gradients. + self._optimizers[agent_key].apply(gradients, trainable_variables) # Delete the tape manually because of the persistent=True flag. train_utils.safe_del(self, "tape") From 97ded76817bed969033a7a0fc6a832da0db38adc Mon Sep 17 00:00:00 2001 From: Kale-ab Date: Tue, 25 May 2021 19:35:15 +0200 Subject: [PATCH 083/188] feat: Made grad norm configurable for madqn and derivatives. --- .../run_feedforward_madqn_with_fingerprints.py | 2 +- mava/systems/tf/maddpg/training.py | 1 - mava/systems/tf/madqn/builder.py | 4 ++-- mava/systems/tf/madqn/system.py | 3 ++- mava/systems/tf/madqn/training.py | 18 +++++++++++------- mava/systems/tf/qmix/builder.py | 2 +- mava/systems/tf/qmix/system.py | 4 ++-- mava/systems/tf/qmix/training.py | 9 ++++----- mava/systems/tf/vdn/builder.py | 3 +-- mava/systems/tf/vdn/system.py | 4 ++-- mava/systems/tf/vdn/training.py | 9 ++++----- 11 files changed, 30 insertions(+), 29 deletions(-) diff --git a/examples/petting_zoo/run_feedforward_madqn_with_fingerprints.py b/examples/petting_zoo/run_feedforward_madqn_with_fingerprints.py index 903f52389..f69930e84 100644 --- a/examples/petting_zoo/run_feedforward_madqn_with_fingerprints.py +++ b/examples/petting_zoo/run_feedforward_madqn_with_fingerprints.py @@ -54,7 +54,7 @@ str(datetime.now()), "Experiment identifier that can be used to continue experiments.", ) -flags.DEFINE_string("base_dir", "./logs/", "Base dir to store experiments.") +flags.DEFINE_string("base_dir", "~/mava/", "Base dir to store experiments.") def make_networks( diff --git a/mava/systems/tf/maddpg/training.py b/mava/systems/tf/maddpg/training.py index 367ba6ae8..6bf2c04c8 100644 --- a/mava/systems/tf/maddpg/training.py +++ b/mava/systems/tf/maddpg/training.py @@ -578,7 +578,6 @@ def __init__( target_observation_network: the target observation network. policy_optimizer: the optimizer to be applied to the DPG (policy) loss. critic_optimizer: the optimizer to be applied to the critic loss. - clipping: whether to clip gradients by global norm. counter: counter object used to keep track of steps. logger: logger object to be used by learner. checkpoint: boolean indicating whether to checkpoint the learner. diff --git a/mava/systems/tf/madqn/builder.py b/mava/systems/tf/madqn/builder.py index 8eb246480..5b370279c 100644 --- a/mava/systems/tf/madqn/builder.py +++ b/mava/systems/tf/madqn/builder.py @@ -57,9 +57,9 @@ class MADQNConfig: epsilon_min: float epsilon_decay: float shared_weights: bool + max_gradient_norm: Optional[float] target_update_period: int executor_variable_update_period: int - clipping: bool min_replay_size: int max_replay_size: int samples_per_insert: Optional[float] @@ -301,7 +301,7 @@ def make_trainer( shared_weights=self._config.shared_weights, optimizer=self._config.optimizer, target_update_period=self._config.target_update_period, - clipping=self._config.clipping, + max_gradient_norm=self._config.max_gradient_norm, exploration_scheduler=exploration_scheduler, dataset=dataset, counter=counter, diff --git a/mava/systems/tf/madqn/system.py b/mava/systems/tf/madqn/system.py index 371df4b94..a85e399e1 100644 --- a/mava/systems/tf/madqn/system.py +++ b/mava/systems/tf/madqn/system.py @@ -104,6 +104,7 @@ def __init__( sequence_length: int = 20, period: int = 20, clipping: bool = True, + max_gradient_norm: float = None, discount: float = 0.99, optimizer: snt.Optimizer = snt.optimizers.Adam(learning_rate=1e-4), target_update_period: int = 100, @@ -171,7 +172,7 @@ def __init__( n_step=n_step, sequence_length=sequence_length, period=period, - clipping=clipping, + max_gradient_norm=max_gradient_norm, checkpoint=checkpoint, optimizer=optimizer, checkpoint_subpath=checkpoint_subpath, diff --git a/mava/systems/tf/madqn/training.py b/mava/systems/tf/madqn/training.py index 8456dae82..ad462c1d5 100644 --- a/mava/systems/tf/madqn/training.py +++ b/mava/systems/tf/madqn/training.py @@ -52,7 +52,7 @@ def __init__( discount: float, shared_weights: bool, exploration_scheduler: LinearExplorationScheduler, - clipping: bool = True, + max_gradient_norm: float = None, fingerprint: bool = False, counter: counting.Counter = None, logger: loggers.Logger = None, @@ -75,7 +75,12 @@ def __init__( # Other learner parameters. self._discount = discount - self._clipping = clipping + # Set up gradient clipping. + if max_gradient_norm is not None: + self._max_gradient_norm = tf.convert_to_tensor(max_gradient_norm) + else: # A very large number. Infinity results in NaNs. + self._max_gradient_norm = tf.convert_to_tensor(1e10) + self._fingerprint = fingerprint # Necessary to track when to update target networks. @@ -299,9 +304,8 @@ def _backward(self) -> None: # Compute gradients gradients = tape.gradient(q_network_losses[agent], q_network_variables) - # Maybe clip gradients. - if self._clipping: - gradients = tf.clip_by_global_norm(gradients, 40.0)[0] + # Clip gradients. + gradients = tf.clip_by_global_norm(gradients, self._max_gradient_norm)[0] # Apply gradients. self._optimizers[agent_key].apply(gradients, q_network_variables) @@ -338,7 +342,7 @@ def __init__( discount: float, shared_weights: bool, exploration_scheduler: LinearExplorationScheduler, - clipping: bool = True, + max_gradient_norm: float = None, counter: counting.Counter = None, logger: loggers.Logger = None, fingerprint: bool = False, @@ -356,7 +360,7 @@ def __init__( discount=discount, shared_weights=shared_weights, exploration_scheduler=exploration_scheduler, - clipping=clipping, + max_gradient_norm=max_gradient_norm, counter=counter, logger=logger, fingerprint=fingerprint, diff --git a/mava/systems/tf/qmix/builder.py b/mava/systems/tf/qmix/builder.py index ffbb27ec6..3d8e3fddd 100644 --- a/mava/systems/tf/qmix/builder.py +++ b/mava/systems/tf/qmix/builder.py @@ -124,7 +124,7 @@ def make_trainer( shared_weights=self._config.shared_weights, optimizer=self._config.optimizer, target_update_period=self._config.target_update_period, - clipping=self._config.clipping, + max_gradient_norm=self._config.max_gradient_norm, exploration_scheduler=exploration_scheduler, dataset=dataset, counter=counter, diff --git a/mava/systems/tf/qmix/system.py b/mava/systems/tf/qmix/system.py index c1197da49..c5952c9f0 100644 --- a/mava/systems/tf/qmix/system.py +++ b/mava/systems/tf/qmix/system.py @@ -98,7 +98,7 @@ def __init__( n_step: int = 5, sequence_length: int = 20, period: int = 20, - clipping: bool = True, + max_gradient_norm: float = None, discount: float = 0.99, optimizer: snt.Optimizer = snt.optimizers.Adam(learning_rate=1e-4), target_update_period: int = 100, @@ -169,7 +169,7 @@ def __init__( n_step=n_step, sequence_length=sequence_length, period=period, - clipping=clipping, + max_gradient_norm=max_gradient_norm, checkpoint=checkpoint, optimizer=optimizer, checkpoint_subpath=checkpoint_subpath, diff --git a/mava/systems/tf/qmix/training.py b/mava/systems/tf/qmix/training.py index 212aad6df..c06e0024b 100644 --- a/mava/systems/tf/qmix/training.py +++ b/mava/systems/tf/qmix/training.py @@ -53,7 +53,7 @@ def __init__( discount: float, shared_weights: bool, exploration_scheduler: LinearExplorationScheduler, - clipping: bool = True, + max_gradient_norm: float = None, counter: counting.Counter = None, logger: loggers.Logger = None, checkpoint: bool = True, @@ -74,7 +74,7 @@ def __init__( discount=discount, shared_weights=shared_weights, exploration_scheduler=exploration_scheduler, - clipping=clipping, + max_gradient_norm=max_gradient_norm, counter=counter, logger=logger, checkpoint=checkpoint, @@ -224,9 +224,8 @@ def _backward(self) -> None: # Compute gradients. gradients = self.tape.gradient(self.loss, trainable_variables) - # Maybe clip gradients. - if self._clipping: - gradients = tf.clip_by_global_norm(gradients, 40.0)[0] + # Clip gradients. + gradients = tf.clip_by_global_norm(gradients, self._max_gradient_norm)[0] # Apply gradients. self._optimizers[agent_key].apply(gradients, trainable_variables) diff --git a/mava/systems/tf/vdn/builder.py b/mava/systems/tf/vdn/builder.py index 916150ebb..0668f1ae4 100644 --- a/mava/systems/tf/vdn/builder.py +++ b/mava/systems/tf/vdn/builder.py @@ -48,7 +48,6 @@ class VDNConfig(MADQNConfig): that is made. n_step: number of steps to squash into a single transition. sigma: standard deviation of zero-mean, Gaussian exploration noise. - clipping: whether to clip gradients by global norm. replay_table_name: string indicating what name to give the replay table.""" @@ -125,7 +124,7 @@ def make_trainer( shared_weights=self._config.shared_weights, optimizer=self._config.optimizer, target_update_period=self._config.target_update_period, - clipping=self._config.clipping, + max_gradient_norm=self._config.max_gradient_norm, exploration_scheduler=exploration_scheduler, dataset=dataset, counter=counter, diff --git a/mava/systems/tf/vdn/system.py b/mava/systems/tf/vdn/system.py index fa8603445..ff53125f0 100644 --- a/mava/systems/tf/vdn/system.py +++ b/mava/systems/tf/vdn/system.py @@ -98,7 +98,7 @@ def __init__( n_step: int = 5, sequence_length: int = 20, period: int = 20, - clipping: bool = True, + max_gradient_norm: float = None, discount: float = 0.99, optimizer: snt.Optimizer = snt.optimizers.Adam(learning_rate=1e-4), target_update_period: int = 100, @@ -169,7 +169,7 @@ def __init__( n_step=n_step, sequence_length=sequence_length, period=period, - clipping=clipping, + max_gradient_norm=max_gradient_norm, checkpoint=checkpoint, optimizer=optimizer, checkpoint_subpath=checkpoint_subpath, diff --git a/mava/systems/tf/vdn/training.py b/mava/systems/tf/vdn/training.py index 951c14099..232468f68 100644 --- a/mava/systems/tf/vdn/training.py +++ b/mava/systems/tf/vdn/training.py @@ -49,7 +49,7 @@ def __init__( discount: float, shared_weights: bool, exploration_scheduler: LinearExplorationScheduler, - clipping: bool = True, + max_gradient_norm: float = None, counter: counting.Counter = None, logger: loggers.Logger = None, checkpoint: bool = True, @@ -70,7 +70,7 @@ def __init__( discount=discount, shared_weights=shared_weights, exploration_scheduler=exploration_scheduler, - clipping=clipping, + max_gradient_norm=max_gradient_norm, counter=counter, logger=logger, checkpoint=checkpoint, @@ -164,9 +164,8 @@ def _backward(self) -> None: # Compute gradients. gradients = self.tape.gradient(self.loss, trainable_variables) - # Maybe clip gradients. - if self._clipping: - gradients = tf.clip_by_global_norm(gradients, 40.0)[0] + # Clip gradients. + gradients = tf.clip_by_global_norm(gradients, self._max_gradient_norm)[0] # Apply gradients. self._optimizers[agent_key].apply(gradients, trainable_variables) From a1c0e7cc47301aaa370fb5c7c15a1d900a1ca967 Mon Sep 17 00:00:00 2001 From: Kale-ab Date: Tue, 25 May 2021 19:44:58 +0200 Subject: [PATCH 084/188] feat: Updated dial. --- mava/systems/tf/dial/builder.py | 4 ++-- mava/systems/tf/dial/system.py | 4 ++-- mava/systems/tf/dial/training.py | 4 ++-- mava/systems/tf/madqn/training.py | 4 ++-- 4 files changed, 8 insertions(+), 8 deletions(-) diff --git a/mava/systems/tf/dial/builder.py b/mava/systems/tf/dial/builder.py index c7e5d0e21..148f90b97 100644 --- a/mava/systems/tf/dial/builder.py +++ b/mava/systems/tf/dial/builder.py @@ -59,7 +59,7 @@ class DIALConfig: shared_weights: bool target_update_period: int executor_variable_update_period: int - clipping: bool + max_gradient_norm: Optional[float] min_replay_size: int max_replay_size: int samples_per_insert: Optional[float] @@ -314,7 +314,7 @@ def make_trainer( shared_weights=self._config.shared_weights, optimizer=self._config.optimizer, target_update_period=self._config.target_update_period, - clipping=self._config.clipping, + max_gradient_norm=self._config.max_gradient_norm, exploration_scheduler=exploration_scheduler, communication_module=communication_module, dataset=dataset, diff --git a/mava/systems/tf/dial/system.py b/mava/systems/tf/dial/system.py index 8edcdb697..9570ef437 100644 --- a/mava/systems/tf/dial/system.py +++ b/mava/systems/tf/dial/system.py @@ -109,7 +109,7 @@ def __init__( n_step: int = 5, sequence_length: int = 6, period: int = 20, - clipping: bool = True, + max_gradient_norm: float = None, discount: float = 1, optimizer: snt.Optimizer = snt.optimizers.Adam(learning_rate=1e-4), target_update_period: int = 100, @@ -178,7 +178,7 @@ def __init__( n_step=n_step, sequence_length=sequence_length, period=period, - clipping=clipping, + max_gradient_norm=max_gradient_norm, checkpoint=checkpoint, optimizer=optimizer, checkpoint_subpath=checkpoint_subpath, diff --git a/mava/systems/tf/dial/training.py b/mava/systems/tf/dial/training.py index 5146d57b5..080650edc 100644 --- a/mava/systems/tf/dial/training.py +++ b/mava/systems/tf/dial/training.py @@ -53,7 +53,7 @@ def __init__( shared_weights: bool, exploration_scheduler: LinearExplorationScheduler, communication_module: BaseCommunicationModule, - clipping: bool = True, + max_gradient_norm: float = None, fingerprint: bool = False, counter: counting.Counter = None, logger: loggers.Logger = None, @@ -71,7 +71,7 @@ def __init__( discount=discount, shared_weights=shared_weights, exploration_scheduler=exploration_scheduler, - clipping=clipping, + max_gradient_norm=max_gradient_norm, fingerprint=fingerprint, counter=counter, logger=logger, diff --git a/mava/systems/tf/madqn/training.py b/mava/systems/tf/madqn/training.py index 526e65a4d..ab971894b 100644 --- a/mava/systems/tf/madqn/training.py +++ b/mava/systems/tf/madqn/training.py @@ -440,7 +440,7 @@ def __init__( shared_weights: bool, exploration_scheduler: LinearExplorationScheduler, communication_module: BaseCommunicationModule, - clipping: bool = True, + max_gradient_norm: float = None, fingerprint: bool = False, counter: counting.Counter = None, logger: loggers.Logger = None, @@ -458,7 +458,7 @@ def __init__( discount=discount, shared_weights=shared_weights, exploration_scheduler=exploration_scheduler, - clipping=clipping, + max_gradient_norm=max_gradient_norm, fingerprint=fingerprint, counter=counter, logger=logger, From 92863f3b7b5a37ca8304c79f0a0d4c9844fbbc5f Mon Sep 17 00:00:00 2001 From: Dries Date: Tue, 25 May 2021 21:24:03 +0200 Subject: [PATCH 085/188] fix: Fix centralised training bug. --- .../tf/architectures/centralised.py | 7 ++++++ mava/components/tf/architectures/networked.py | 1 - mava/systems/tf/maddpg/training.py | 24 +++++++++++++++---- 3 files changed, 27 insertions(+), 5 deletions(-) diff --git a/mava/components/tf/architectures/centralised.py b/mava/components/tf/architectures/centralised.py index b84b5b1c0..0c92c5dfd 100644 --- a/mava/components/tf/architectures/centralised.py +++ b/mava/components/tf/architectures/centralised.py @@ -143,6 +143,10 @@ def _get_critic_specs( for agent_type, agents in agents_by_type.items(): agent_key = agent_type if self._shared_weights else agents[0] + + # TODO (dries): Add a check to see if all + # self._embed_specs[agent_key].shape are of the same shape + critic_obs_shape = list(copy.copy(self._embed_specs[agent_key].shape)) critic_obs_shape.insert(0, len(agents)) obs_specs_per_type[agent_type] = tf.TensorSpec( @@ -153,6 +157,9 @@ def _get_critic_specs( critic_act_shape = list( copy.copy(self._agent_specs[agents[0]].actions.shape) ) + # TODO (dries): Add a check to see if all + # self._agent_specs[agents[0]].actions.shape are of the same shape + critic_act_shape.insert(0, len(agents)) action_specs_per_type[agent_type] = tf.TensorSpec( shape=critic_act_shape, diff --git a/mava/components/tf/architectures/networked.py b/mava/components/tf/architectures/networked.py index 3b63ea40d..92e282ea4 100644 --- a/mava/components/tf/architectures/networked.py +++ b/mava/components/tf/architectures/networked.py @@ -126,5 +126,4 @@ def _get_critic_specs( shape=critic_act_shape, dtype=tf.dtypes.float32, ) - return critic_obs_specs, critic_act_specs diff --git a/mava/systems/tf/maddpg/training.py b/mava/systems/tf/maddpg/training.py index dbf0f7381..d633b08eb 100644 --- a/mava/systems/tf/maddpg/training.py +++ b/mava/systems/tf/maddpg/training.py @@ -621,10 +621,25 @@ def _get_critic_feed( ) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor, tf.Tensor]: # Centralised based - o_tm1_feed = tf.stack([x for x in o_tm1_trans.values()], 1) - o_t_feed = tf.stack([x for x in o_t_trans.values()], 1) - a_tm1_feed = tf.stack([x for x in a_tm1.values()], 1) - a_t_feed = tf.stack([x for x in a_t.values()], 1) + # o_tm1_feed = tf.stack([x for x in o_tm1_trans.values()], 1) + # o_t_feed = tf.stack([x for x in o_t_trans.values()], 1) + # a_tm1_feed = tf.stack([x for x in a_tm1.values()], 1) + # a_t_feed = tf.stack([x for x in a_t.values()], 1) + + o_tm1_vals = [] + o_t_vals = [] + a_tm1_vals = [] + a_t_vals = [] + for connected_agent in o_tm1_trans.keys(): + o_tm1_vals.append(o_tm1_trans[connected_agent]) + o_t_vals.append(o_t_trans[connected_agent]) + a_tm1_vals.append(a_tm1[connected_agent]) + a_t_vals.append(a_t[connected_agent]) + o_tm1_feed = tf.stack(o_tm1_vals, 1) + o_t_feed = tf.stack(o_t_vals, 1) + a_tm1_feed = tf.stack(a_tm1_vals, 1) + a_t_feed = tf.stack(a_t_vals, 1) + return o_tm1_feed, o_t_feed, a_tm1_feed, a_t_feed def _get_dpg_feed( @@ -749,6 +764,7 @@ def _get_critic_feed( o_t_feed = tf.stack(o_t_vals, 1) a_tm1_feed = tf.stack(a_tm1_vals, 1) a_t_feed = tf.stack(a_t_vals, 1) + return o_tm1_feed, o_t_feed, a_tm1_feed, a_t_feed def _get_dpg_feed( From 56e0d99956bdf20633217c0f164ba7655950a103 Mon Sep 17 00:00:00 2001 From: Dries Date: Tue, 25 May 2021 21:26:12 +0200 Subject: [PATCH 086/188] fix: Small fixes. --- mava/systems/tf/maddpg/training.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/mava/systems/tf/maddpg/training.py b/mava/systems/tf/maddpg/training.py index ef0ebeffd..d90c17df8 100644 --- a/mava/systems/tf/maddpg/training.py +++ b/mava/systems/tf/maddpg/training.py @@ -629,11 +629,11 @@ def _get_critic_feed( o_t_vals = [] a_tm1_vals = [] a_t_vals = [] - for connected_agent in o_tm1_trans.keys(): - o_tm1_vals.append(o_tm1_trans[connected_agent]) - o_t_vals.append(o_t_trans[connected_agent]) - a_tm1_vals.append(a_tm1[connected_agent]) - a_t_vals.append(a_t[connected_agent]) + for agent_key in o_tm1_trans.keys(): + o_tm1_vals.append(o_tm1_trans[agent_key]) + o_t_vals.append(o_t_trans[agent_key]) + a_tm1_vals.append(a_tm1[agent_key]) + a_t_vals.append(a_t[agent_key]) o_tm1_feed = tf.stack(o_tm1_vals, 1) o_t_feed = tf.stack(o_t_vals, 1) a_tm1_feed = tf.stack(a_tm1_vals, 1) From 2933b7443d19973e2fa9bf7df4be5b9de53efd84 Mon Sep 17 00:00:00 2001 From: Dries Date: Tue, 25 May 2021 21:38:16 +0200 Subject: [PATCH 087/188] fix: Fix state based and recurrent versions. --- mava/systems/tf/maddpg/training.py | 65 +++++++++++++++++++++++------- 1 file changed, 51 insertions(+), 14 deletions(-) diff --git a/mava/systems/tf/maddpg/training.py b/mava/systems/tf/maddpg/training.py index d90c17df8..ed2f1916f 100644 --- a/mava/systems/tf/maddpg/training.py +++ b/mava/systems/tf/maddpg/training.py @@ -620,10 +620,10 @@ def _get_critic_feed( ) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor, tf.Tensor]: # Centralised based - # o_tm1_feed = tf.stack([x for x in o_tm1_trans.values()], 1) - # o_t_feed = tf.stack([x for x in o_t_trans.values()], 1) - # a_tm1_feed = tf.stack([x for x in a_tm1.values()], 1) - # a_t_feed = tf.stack([x for x in a_t.values()], 1) + # o_tm1_feed_cen = tf.stack([x for x in o_tm1_trans.values()], 1) + # o_t_feed_cen = tf.stack([x for x in o_t_trans.values()], 1) + # a_tm1_feed_cen = tf.stack([x for x in a_tm1.values()], 1) + # a_t_feed_cen = tf.stack([x for x in a_t.values()], 1) o_tm1_vals = [] o_t_vals = [] @@ -639,6 +639,12 @@ def _get_critic_feed( a_tm1_feed = tf.stack(a_tm1_vals, 1) a_t_feed = tf.stack(a_t_vals, 1) + # assert o_tm1_feed.numpy() == o_tm1_feed_cen.numpy() + + # print( + # "o_tm1_feed: ", o_tm1_feed.shape, "o_tm1_feed_cen: ", o_tm1_feed_cen.shape + # ) + return o_tm1_feed, o_t_feed, a_tm1_feed, a_t_feed def _get_dpg_feed( @@ -871,8 +877,17 @@ def _get_critic_feed( # State based o_tm1_feed = e_tm1["s_t"] o_t_feed = e_t["s_t"] - a_tm1_feed = tf.stack([x for x in a_tm1.values()], 1) - a_t_feed = tf.stack([x for x in a_t.values()], 1) + + a_tm1_vals = [] + a_t_vals = [] + for agent_key in o_tm1_trans.keys(): + a_tm1_vals.append(a_tm1[agent_key]) + a_t_vals.append(a_t[agent_key]) + a_tm1_feed = tf.stack(a_tm1_vals, 1) + a_t_feed = tf.stack(a_t_vals, 1) + + # a_tm1_feed = tf.stack([x for x in a_tm1.values()], 1) + # a_t_feed = tf.stack([x for x in a_t.values()], 1) return o_tm1_feed, o_t_feed, a_tm1_feed, a_t_feed def _get_dpg_feed( @@ -1565,11 +1580,25 @@ def _get_critic_feed( ) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor, tf.Tensor]: # Centralised based - obs_trans_feed = tf.stack([x for x in obs_trans.values()], -1) - target_obs_trans_feed = tf.stack([x for x in target_obs_trans.values()], -1) - action_feed = tf.stack([x for x in actions.values()], -1) - target_actions_feed = tf.stack([x for x in target_actions.values()], -1) - return obs_trans_feed, target_obs_trans_feed, action_feed, target_actions_feed + obs_trans_vals = [] + target_obs_trans_vals = [] + actions_vals = [] + target_actions_vals = [] + for agent_key in obs_trans.keys(): + obs_trans_vals.append(obs_trans[agent_key]) + target_obs_trans_vals.append(target_obs_trans[agent_key]) + actions_vals.append(actions[agent_key]) + target_actions_vals.append(target_actions[agent_key]) + obs_trans_feed = tf.stack(obs_trans_vals, 1) + target_obs_trans_feed = tf.stack(target_obs_trans_vals, 1) + actions_feed = tf.stack(actions_vals, 1) + target_actions_feed = tf.stack(target_actions_vals, 1) + + # obs_trans_feed = tf.stack([x for x in obs_trans.values()], -1) + # target_obs_trans_feed = tf.stack([x for x in target_obs_trans.values()], -1) + # actions_feed = tf.stack([x for x in actions.values()], -1) + # target_actions_feed = tf.stack([x for x in target_actions.values()], -1) + return obs_trans_feed, target_obs_trans_feed, actions_feed, target_actions_feed def _get_dpg_feed( self, @@ -1680,9 +1709,17 @@ def _get_critic_feed( # State based obs_trans_feed = extras["s_t"] target_obs_trans_feed = extras["s_t"] - action_feed = tf.stack([x for x in actions.values()], -1) - target_actions_feed = tf.stack([x for x in target_actions.values()], -1) - return obs_trans_feed, target_obs_trans_feed, action_feed, target_actions_feed + actions_vals = [] + target_actions_vals = [] + for agent_key in obs_trans.keys(): + actions_vals.append(actions[agent_key]) + target_actions_vals.append(target_actions[agent_key]) + actions_feed = tf.stack(actions_vals, 1) + target_actions_feed = tf.stack(target_actions_vals, 1) + + # actions_feed = tf.stack([x for x in actions.values()], -1) + # target_actions_feed = tf.stack([x for x in target_actions.values()], -1) + return obs_trans_feed, target_obs_trans_feed, actions_feed, target_actions_feed def _get_dpg_feed( self, From dc4acf827176fcf65da5b227e462771ec6fbddae Mon Sep 17 00:00:00 2001 From: St John Grimbly Date: Tue, 25 May 2021 21:44:28 +0200 Subject: [PATCH 088/188] fix: get Qmix trainer loops running again. --- .../debugging_envs/run_feedforward_qmix.py | 3 +- .../debugging_envs/run_feedforward_vdn.py | 2 +- .../components/tf/modules/mixing/monotonic.py | 25 +++---- mava/components/tf/networks/hypernetwork.py | 8 +-- mava/components/tf/networks/monotonic.py | 67 ++++++++++------- mava/systems/tf/qmix/builder.py | 17 +++-- mava/systems/tf/qmix/system.py | 72 +++++++++++++++---- mava/systems/tf/qmix/training.py | 38 +++++++++- mava/systems/tf/vdn/system.py | 1 + 9 files changed, 167 insertions(+), 66 deletions(-) diff --git a/examples/debugging_envs/run_feedforward_qmix.py b/examples/debugging_envs/run_feedforward_qmix.py index 64d3fa1d4..b0417862c 100644 --- a/examples/debugging_envs/run_feedforward_qmix.py +++ b/examples/debugging_envs/run_feedforward_qmix.py @@ -63,7 +63,7 @@ def make_networks( environment_spec: mava_specs.MAEnvironmentSpec, q_networks_layer_sizes: Union[Dict[str, Sequence], Sequence] = (512, 512, 256), - shared_weights: bool = True, + shared_weights: bool = False, ) -> Mapping[str, types.TensorTransformation]: """Creates networks used by the agents.""" @@ -150,6 +150,7 @@ def main(_: Any) -> None: exploration_scheduler_fn=LinearExplorationScheduler, epsilon_min=0.05, epsilon_decay=3e-4, + max_replay_size=1000000, optimizer=snt.optimizers.RMSProp(learning_rate=1e-4), checkpoint_subpath=checkpoint_dir, ).build() diff --git a/examples/debugging_envs/run_feedforward_vdn.py b/examples/debugging_envs/run_feedforward_vdn.py index 4d2652bdf..fae4ea2a4 100644 --- a/examples/debugging_envs/run_feedforward_vdn.py +++ b/examples/debugging_envs/run_feedforward_vdn.py @@ -120,7 +120,7 @@ def main(_: Any) -> None: debugging_utils.make_environment, env_name=FLAGS.env_name, action_space=FLAGS.action_space, - num_agents=2, + num_agents=3, return_state_info=True, ) diff --git a/mava/components/tf/modules/mixing/monotonic.py b/mava/components/tf/modules/mixing/monotonic.py index 1a8fe9a09..a31251c86 100644 --- a/mava/components/tf/modules/mixing/monotonic.py +++ b/mava/components/tf/modules/mixing/monotonic.py @@ -17,7 +17,7 @@ # - [] Complete class for monotonic mixing """Mixing for multi-agent RL systems""" - +import copy from typing import Dict, Optional import sonnet as snt @@ -59,26 +59,21 @@ def __init__( if agent_networks is None: agent_networks = self._architecture.create_actor_variables() - self._agent_networks = {} - self._agent_networks["values"] = agent_networks["values"] - self._agent_networks["target_values"] = agent_networks["target_values"] + self._agent_networks = agent_networks + # self._agent_networks["values"] = agent_networks["values"] + # self._agent_networks["target_values"] = agent_networks["target_values"] def _create_mixing_layer(self) -> snt.Module: """Modify and return system architecture given mixing structure.""" state_specs = self._environment_spec.get_extra_specs() state_specs = state_specs["s_t"] - # TODO Currently hard coded to 3 but need to generalise - # Need to figure out how to get the size of one-hot representation - # of the global state that is returned by the environment. - # One hot input dimension of global state - # state_specs = tf.TensorSpec(shape=(1, 3)) # Check this shape - self._num_agents = len(self._agent_networks["values"]) - q_value_dim = tf.TensorSpec(self._num_agents) + + self._n_agents = len(self._agent_networks["values"]) + q_value_dim = tf.TensorSpec(self._n_agents) # Implement method from base class self._mixed_network = MonotonicMixingNetwork( - self._architecture, - self._agent_networks, + self._n_agents, self._qmix_hidden_dim, num_hypernet_layers=self._num_hypernet_layers, hypernet_hidden_dim=self._hypernet_hidden_dim, @@ -90,5 +85,7 @@ def _create_mixing_layer(self) -> snt.Module: def create_system(self) -> Dict[str, Dict[str, snt.Module]]: # Implement method from base class self._agent_networks["mixing"] = self._create_mixing_layer() - self._agent_networks["target_mixing"] = self._create_mixing_layer() + self._agent_networks["target_mixing"] = copy.deepcopy( + self._agent_networks["mixing"] + ) return self._agent_networks diff --git a/mava/components/tf/networks/hypernetwork.py b/mava/components/tf/networks/hypernetwork.py index f0462efcb..8771d4ef9 100644 --- a/mava/components/tf/networks/hypernetwork.py +++ b/mava/components/tf/networks/hypernetwork.py @@ -74,16 +74,14 @@ def __init__( self.hyper_b1 = snt.nets.MLP(output_sizes=[self._qmix_hidden_dim]) self.hyper_b2 = snt.nets.MLP(output_sizes=[self._qmix_hidden_dim, 1]) - def __call__( - self, states: Tensor # [batch_size=B, self._state_dim=3] - ) -> Dict[str, float]: + def __call__(self, states: Tensor) -> Dict[str, float]: # [batch_size=B, state_dim] w1 = tf.abs( self.hyper_w1(states) ) # [B, qmix_hidden_dim] = [B, qmix_hidden_dim] w1 = tf.reshape( w1, (-1, self._n_agents, self._qmix_hidden_dim), - ) # [B, n_agents, qmix_hidden_dim] = [B, 2, qmix_hidden_dim] + ) # [B, n_agents, qmix_hidden_dim] b1 = self.hyper_b1(states) # [B, qmix_hidden_dim] = [B, qmix_hidden_dim] b1 = tf.reshape(b1, [-1, 1, self._qmix_hidden_dim]) # [B, 1, qmix_hidden_dim] @@ -97,7 +95,7 @@ def __call__( b2 = tf.reshape(b2, shape=(-1, 1, 1)) # [B, 1, 1] hyperparams = {} - hyperparams["w1"] = w1 # [B, 2, qmix_hidden_dim] + hyperparams["w1"] = w1 # [B, n_agents, qmix_hidden_dim] hyperparams["b1"] = b1 # [B, 1, qmix_hidden_dim] hyperparams["w2"] = w2 # [B, qmix_hidden_dim] hyperparams["b2"] = b2 # [B, 1] diff --git a/mava/components/tf/networks/monotonic.py b/mava/components/tf/networks/monotonic.py index 9e38e0a04..089eca67e 100644 --- a/mava/components/tf/networks/monotonic.py +++ b/mava/components/tf/networks/monotonic.py @@ -13,22 +13,17 @@ # See the License for the specific language governing permissions and # limitations under the License. -# TODO (StJohn): Generalise Qmixing to allow for different activations, -# hypernetwork structures etc. - # Code inspired by PyMARL framework implementation # https://github.com/oxwhirl/pymarl/blob/master/src/modules/mixers/qmix.py """Mixing for multi-agent RL systems""" -from typing import Dict - # import launchpad as lp import sonnet as snt import tensorflow as tf -from mava.components.tf.architectures.base import BaseArchitecture -from mava.components.tf.networks.hypernetwork import HyperNetwork +# from mava.components.tf.architectures.base import BaseArchitecture +# from mava.components.tf.networks.hypernetwork import HyperNetwork class MonotonicMixingNetwork(snt.Module): @@ -39,15 +34,13 @@ class MonotonicMixingNetwork(snt.Module): def __init__( self, - architecture: BaseArchitecture, - agent_networks: Dict[str, snt.Module], + n_agents: int, qmix_hidden_dim: int = 64, num_hypernet_layers: int = 2, hypernet_hidden_dim: int = 0, ) -> None: """Initializes the mixer. Args: - architecture: the BaseArchitecture used. state_shape: The state shape as defined by the environment. n_agents: The number of agents (i.e. Q-values) to mix. qmix_hidden_dim: Mixing layers hidden dimensions. @@ -56,36 +49,62 @@ def __init__( layer. Relevant for num_hypernet_layers > 1. """ super(MonotonicMixingNetwork, self).__init__() - self._architecture = architecture - self._agent_networks = agent_networks + self._n_agents = n_agents self._qmix_hidden_dim = qmix_hidden_dim self._num_hypernet_layers = num_hypernet_layers self._hypernet_hidden_dim = hypernet_hidden_dim # Create hypernetwork - self._hypernetworks = HyperNetwork( - self._agent_networks, - self._qmix_hidden_dim, - self._num_hypernet_layers, - self._hypernet_hidden_dim, + # self._hypernetworks = HyperNetwork( + # self._agent_networks, + # self._qmix_hidden_dim, + # self._num_hypernet_layers, + # self._hypernet_hidden_dim, + # ) + self.hyper_w1 = snt.nets.MLP( + output_sizes=[self._qmix_hidden_dim * self._n_agents] ) + self.hyper_w2 = snt.nets.MLP(output_sizes=[self._qmix_hidden_dim]) + self.hyper_b1 = snt.nets.MLP(output_sizes=[self._qmix_hidden_dim]) + self.hyper_b2 = snt.nets.MLP(output_sizes=[self._qmix_hidden_dim, 1]) def __call__( self, - q_values: tf.Tensor, # [batch_size, n_agents] = [B,2] - states: tf.Tensor, # [batch_size, one_hot_state_dim = 3] + q_values: tf.Tensor, # [batch_size, n_agents] + states: tf.Tensor, # [batch_size, state_dim] ) -> tf.Tensor: """Monotonic mixing logic.""" # Expand dimensions to [B, 1, n_agents] = [B,1,2] for matmul q_values = tf.expand_dims(q_values, axis=1) - self._hyperparams = self._hypernetworks(states) + + # --- HYPERNET ---- + w1 = tf.abs( + self.hyper_w1(states) + ) # [B, qmix_hidden_dim] = [B, qmix_hidden_dim] + w1 = tf.reshape( + w1, + (-1, self._n_agents, self._qmix_hidden_dim), + ) # [B, n_agents, qmix_hidden_dim] + + b1 = self.hyper_b1(states) # [B, qmix_hidden_dim] = [B, qmix_hidden_dim] + b1 = tf.reshape(b1, [-1, 1, self._qmix_hidden_dim]) # [B, 1, qmix_hidden_dim] + + w2 = tf.abs(self.hyper_w2(states)) + w2 = tf.reshape( + w2, shape=(-1, self._qmix_hidden_dim, 1) + ) # [B, qmix_hidden_dim, 1] + + b2 = self.hyper_b2(states) # [B, 1] + b2 = tf.reshape(b2, shape=(-1, 1, 1)) # [B, 1, 1] + # ----------------- + # self._hyperparams = self._hypernetworks(states) # For convenience - w1 = self._hyperparams["w1"] # [B, 2, qmix_hidden_dim] - b1 = self._hyperparams["b1"] # [B, 1, qmix_hidden_dim] - w2 = self._hyperparams["w2"] # [B, qmix_hidden_dim, 1] - b2 = self._hyperparams["b2"] # [B, 1, 1] + # w1 = self._hyperparams["w1"] # [B, n_agents, qmix_hidden_dim] + # b1 = self._hyperparams["b1"] # [B, 1, qmix_hidden_dim] + # w2 = self._hyperparams["w2"] # [B, qmix_hidden_dim, 1] + # b2 = self._hyperparams["b2"] # [B, 1, 1] # ELU -> Exp. linear unit hidden = tf.nn.elu(tf.matmul(q_values, w1) + b1) # [B, 1, qmix_hidden_dim] diff --git a/mava/systems/tf/qmix/builder.py b/mava/systems/tf/qmix/builder.py index 4fdafbd19..fe5d420b1 100644 --- a/mava/systems/tf/qmix/builder.py +++ b/mava/systems/tf/qmix/builder.py @@ -13,11 +13,14 @@ # See the License for the specific language governing permissions and # limitations under the License. +import copy import dataclasses from typing import Any, Dict, Iterator, Optional, Type import reverb import sonnet as snt +import tensorflow as tf +from acme.tf import utils as tf_utils from acme.utils import counting from mava import core, types @@ -26,6 +29,7 @@ LinearExplorationScheduler, ) from mava.components.tf.modules.stabilising import FingerPrintStabalisation +from mava.components.tf.networks import MonotonicMixingNetwork from mava.systems.tf.madqn.builder import MADQNBuilder, MADQNConfig from mava.systems.tf.qmix import execution, training from mava.wrappers import DetailedTrainerStatisticsWithEpsilon @@ -69,6 +73,7 @@ def __init__( config: QMIXConfig, trainer_fn: Type[training.QMIXTrainer] = training.QMIXTrainer, executor_fn: Type[core.Executor] = execution.QMIXFeedForwardExecutor, + mixer: Type[MonotonicMixingNetwork] = MonotonicMixingNetwork, extra_specs: Dict[str, Any] = {}, exploration_scheduler_fn: Type[ LinearExplorationScheduler @@ -87,6 +92,7 @@ def __init__( exploration_scheduler_fn=exploration_scheduler_fn, replay_stabilisation_fn=replay_stabilisation_fn, ) + self._mixer = mixer def make_trainer( self, @@ -105,13 +111,16 @@ def make_trainer( executor steps, etc.) distributed throughout the system. logger: Logger object for logging metadata. """ + agents = self._config.environment_spec.get_agent_ids() + agent_types = self._config.environment_spec.get_agent_types() + q_networks = networks["values"] target_q_networks = networks["target_values"] - mixing_network = networks["mixing"] - target_mixing_network = networks["target_mixing"] - agents = self._config.environment_spec.get_agent_ids() - agent_types = self._config.environment_spec.get_agent_types() + mixing_network = self._mixer(len(agents)) + state_spec = self._config.environment_spec.get_extra_specs()["s_t"] + tf_utils.create_variables(mixing_network, [tf.ones(len(agents)), state_spec]) + target_mixing_network = copy.deepcopy(mixing_network) # Make epsilon scheduler exploration_scheduler = self._exploration_scheduler_fn( diff --git a/mava/systems/tf/qmix/system.py b/mava/systems/tf/qmix/system.py index 76cd44e83..28fa8a4f2 100644 --- a/mava/systems/tf/qmix/system.py +++ b/mava/systems/tf/qmix/system.py @@ -27,8 +27,10 @@ from mava import core from mava import specs as mava_specs from mava.components.tf.architectures import DecentralisedValueActor -from mava.components.tf.modules import mixing +from mava.components.tf.modules.communication import BaseCommunicationModule from mava.components.tf.modules.exploration import LinearExplorationScheduler +from mava.components.tf.modules.stabilising import FingerPrintStabalisation +from mava.components.tf.networks import MonotonicMixingNetwork from mava.environment_loop import ParallelEnvironmentLoop from mava.systems.tf import executors from mava.systems.tf.madqn.system import MADQN @@ -80,10 +82,12 @@ def __init__( architecture: Type[DecentralisedValueActor] = DecentralisedValueActor, trainer_fn: Type[training.QMIXTrainer] = training.QMIXTrainer, executor_fn: Type[core.Executor] = execution.QMIXFeedForwardExecutor, - mixer: Type[mixing.MonotonicMixing] = mixing.MonotonicMixing, + mixer: Type[MonotonicMixingNetwork] = MonotonicMixingNetwork, + communication_module: Type[BaseCommunicationModule] = None, exploration_scheduler_fn: Type[ LinearExplorationScheduler ] = LinearExplorationScheduler, + replay_stabilisation_fn: Optional[Type[FingerPrintStabalisation]] = None, epsilon_min: float = 0.05, epsilon_decay: float = 1e-4, num_executors: int = 1, @@ -130,20 +134,41 @@ def __init__( ) super(QMIX, self).__init__( - architecture=architecture, environment_factory=environment_factory, network_factory=network_factory, logger_factory=logger_factory, - environment_spec=environment_spec, - shared_weights=shared_weights, + architecture=architecture, + trainer_fn=trainer_fn, + communication_module=communication_module, + executor_fn=executor_fn, + exploration_scheduler_fn=exploration_scheduler_fn, + replay_stabilisation_fn=replay_stabilisation_fn, + epsilon_min=epsilon_min, + epsilon_decay=epsilon_decay, num_executors=num_executors, num_caches=num_caches, + environment_spec=environment_spec, + shared_weights=shared_weights, + batch_size=batch_size, + prefetch_size=prefetch_size, + min_replay_size=min_replay_size, + max_replay_size=max_replay_size, + samples_per_insert=samples_per_insert, + n_step=n_step, + sequence_length=sequence_length, + period=period, + clipping=clipping, + discount=discount, + optimizer=optimizer, + target_update_period=target_update_period, + executor_variable_update_period=executor_variable_update_period, max_executor_steps=max_executor_steps, - checkpoint_subpath=checkpoint_subpath, checkpoint=checkpoint, + checkpoint_subpath=checkpoint_subpath, + logger_config=logger_config, train_loop_fn=train_loop_fn, - train_loop_fn_kwargs=train_loop_fn_kwargs, eval_loop_fn=eval_loop_fn, + train_loop_fn_kwargs=train_loop_fn_kwargs, eval_loop_fn_kwargs=eval_loop_fn_kwargs, ) @@ -178,6 +203,8 @@ def __init__( executor_fn=executor_fn, extra_specs=extra_specs, exploration_scheduler_fn=exploration_scheduler_fn, + replay_stabilisation_fn=replay_stabilisation_fn, + mixer=mixer, ) def trainer( @@ -198,15 +225,29 @@ def trainer( shared_weights=self._shared_weights, ) - agent_networks = architecture.create_actor_variables() + if self._builder._replay_stabiliser_fn is not None: + architecture = self._builder._replay_stabiliser_fn( # type: ignore + architecture + ) - # Augment network architecture by adding mixing layer network. - system_networks = self._mixer( - architecture=architecture, - environment_spec=self._environment_spec, - agent_networks=agent_networks, - num_hypernet_layers=1, - ).create_system() + communication_module = None + if self._communication_module_fn is not None: + communication_module = self._communication_module_fn( + architecture=architecture, + shared=True, + channel_size=1, + channel_noise=0, + ) + system_networks = communication_module.create_system() + else: + system_networks = architecture.create_system() + + # # Augment network architecture by adding mixing layer network. + # system_networks = self._mixer( + # environment_spec=self._environment_spec, + # agent_networks=system_networks, + # num_hypernet_layers=1, + # ).create_system() # create logger trainer_logger_config = {} @@ -223,6 +264,7 @@ def trainer( networks=system_networks, dataset=dataset, counter=counter, + communication_module=communication_module, logger=trainer_logger, ) diff --git a/mava/systems/tf/qmix/training.py b/mava/systems/tf/qmix/training.py index 99ca0cfc7..499b9be5a 100644 --- a/mava/systems/tf/qmix/training.py +++ b/mava/systems/tf/qmix/training.py @@ -15,6 +15,7 @@ """Qmix trainer implementation.""" +import time from typing import Any, Dict, List, Optional, Sequence import numpy as np @@ -127,9 +128,11 @@ def _update_target_networks(self) -> None: # NOTE These shouldn't really be in the agent for loop. online_variables = [ *self._mixing_network.variables, + # *self._mixing_network._hypernetworks.variables, ] target_variables = [ *self._target_mixing_network.variables, + # *self._target_mixing_network._hypernetworks.variables, ] # Make online -> target network update ops. for src, dest in zip(online_variables, target_variables): @@ -137,6 +140,34 @@ def _update_target_networks(self) -> None: self._num_steps.assign_add(1) + def step(self) -> None: + # Run the learning step. + fetches = self._step() + + # Compute elapsed time. + timestamp = time.time() + if self._timestamp: + elapsed_time = timestamp - self._timestamp + else: + elapsed_time = 0 + self._timestamp = timestamp # type: ignore + + # Update our counts and record it. + counts = self._counter.increment(steps=1, walltime=elapsed_time) + fetches.update(counts) + + # Checkpoint and attempt to write the logs. + if self._checkpoint: + train_utils.checkpoint_networks(self._system_checkpointer) + + # Log and decrement epsilon + epsilon = self.get_epsilon() + fetches["epsilon"] = epsilon + self._decrement_epsilon() + + if self._logger: + self._logger.write(fetches) + @tf.function def _step( self, @@ -229,14 +260,17 @@ def _backward(self) -> None: agent_key = self.agent_net_keys[agent] # Update agent networks - variables = self._q_networks[agent_key].trainable_variables + variables = [*self._q_networks[agent_key].trainable_variables] gradients = self.tape.gradient(self.loss, variables) if self._clipping: gradients = tf.clip_by_global_norm(gradients, 40.0)[0] self._optimizers[agent_key].apply(gradients, variables) # Update mixing network - variables = self._mixing_network.trainable_variables + variables = [ + *self._mixing_network.trainable_variables, + # *self._mixing_network._hypernetworks.trainable_variables, + ] gradients = self.tape.gradient(self.loss, variables) if self._clipping: gradients = tf.clip_by_global_norm(gradients, 40.0)[0] diff --git a/mava/systems/tf/vdn/system.py b/mava/systems/tf/vdn/system.py index 0d5e0eb29..f68ff4f85 100644 --- a/mava/systems/tf/vdn/system.py +++ b/mava/systems/tf/vdn/system.py @@ -119,6 +119,7 @@ def __init__( environment_spec = mava_specs.MAEnvironmentSpec( environment_factory(evaluation=False) # type:ignore ) + self._environment_spec = environment_spec # set default logger if no logger provided if not logger_factory: From 43c67cb3798fae82811bb583278088695f6946ab Mon Sep 17 00:00:00 2001 From: Dries Date: Tue, 25 May 2021 21:45:04 +0200 Subject: [PATCH 089/188] fix: Small space fix. --- mava/components/tf/architectures/networked.py | 1 + 1 file changed, 1 insertion(+) diff --git a/mava/components/tf/architectures/networked.py b/mava/components/tf/architectures/networked.py index 92e282ea4..3b63ea40d 100644 --- a/mava/components/tf/architectures/networked.py +++ b/mava/components/tf/architectures/networked.py @@ -126,4 +126,5 @@ def _get_critic_specs( shape=critic_act_shape, dtype=tf.dtypes.float32, ) + return critic_obs_specs, critic_act_specs From e41baf0104bff4e67c8421e9ec64e7b53bdc8239 Mon Sep 17 00:00:00 2001 From: Dries Date: Wed, 26 May 2021 05:51:19 +0200 Subject: [PATCH 090/188] fix: Fixes .keys() and .values() code in MA-DDPG trainers. --- examples/README.md | 9 +- .../run_centralised_feedforward_maddpg.py | 4 +- .../run_state_based_feedforward_maddpg.py | 208 ++++++++++++++++++ mava/systems/tf/maddpg/training.py | 100 ++++----- 4 files changed, 251 insertions(+), 70 deletions(-) create mode 100644 examples/debugging_envs/run_state_based_feedforward_maddpg.py diff --git a/examples/README.md b/examples/README.md index b99aac4ce..553ee3f7c 100644 --- a/examples/README.md +++ b/examples/README.md @@ -23,15 +23,14 @@ We include a number of systems running on continuous control tasks. an MA-D4PG system on the continuous action space simple_spread MPE debugging environment. - [MA-DDPG (decentralised, recurrent)](debugging_envs/run_decentralised_recurrent_maddpg.py): an MA-DDPG system on the continuous action space simple_spread MPE debugging environment. -- [MA-DDPG (decentralised, recurrent)](debugging_envs/run_decentralised_recurrent_maddpg.py): - an MA-DDPG system on the continuous action space simple_spread MPE debugging environment. - [MA-DDPG (decentralised, feedforward)](debugging_envs/run_feedforward_maddpg_record_video.py): an MA-DDPG system (with video wrapping) on the continuous action space simple_spread MPE debugging environment. - [MA-DDPG (networked, feedforward)](debugging_envs/run_networked_feedforward_maddpg.py): - an MA-D4PG system on the continuous action space simple_spread MPE debugging environment. - + an MA-DDPG system on the continuous action space simple_spread MPE debugging environment. +- [MA-DDPG (state-based, feedforward)](debugging_envs/run_state_based_feedforward_maddpg.py): + an MA-DDPG system on the continuous action space simple_spread MPE debugging environment. - [MA-DDPG (state-based, recurrent)](debugging_envs/run_state_based_recurrent_maddpg.py): - an MA-D4PG system on the continuous action space simple_spread MPE debugging environment. + an MA-DDPG system on the continuous action space simple_spread MPE debugging environment. - [PPO (centralised, feedforward)](petting_zoo/run_centralised_feedforward_mappo.py): a PPO system using a centralised critic on the continuous action space multiwalker_v7 PettingZoo environment. - [MA-D4PG (decentralised, feedforward)](petting_zoo/run_decentralised_feedforward_mad4pg.py): diff --git a/examples/debugging_envs/run_centralised_feedforward_maddpg.py b/examples/debugging_envs/run_centralised_feedforward_maddpg.py index 98b829731..4680230bc 100644 --- a/examples/debugging_envs/run_centralised_feedforward_maddpg.py +++ b/examples/debugging_envs/run_centralised_feedforward_maddpg.py @@ -30,7 +30,7 @@ from launchpad.nodes.python.local_multi_processing import PythonProcess from mava import specs as mava_specs -from mava.components.tf.architectures import CentralisedQValueCritic +from mava.components.tf import architectures from mava.systems.tf import maddpg from mava.utils import lp_utils from mava.utils.environments import debugging_utils @@ -177,7 +177,7 @@ def main(_: Any) -> None: environment_factory=environment_factory, network_factory=network_factory, logger_config=logger_config, - architecture=CentralisedQValueCritic, + architecture=architectures.CentralisedQValueCritic, num_executors=2, policy_optimizer=snt.optimizers.Adam(learning_rate=1e-4), critic_optimizer=snt.optimizers.Adam(learning_rate=1e-4), diff --git a/examples/debugging_envs/run_state_based_feedforward_maddpg.py b/examples/debugging_envs/run_state_based_feedforward_maddpg.py new file mode 100644 index 000000000..c127de397 --- /dev/null +++ b/examples/debugging_envs/run_state_based_feedforward_maddpg.py @@ -0,0 +1,208 @@ +# python3 +# Copyright 2021 [...placeholder...]. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Example running feedforward MADDPG on debug MPE environments.""" + +import functools +from datetime import datetime +from typing import Any, Dict, Mapping, Sequence, Union + +import launchpad as lp +import numpy as np +import sonnet as snt +import tensorflow as tf +from absl import app, flags +from acme import types +from acme.tf import networks +from acme.tf import utils as tf2_utils +from launchpad.nodes.python.local_multi_processing import PythonProcess + +from mava import specs as mava_specs +from mava.components.tf import architectures +from mava.systems.tf import maddpg +from mava.utils import lp_utils +from mava.utils.environments import debugging_utils + +FLAGS = flags.FLAGS +flags.DEFINE_string( + "env_name", + "simple_spread", + "Debugging environment name (str).", +) +flags.DEFINE_string( + "action_space", + "continuous", + "Environment action space type (str).", +) +flags.DEFINE_string( + "mava_id", + str(datetime.now()), + "Experiment identifier that can be used to continue experiments.", +) +flags.DEFINE_string("base_dir", "~/mava/", "Base dir to store experiments.") + + +def make_networks( + environment_spec: mava_specs.MAEnvironmentSpec, + policy_networks_layer_sizes: Union[Dict[str, Sequence], Sequence] = ( + 256, + 256, + 256, + ), + critic_networks_layer_sizes: Union[Dict[str, Sequence], Sequence] = (512, 512, 256), + shared_weights: bool = True, + sigma: float = 0.3, +) -> Mapping[str, types.TensorTransformation]: + """Creates networks used by the agents.""" + specs = environment_spec.get_agent_specs() + + # Create agent_type specs + if shared_weights: + type_specs = {key.split("_")[0]: specs[key] for key in specs.keys()} + specs = type_specs + + if isinstance(policy_networks_layer_sizes, Sequence): + policy_networks_layer_sizes = { + key: policy_networks_layer_sizes for key in specs.keys() + } + if isinstance(critic_networks_layer_sizes, Sequence): + critic_networks_layer_sizes = { + key: critic_networks_layer_sizes for key in specs.keys() + } + + observation_networks = {} + policy_networks = {} + critic_networks = {} + for key in specs.keys(): + + # Get total number of action dimensions from action spec. + num_dimensions = np.prod(specs[key].actions.shape, dtype=int) + + # Create the shared observation network; here simply a state-less operation. + observation_network = tf2_utils.to_sonnet_module(tf.identity) + + # Create the policy network. + policy_network = snt.Sequential( + [ + networks.LayerNormMLP( + policy_networks_layer_sizes[key], activate_final=True + ), + networks.NearZeroInitializedLinear(num_dimensions), + networks.TanhToSpec(specs[key].actions), + networks.ClippedGaussian(sigma), + networks.ClipToSpec(specs[key].actions), + ] + ) + + # Create the critic network. + critic_network = snt.Sequential( + [ + # The multiplexer concatenates the observations/actions. + networks.CriticMultiplexer(), + networks.LayerNormMLP( + critic_networks_layer_sizes[key], activate_final=False + ), + snt.Linear(1), + ] + ) + observation_networks[key] = observation_network + policy_networks[key] = policy_network + critic_networks[key] = critic_network + + return { + "policies": policy_networks, + "critics": critic_networks, + "observations": observation_networks, + } + + +def main(_: Any) -> None: + + # environment + environment_factory = functools.partial( + debugging_utils.make_environment, + env_name=FLAGS.env_name, + action_space=FLAGS.action_space, + return_state_info=True, + ) + + # networks + network_factory = lp_utils.partial_kwargs(make_networks) + + # loggers + # set custom logger config for each process + # -- log trainer,executor and evaluator to TF + # -- log only evaluator to terminal + log_every = 10 + logger_config = { + "trainer": { + "directory": FLAGS.base_dir, + "to_terminal": False, + "to_tensorboard": True, + "time_stamp": FLAGS.mava_id, + "time_delta": log_every, + }, + "executor": { + "directory": FLAGS.base_dir, + "to_terminal": False, + "to_tensorboard": True, + "time_stamp": FLAGS.mava_id, + "time_delta": log_every, + }, + "evaluator": { + "directory": FLAGS.base_dir, + "to_terminal": True, + "to_tensorboard": True, + "time_stamp": FLAGS.mava_id, + "time_delta": log_every, + }, + } + + # Checkpointer appends "Checkpoints" to checkpoint_dir + checkpoint_dir = f"{FLAGS.base_dir}/{FLAGS.mava_id}" + + # distributed program + program = maddpg.MADDPG( + environment_factory=environment_factory, + network_factory=network_factory, + logger_config=logger_config, + architecture=architectures.StateBasedQValueCritic, + num_executors=2, + policy_optimizer=snt.optimizers.Adam(learning_rate=1e-4), + critic_optimizer=snt.optimizers.Adam(learning_rate=1e-4), + checkpoint_subpath=checkpoint_dir, + max_gradient_norm=40.0, + trainer_fn=maddpg.StateBasedMADDPGTrainer, + shared_weights=False, + ).build() + + # launch + gpu_id = -1 + env_vars = {"CUDA_VISIBLE_DEVICES": str(gpu_id)} + local_resources = { + "trainer": [], + "evaluator": PythonProcess(env=env_vars), + "executor": PythonProcess(env=env_vars), + } + lp.launch( + program, + lp.LaunchType.LOCAL_MULTI_PROCESSING, + terminal="current_terminal", + local_resources=local_resources, + ) + + +if __name__ == "__main__": + app.run(main) diff --git a/mava/systems/tf/maddpg/training.py b/mava/systems/tf/maddpg/training.py index ed2f1916f..6656a52a1 100644 --- a/mava/systems/tf/maddpg/training.py +++ b/mava/systems/tf/maddpg/training.py @@ -620,30 +620,14 @@ def _get_critic_feed( ) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor, tf.Tensor]: # Centralised based - # o_tm1_feed_cen = tf.stack([x for x in o_tm1_trans.values()], 1) - # o_t_feed_cen = tf.stack([x for x in o_t_trans.values()], 1) - # a_tm1_feed_cen = tf.stack([x for x in a_tm1.values()], 1) - # a_t_feed_cen = tf.stack([x for x in a_t.values()], 1) + o_tm1_feed = tf.stack([o_tm1_trans[agent] for agent in self._agents], 1) + o_t_feed = tf.stack([o_t_trans[agent] for agent in self._agents], 1) + a_tm1_feed = tf.stack([a_tm1[agent] for agent in self._agents], 1) + a_t_feed = tf.stack([a_t[agent] for agent in self._agents], 1) - o_tm1_vals = [] - o_t_vals = [] - a_tm1_vals = [] - a_t_vals = [] - for agent_key in o_tm1_trans.keys(): - o_tm1_vals.append(o_tm1_trans[agent_key]) - o_t_vals.append(o_t_trans[agent_key]) - a_tm1_vals.append(a_tm1[agent_key]) - a_t_vals.append(a_t[agent_key]) - o_tm1_feed = tf.stack(o_tm1_vals, 1) - o_t_feed = tf.stack(o_t_vals, 1) - a_tm1_feed = tf.stack(a_tm1_vals, 1) - a_t_feed = tf.stack(a_t_vals, 1) - - # assert o_tm1_feed.numpy() == o_tm1_feed_cen.numpy() - - # print( - # "o_tm1_feed: ", o_tm1_feed.shape, "o_tm1_feed_cen: ", o_tm1_feed_cen.shape - # ) + # TODO (dries): Make sure self._agents are always in the same order. + # It might be that if a checkpoint is loaded the order might be + # different if one uses python 3.6 due to a .keys() call. return o_tm1_feed, o_t_feed, a_tm1_feed, a_t_feed @@ -659,6 +643,11 @@ def _get_dpg_feed( tree.map_structure(tf.stop_gradient, a_t) dpg_a_t_feed = copy.copy(a_t) dpg_a_t_feed[agent] = dpg_a_t + + dpg_a_t_feed = tf.squeeze( + tf.stack([dpg_a_t_feed[agent] for agent in self._agents], 1) + ) + return dpg_a_t_feed @@ -784,6 +773,9 @@ def _get_dpg_feed( tree.map_structure(tf.stop_gradient, a_t) dpg_a_t_feed = copy.copy(a_t) dpg_a_t_feed[agent] = dpg_a_t + dpg_a_t_feed = tf.squeeze( + tf.stack([dpg_a_t_feed[agent] for agent in self._agents], 1) + ) return dpg_a_t_feed @@ -877,17 +869,9 @@ def _get_critic_feed( # State based o_tm1_feed = e_tm1["s_t"] o_t_feed = e_t["s_t"] + a_tm1_feed = tf.stack([a_tm1[agent] for agent in self._agents], 1) + a_t_feed = tf.stack([a_t[agent] for agent in self._agents], 1) - a_tm1_vals = [] - a_t_vals = [] - for agent_key in o_tm1_trans.keys(): - a_tm1_vals.append(a_tm1[agent_key]) - a_t_vals.append(a_t[agent_key]) - a_tm1_feed = tf.stack(a_tm1_vals, 1) - a_t_feed = tf.stack(a_t_vals, 1) - - # a_tm1_feed = tf.stack([x for x in a_tm1.values()], 1) - # a_t_feed = tf.stack([x for x in a_t.values()], 1) return o_tm1_feed, o_t_feed, a_tm1_feed, a_t_feed def _get_dpg_feed( @@ -903,6 +887,10 @@ def _get_dpg_feed( dpg_a_t_feed = copy.copy(a_t) dpg_a_t_feed[agent] = dpg_a_t + dpg_a_t_feed = tf.squeeze( + tf.stack([dpg_a_t_feed[agent] for agent in self._agents], 1) + ) + return dpg_a_t_feed @@ -1580,24 +1568,15 @@ def _get_critic_feed( ) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor, tf.Tensor]: # Centralised based - obs_trans_vals = [] - target_obs_trans_vals = [] - actions_vals = [] - target_actions_vals = [] - for agent_key in obs_trans.keys(): - obs_trans_vals.append(obs_trans[agent_key]) - target_obs_trans_vals.append(target_obs_trans[agent_key]) - actions_vals.append(actions[agent_key]) - target_actions_vals.append(target_actions[agent_key]) - obs_trans_feed = tf.stack(obs_trans_vals, 1) - target_obs_trans_feed = tf.stack(target_obs_trans_vals, 1) - actions_feed = tf.stack(actions_vals, 1) - target_actions_feed = tf.stack(target_actions_vals, 1) - - # obs_trans_feed = tf.stack([x for x in obs_trans.values()], -1) - # target_obs_trans_feed = tf.stack([x for x in target_obs_trans.values()], -1) - # actions_feed = tf.stack([x for x in actions.values()], -1) - # target_actions_feed = tf.stack([x for x in target_actions.values()], -1) + obs_trans_feed = tf.stack([obs_trans[agent] for agent in self._agents], -1) + target_obs_trans_feed = tf.stack( + [target_obs_trans[agent] for agent in self._agents], -1 + ) + actions_feed = tf.stack([actions[agent] for agent in self._agents], -1) + target_actions_feed = tf.stack( + [target_actions[agent] for agent in self._agents], -1 + ) + return obs_trans_feed, target_obs_trans_feed, actions_feed, target_actions_feed def _get_dpg_feed( @@ -1613,7 +1592,7 @@ def _get_dpg_feed( dpg_actions_feed = copy.copy(actions) dpg_actions_feed[agent] = dpg_actions dpg_actions_feed = tf.squeeze( - tf.stack([x for x in dpg_actions_feed.values()], -1) + tf.stack([dpg_actions_feed[agent] for agent in self._agents], -1) ) return dpg_actions_feed @@ -1709,16 +1688,11 @@ def _get_critic_feed( # State based obs_trans_feed = extras["s_t"] target_obs_trans_feed = extras["s_t"] - actions_vals = [] - target_actions_vals = [] - for agent_key in obs_trans.keys(): - actions_vals.append(actions[agent_key]) - target_actions_vals.append(target_actions[agent_key]) - actions_feed = tf.stack(actions_vals, 1) - target_actions_feed = tf.stack(target_actions_vals, 1) - - # actions_feed = tf.stack([x for x in actions.values()], -1) - # target_actions_feed = tf.stack([x for x in target_actions.values()], -1) + actions_feed = tf.stack([actions[agent] for agent in self._agents], -1) + target_actions_feed = tf.stack( + [target_actions[agent] for agent in self._agents], -1 + ) + return obs_trans_feed, target_obs_trans_feed, actions_feed, target_actions_feed def _get_dpg_feed( @@ -1734,6 +1708,6 @@ def _get_dpg_feed( dpg_actions_feed = copy.copy(actions) dpg_actions_feed[agent] = dpg_actions dpg_actions_feed = tf.squeeze( - tf.stack([x for x in dpg_actions_feed.values()], -1) + tf.stack([dpg_actions_feed[agent] for agent in self._agents], -1) ) return dpg_actions_feed From 5722a7b16194d1efd420e1e8b48fb25b8b5fd248 Mon Sep 17 00:00:00 2001 From: Dries Date: Wed, 26 May 2021 06:00:43 +0200 Subject: [PATCH 091/188] fix: Sort specs and keys inside Mava specs. --- mava/specs.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/mava/specs.py b/mava/specs.py index 7d0933366..cf4851cde 100644 --- a/mava/specs.py +++ b/mava/specs.py @@ -15,8 +15,9 @@ # the specs on the wrappers themselves class MAEnvironmentSpec: def __init__(self, environment: dm_env.Environment): - self._specs = self._make_ma_environment_spec(environment) - self._keys = list(self._specs.keys()) + specs = self._make_ma_environment_spec(environment) + self._keys = list(sorted(specs.keys())) + self._specs = {key: specs[key] for key in self._keys} def _make_ma_environment_spec( self, environment: dm_env.Environment From 45397a83f393e854ef80f263c9c5a8224d047556 Mon Sep 17 00:00:00 2001 From: Dries Date: Wed, 26 May 2021 07:24:21 +0200 Subject: [PATCH 092/188] fix: Remove comments. --- mava/systems/tf/maddpg/training.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/mava/systems/tf/maddpg/training.py b/mava/systems/tf/maddpg/training.py index 6656a52a1..9e46cebe0 100644 --- a/mava/systems/tf/maddpg/training.py +++ b/mava/systems/tf/maddpg/training.py @@ -625,10 +625,6 @@ def _get_critic_feed( a_tm1_feed = tf.stack([a_tm1[agent] for agent in self._agents], 1) a_t_feed = tf.stack([a_t[agent] for agent in self._agents], 1) - # TODO (dries): Make sure self._agents are always in the same order. - # It might be that if a checkpoint is loaded the order might be - # different if one uses python 3.6 due to a .keys() call. - return o_tm1_feed, o_t_feed, a_tm1_feed, a_t_feed def _get_dpg_feed( From 733a7d538eca9c3005043914bfb9929d90f1e539 Mon Sep 17 00:00:00 2001 From: Dries Date: Wed, 26 May 2021 10:09:36 +0200 Subject: [PATCH 093/188] fix: Rename MADDPG and MAD4PG trainers. --- .../run_centralised_feedforward_maddpg.py | 2 +- .../run_decentralised_recurrent_mad4pg.py | 4 +- .../run_decentralised_recurrent_maddpg.py | 4 +- .../run_networked_feedforward_maddpg.py | 2 +- .../run_state_based_feedforward_maddpg.py | 2 +- .../run_state_based_recurrent_maddpg.py | 4 +- ...tralised_feedforward_mad4pg_continuous.py} | 0 ...tralised_feedforward_maddpg_continuous.py} | 0 .../run_decentralised_recurrent_maddpg.py | 4 +- mava/systems/tf/mad4pg/__init__.py | 12 ++++-- mava/systems/tf/mad4pg/builder.py | 9 +++-- mava/systems/tf/mad4pg/system.py | 6 +-- mava/systems/tf/mad4pg/training.py | 38 +++++++++---------- mava/systems/tf/maddpg/__init__.py | 14 ++++--- mava/systems/tf/maddpg/builder.py | 6 +-- mava/systems/tf/maddpg/system.py | 6 +-- mava/systems/tf/maddpg/training.py | 18 ++++----- 17 files changed, 70 insertions(+), 61 deletions(-) rename examples/petting_zoo/{run_decentralised_feedforward_mad4pg_continous.py => run_decentralised_feedforward_mad4pg_continuous.py} (100%) rename examples/petting_zoo/{run_decentralised_feedforward_maddpg_continous.py => run_decentralised_feedforward_maddpg_continuous.py} (100%) diff --git a/examples/debugging_envs/run_centralised_feedforward_maddpg.py b/examples/debugging_envs/run_centralised_feedforward_maddpg.py index 4680230bc..e15a71a70 100644 --- a/examples/debugging_envs/run_centralised_feedforward_maddpg.py +++ b/examples/debugging_envs/run_centralised_feedforward_maddpg.py @@ -183,7 +183,7 @@ def main(_: Any) -> None: critic_optimizer=snt.optimizers.Adam(learning_rate=1e-4), checkpoint_subpath=checkpoint_dir, max_gradient_norm=40.0, - trainer_fn=maddpg.CentralisedMADDPGTrainer, + trainer_fn=maddpg.MADDPGCentralisedTrainer, shared_weights=False, ).build() diff --git a/examples/debugging_envs/run_decentralised_recurrent_mad4pg.py b/examples/debugging_envs/run_decentralised_recurrent_mad4pg.py index d90e3dc2b..c201588b2 100644 --- a/examples/debugging_envs/run_decentralised_recurrent_mad4pg.py +++ b/examples/debugging_envs/run_decentralised_recurrent_mad4pg.py @@ -32,7 +32,7 @@ from mava.components.tf.networks.mad4pg import DiscreteValuedHead from mava.systems.tf import mad4pg from mava.systems.tf.mad4pg.execution import MAD4PGRecurrentExecutor -from mava.systems.tf.mad4pg.training import DecentralisedRecurrentMAD4PGTrainer +from mava.systems.tf.mad4pg.training import MAD4PGDecentralisedRecurrentTrainer from mava.utils import lp_utils from mava.utils.environments import debugging_utils from mava.utils.loggers import logger_utils @@ -166,7 +166,7 @@ def main(_: Any) -> None: network_factory=network_factory, logger_factory=logger_factory, num_executors=2, - trainer_fn=DecentralisedRecurrentMAD4PGTrainer, + trainer_fn=MAD4PGDecentralisedRecurrentTrainer, executor_fn=MAD4PGRecurrentExecutor, checkpoint_subpath=checkpoint_dir, ).build() diff --git a/examples/debugging_envs/run_decentralised_recurrent_maddpg.py b/examples/debugging_envs/run_decentralised_recurrent_maddpg.py index b591fd67e..8d11e9dc3 100644 --- a/examples/debugging_envs/run_decentralised_recurrent_maddpg.py +++ b/examples/debugging_envs/run_decentralised_recurrent_maddpg.py @@ -30,7 +30,7 @@ from mava import specs as mava_specs from mava.systems.tf import maddpg from mava.systems.tf.maddpg.execution import MADDPGRecurrentExecutor -from mava.systems.tf.maddpg.training import DecentralisedRecurrentMADDPGTrainer +from mava.systems.tf.maddpg.training import MADDPGDecentralisedRecurrentTrainer from mava.utils import lp_utils from mava.utils.environments import debugging_utils from mava.utils.loggers import logger_utils @@ -161,7 +161,7 @@ def main(_: Any) -> None: network_factory=network_factory, logger_factory=logger_factory, num_executors=2, - trainer_fn=DecentralisedRecurrentMADDPGTrainer, + trainer_fn=MADDPGDecentralisedRecurrentTrainer, executor_fn=MADDPGRecurrentExecutor, checkpoint_subpath=checkpoint_dir, ).build() diff --git a/examples/debugging_envs/run_networked_feedforward_maddpg.py b/examples/debugging_envs/run_networked_feedforward_maddpg.py index ffb8ae343..303160d98 100644 --- a/examples/debugging_envs/run_networked_feedforward_maddpg.py +++ b/examples/debugging_envs/run_networked_feedforward_maddpg.py @@ -184,7 +184,7 @@ def main(_: Any) -> None: critic_optimizer=snt.optimizers.Adam(learning_rate=1e-4), checkpoint_subpath=checkpoint_dir, max_gradient_norm=40.0, - trainer_fn=maddpg.NetworkedMADDPGTrainer, + trainer_fn=maddpg.MADDPGNetworkedTrainer, connection_spec=fully_connected_network_spec, shared_weights=False, ).build() diff --git a/examples/debugging_envs/run_state_based_feedforward_maddpg.py b/examples/debugging_envs/run_state_based_feedforward_maddpg.py index c127de397..5728508ef 100644 --- a/examples/debugging_envs/run_state_based_feedforward_maddpg.py +++ b/examples/debugging_envs/run_state_based_feedforward_maddpg.py @@ -184,7 +184,7 @@ def main(_: Any) -> None: critic_optimizer=snt.optimizers.Adam(learning_rate=1e-4), checkpoint_subpath=checkpoint_dir, max_gradient_norm=40.0, - trainer_fn=maddpg.StateBasedMADDPGTrainer, + trainer_fn=maddpg.MADDPGStateBasedTrainer, shared_weights=False, ).build() diff --git a/examples/debugging_envs/run_state_based_recurrent_maddpg.py b/examples/debugging_envs/run_state_based_recurrent_maddpg.py index 4237dacd6..e987f0c81 100644 --- a/examples/debugging_envs/run_state_based_recurrent_maddpg.py +++ b/examples/debugging_envs/run_state_based_recurrent_maddpg.py @@ -31,7 +31,7 @@ from mava.components.tf.architectures import StateBasedQValueCritic from mava.systems.tf import maddpg from mava.systems.tf.maddpg.execution import MADDPGRecurrentExecutor -from mava.systems.tf.maddpg.training import StateBasedRecurrentMADDPGTrainer +from mava.systems.tf.maddpg.training import MADDPGStateBasedRecurrentTrainer from mava.utils import lp_utils from mava.utils.environments import debugging_utils from mava.utils.loggers import logger_utils @@ -164,7 +164,7 @@ def main(_: Any) -> None: logger_factory=logger_factory, num_executors=2, architecture=StateBasedQValueCritic, - trainer_fn=StateBasedRecurrentMADDPGTrainer, + trainer_fn=MADDPGStateBasedRecurrentTrainer, executor_fn=MADDPGRecurrentExecutor, checkpoint_subpath=checkpoint_dir, shared_weights=False, diff --git a/examples/petting_zoo/run_decentralised_feedforward_mad4pg_continous.py b/examples/petting_zoo/run_decentralised_feedforward_mad4pg_continuous.py similarity index 100% rename from examples/petting_zoo/run_decentralised_feedforward_mad4pg_continous.py rename to examples/petting_zoo/run_decentralised_feedforward_mad4pg_continuous.py diff --git a/examples/petting_zoo/run_decentralised_feedforward_maddpg_continous.py b/examples/petting_zoo/run_decentralised_feedforward_maddpg_continuous.py similarity index 100% rename from examples/petting_zoo/run_decentralised_feedforward_maddpg_continous.py rename to examples/petting_zoo/run_decentralised_feedforward_maddpg_continuous.py diff --git a/examples/petting_zoo/run_decentralised_recurrent_maddpg.py b/examples/petting_zoo/run_decentralised_recurrent_maddpg.py index 2f6e7f046..2d3158be4 100644 --- a/examples/petting_zoo/run_decentralised_recurrent_maddpg.py +++ b/examples/petting_zoo/run_decentralised_recurrent_maddpg.py @@ -31,7 +31,7 @@ from mava import specs as mava_specs from mava.systems.tf import maddpg from mava.systems.tf.maddpg.execution import MADDPGRecurrentExecutor -from mava.systems.tf.maddpg.training import DecentralisedRecurrentMADDPGTrainer +from mava.systems.tf.maddpg.training import MADDPGDecentralisedRecurrentTrainer from mava.utils import lp_utils from mava.utils.environments import pettingzoo_utils from mava.utils.loggers import logger_utils @@ -163,7 +163,7 @@ def main(_: Any) -> None: network_factory=network_factory, logger_factory=logger_factory, num_executors=2, - trainer_fn=DecentralisedRecurrentMADDPGTrainer, + trainer_fn=MADDPGDecentralisedRecurrentTrainer, executor_fn=MADDPGRecurrentExecutor, policy_optimizer=snt.optimizers.Adam(learning_rate=1e-4), critic_optimizer=snt.optimizers.Adam(learning_rate=1e-4), diff --git a/mava/systems/tf/mad4pg/__init__.py b/mava/systems/tf/mad4pg/__init__.py index ab020dea6..e540edebb 100644 --- a/mava/systems/tf/mad4pg/__init__.py +++ b/mava/systems/tf/mad4pg/__init__.py @@ -20,8 +20,12 @@ ) from mava.systems.tf.mad4pg.system import MAD4PG from mava.systems.tf.mad4pg.training import ( - BaseMAD4PGTrainer, - CentralisedMAD4PGTrainer, - DecentralisedMAD4PGTrainer, - StateBasedMAD4PGTrainer, + MAD4PGBaseRecurrentTrainer, + MAD4PGBaseTrainer, + MAD4PGCentralisedRecurrentTrainer, + MAD4PGCentralisedTrainer, + MAD4PGDecentralisedRecurrentTrainer, + MAD4PGDecentralisedTrainer, + MAD4PGStateBasedRecurrentTrainer, + MAD4PGStateBasedTrainer, ) diff --git a/mava/systems/tf/mad4pg/builder.py b/mava/systems/tf/mad4pg/builder.py index 052b68b3c..4f2ded222 100644 --- a/mava/systems/tf/mad4pg/builder.py +++ b/mava/systems/tf/mad4pg/builder.py @@ -14,7 +14,7 @@ # limitations under the License. """MAD4PG system implementation.""" -from typing import Any, Dict, Type +from typing import Any, Dict, Type, Union from mava import core from mava.systems.tf import executors @@ -35,9 +35,10 @@ class MAD4PGBuilder(MADDPGBuilder): def __init__( self, config: MADDPGConfig, - trainer_fn: Type[ - training.BaseMAD4PGTrainer - ] = training.DecentralisedMAD4PGTrainer, + trainer_fn: Union[ + Type[training.MAD4PGBaseTrainer], + Type[training.MAD4PGBaseRecurrentTrainer], + ] = training.MAD4PGDecentralisedTrainer, executor_fn: Type[core.Executor] = executors.FeedForwardExecutor, extra_specs: Dict[str, Any] = {}, ): diff --git a/mava/systems/tf/mad4pg/system.py b/mava/systems/tf/mad4pg/system.py index 8df5e5447..d399f1355 100644 --- a/mava/systems/tf/mad4pg/system.py +++ b/mava/systems/tf/mad4pg/system.py @@ -47,9 +47,9 @@ def __init__( DecentralisedQValueActorCritic ] = DecentralisedQValueActorCritic, trainer_fn: Union[ - Type[training.BaseMAD4PGTrainer], - Type[training.BaseRecurrentMAD4PGTrainer], - ] = training.DecentralisedMAD4PGTrainer, + Type[training.MAD4PGBaseTrainer], + Type[training.MAD4PGBaseRecurrentTrainer], + ] = training.MAD4PGDecentralisedTrainer, executor_fn: Type[core.Executor] = MADDPGFeedForwardExecutor, num_executors: int = 1, num_caches: int = 0, diff --git a/mava/systems/tf/mad4pg/training.py b/mava/systems/tf/mad4pg/training.py index aa31b8362..0507ef564 100644 --- a/mava/systems/tf/mad4pg/training.py +++ b/mava/systems/tf/mad4pg/training.py @@ -26,19 +26,19 @@ from mava.components.tf.losses.sequence import recurrent_n_step_critic_loss from mava.systems.tf.maddpg.training import ( - BaseMADDPGTrainer, - BaseRecurrentMADDPGTrainer, - CentralisedMADDPGTrainer, - CentralisedRecurrentMADDPGTrainer, - DecentralisedMADDPGTrainer, - DecentralisedRecurrentMADDPGTrainer, - StateBasedMADDPGTrainer, - StateBasedRecurrentMADDPGTrainer, + MADDPGBaseRecurrentTrainer, + MADDPGBaseTrainer, + MADDPGCentralisedRecurrentTrainer, + MADDPGCentralisedTrainer, + MADDPGDecentralisedRecurrentTrainer, + MADDPGDecentralisedTrainer, + MADDPGStateBasedRecurrentTrainer, + MADDPGStateBasedTrainer, ) from mava.utils import training_utils as train_utils -class BaseMAD4PGTrainer(BaseMADDPGTrainer): +class MAD4PGBaseTrainer(MADDPGBaseTrainer): """MAD4PG trainer. This is the trainer component of a MAD4PG system. IE it takes a dataset as input and implements update functionality to learn from this dataset. @@ -189,7 +189,7 @@ def _forward(self, inputs: Any) -> None: self.tape = tape -class DecentralisedMAD4PGTrainer(BaseMAD4PGTrainer, DecentralisedMADDPGTrainer): +class MAD4PGDecentralisedTrainer(MAD4PGBaseTrainer, MADDPGDecentralisedTrainer): """MAD4PG trainer. This is the trainer component of a MAD4PG system. IE it takes a dataset as input and implements update functionality to learn from this dataset. @@ -267,7 +267,7 @@ def __init__( ) -class CentralisedMAD4PGTrainer(BaseMAD4PGTrainer, CentralisedMADDPGTrainer): +class MAD4PGCentralisedTrainer(MAD4PGBaseTrainer, MADDPGCentralisedTrainer): """MAD4PG trainer. This is the trainer component of a MAD4PG system. IE it takes a dataset as input and implements update functionality to learn from this dataset. @@ -345,7 +345,7 @@ def __init__( ) -class StateBasedMAD4PGTrainer(BaseMAD4PGTrainer, StateBasedMADDPGTrainer): +class MAD4PGStateBasedTrainer(MAD4PGBaseTrainer, MADDPGStateBasedTrainer): """MAD4PG trainer. This is the trainer component of a MAD4PG system. IE it takes a dataset as input and implements update functionality to learn from this dataset. @@ -423,7 +423,7 @@ def __init__( ) -class BaseRecurrentMAD4PGTrainer(BaseRecurrentMADDPGTrainer): +class MAD4PGBaseRecurrentTrainer(MADDPGBaseRecurrentTrainer): """MAD4PG trainer. This is the trainer component of a MAD4PG system. IE it takes a dataset as input and implements update functionality to learn from this dataset. @@ -636,8 +636,8 @@ def _forward(self, inputs: Any) -> None: self.tape = tape -class DecentralisedRecurrentMAD4PGTrainer( - BaseRecurrentMAD4PGTrainer, DecentralisedRecurrentMADDPGTrainer +class MAD4PGDecentralisedRecurrentTrainer( + MAD4PGBaseRecurrentTrainer, MADDPGDecentralisedRecurrentTrainer ): """MAD4PG trainer. This is the trainer component of a MAD4PG system. IE it takes a dataset as input @@ -718,8 +718,8 @@ def __init__( ) -class CentralisedRecurrentMAD4PGTrainer( - BaseRecurrentMAD4PGTrainer, CentralisedRecurrentMADDPGTrainer +class MAD4PGCentralisedRecurrentTrainer( + MAD4PGBaseRecurrentTrainer, MADDPGCentralisedRecurrentTrainer ): """MAD4PG trainer. This is the trainer component of a MAD4PG system. IE it takes a dataset as input @@ -800,8 +800,8 @@ def __init__( ) -class StateBasedRecurrentMAD4PGTrainer( - BaseRecurrentMAD4PGTrainer, StateBasedRecurrentMADDPGTrainer +class MAD4PGStateBasedRecurrentTrainer( + MAD4PGBaseRecurrentTrainer, MADDPGStateBasedRecurrentTrainer ): """MAD4PG trainer. This is the trainer component of a MAD4PG system. IE it takes a dataset as input diff --git a/mava/systems/tf/maddpg/__init__.py b/mava/systems/tf/maddpg/__init__.py index 50841f280..2f823085d 100644 --- a/mava/systems/tf/maddpg/__init__.py +++ b/mava/systems/tf/maddpg/__init__.py @@ -21,9 +21,13 @@ ) from mava.systems.tf.maddpg.system import MADDPG from mava.systems.tf.maddpg.training import ( - BaseMADDPGTrainer, - CentralisedMADDPGTrainer, - DecentralisedMADDPGTrainer, - NetworkedMADDPGTrainer, - StateBasedMADDPGTrainer, + MADDPGBaseRecurrentTrainer, + MADDPGBaseTrainer, + MADDPGCentralisedRecurrentTrainer, + MADDPGCentralisedTrainer, + MADDPGDecentralisedRecurrentTrainer, + MADDPGDecentralisedTrainer, + MADDPGNetworkedTrainer, + MADDPGStateBasedRecurrentTrainer, + MADDPGStateBasedTrainer, ) diff --git a/mava/systems/tf/maddpg/builder.py b/mava/systems/tf/maddpg/builder.py index a40bb92eb..1388d3ffa 100644 --- a/mava/systems/tf/maddpg/builder.py +++ b/mava/systems/tf/maddpg/builder.py @@ -105,9 +105,9 @@ def __init__( self, config: MADDPGConfig, trainer_fn: Union[ - Type[training.BaseMADDPGTrainer], - Type[training.BaseRecurrentMADDPGTrainer], - ] = training.DecentralisedMADDPGTrainer, + Type[training.MADDPGBaseTrainer], + Type[training.MADDPGBaseRecurrentTrainer], + ] = training.MADDPGDecentralisedTrainer, executor_fn: Type[core.Executor] = MADDPGFeedForwardExecutor, extra_specs: Dict[str, Any] = {}, ): diff --git a/mava/systems/tf/maddpg/system.py b/mava/systems/tf/maddpg/system.py index 95f2aea46..aa44538fe 100644 --- a/mava/systems/tf/maddpg/system.py +++ b/mava/systems/tf/maddpg/system.py @@ -57,9 +57,9 @@ def __init__( DecentralisedQValueActorCritic ] = DecentralisedQValueActorCritic, trainer_fn: Union[ - Type[training.BaseMADDPGTrainer], - Type[training.BaseRecurrentMADDPGTrainer], - ] = training.DecentralisedMADDPGTrainer, + Type[training.MADDPGBaseTrainer], + Type[training.MADDPGBaseRecurrentTrainer], + ] = training.MADDPGDecentralisedTrainer, executor_fn: Type[core.Executor] = MADDPGFeedForwardExecutor, num_executors: int = 1, num_caches: int = 0, diff --git a/mava/systems/tf/maddpg/training.py b/mava/systems/tf/maddpg/training.py index 9e46cebe0..58ef9c29e 100644 --- a/mava/systems/tf/maddpg/training.py +++ b/mava/systems/tf/maddpg/training.py @@ -37,7 +37,7 @@ from mava.utils import training_utils as train_utils -class BaseMADDPGTrainer(mava.Trainer): +class MADDPGBaseTrainer(mava.Trainer): """MADDPG trainer. This is the trainer component of a MADDPG system. IE it takes a dataset as input and implements update functionality to learn from this dataset. @@ -454,7 +454,7 @@ def get_variables(self, names: Sequence[str]) -> Dict[str, Dict[str, np.ndarray] return variables -class DecentralisedMADDPGTrainer(BaseMADDPGTrainer): +class MADDPGDecentralisedTrainer(MADDPGBaseTrainer): """MADDPG trainer. This is the trainer component of a MADDPG system. IE it takes a dataset as input and implements update functionality to learn from this dataset. @@ -532,7 +532,7 @@ def __init__( ) -class CentralisedMADDPGTrainer(BaseMADDPGTrainer): +class MADDPGCentralisedTrainer(MADDPGBaseTrainer): """MADDPG trainer. This is the trainer component of a MADDPG system. IE it takes a dataset as input and implements update functionality to learn from this dataset. @@ -647,7 +647,7 @@ def _get_dpg_feed( return dpg_a_t_feed -class NetworkedMADDPGTrainer(BaseMADDPGTrainer): +class MADDPGNetworkedTrainer(MADDPGBaseTrainer): """MADDPG trainer. This is the trainer component of a MADDPG system. IE it takes a dataset as input and implements update functionality to learn from this dataset. @@ -775,7 +775,7 @@ def _get_dpg_feed( return dpg_a_t_feed -class StateBasedMADDPGTrainer(BaseMADDPGTrainer): +class MADDPGStateBasedTrainer(MADDPGBaseTrainer): """MADDPG trainer. This is the trainer component of a MADDPG system. IE it takes a dataset as input and implements update functionality to learn from this dataset. @@ -890,7 +890,7 @@ def _get_dpg_feed( return dpg_a_t_feed -class BaseRecurrentMADDPGTrainer(mava.Trainer): +class MADDPGBaseRecurrentTrainer(mava.Trainer): """MADDPG trainer. This is the trainer component of a MADDPG system. IE it takes a dataset as input and implements update functionality to learn from this dataset. @@ -1395,7 +1395,7 @@ def get_variables(self, names: Sequence[str]) -> Dict[str, Dict[str, np.ndarray] return variables -class DecentralisedRecurrentMADDPGTrainer(BaseRecurrentMADDPGTrainer): +class MADDPGDecentralisedRecurrentTrainer(MADDPGBaseRecurrentTrainer): """MADDPG trainer. This is the trainer component of a MADDPG system. IE it takes a dataset as input and implements update functionality to learn from this dataset. @@ -1475,7 +1475,7 @@ def __init__( ) -class CentralisedRecurrentMADDPGTrainer(BaseRecurrentMADDPGTrainer): +class MADDPGCentralisedRecurrentTrainer(MADDPGBaseRecurrentTrainer): """MADDPG trainer. This is the trainer component of a MADDPG system. IE it takes a dataset as input and implements update functionality to learn from this dataset. @@ -1593,7 +1593,7 @@ def _get_dpg_feed( return dpg_actions_feed -class StateBasedRecurrentMADDPGTrainer(BaseRecurrentMADDPGTrainer): +class MADDPGStateBasedRecurrentTrainer(MADDPGBaseRecurrentTrainer): """MADDPG trainer. This is the trainer component of a MADDPG system. IE it takes a dataset as input and implements update functionality to learn from this dataset. From afb194a28ac0272f2531f1fa797843ace3a62c1c Mon Sep 17 00:00:00 2001 From: Kale-ab Date: Wed, 26 May 2021 10:11:14 +0200 Subject: [PATCH 094/188] fix: Re-added SMACEnvWrapper wrapper. --- mava/wrappers/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mava/wrappers/__init__.py b/mava/wrappers/__init__.py index 66657ed37..02a95a511 100644 --- a/mava/wrappers/__init__.py +++ b/mava/wrappers/__init__.py @@ -26,7 +26,7 @@ ) # TODO only import smac if the user needs it. -# from mava.wrappers.smac import SMACEnvWrapper +from mava.wrappers.smac import SMACEnvWrapper from mava.wrappers.system_trainer_statistics import ( DetailedTrainerStatistics, DetailedTrainerStatisticsWithEpsilon, From e8693b8eb37d468ab4311fcd90c77f0b533a2958 Mon Sep 17 00:00:00 2001 From: Kale-ab Date: Wed, 26 May 2021 10:23:47 +0200 Subject: [PATCH 095/188] feat: Catch exceptions thrown by rendering. --- mava/wrappers/environment_loop_wrappers.py | 17 +++++++++++------ 1 file changed, 11 insertions(+), 6 deletions(-) diff --git a/mava/wrappers/environment_loop_wrappers.py b/mava/wrappers/environment_loop_wrappers.py index 6777e90ba..3c6e6ed85 100644 --- a/mava/wrappers/environment_loop_wrappers.py +++ b/mava/wrappers/environment_loop_wrappers.py @@ -261,12 +261,17 @@ def step(self, action: Dict[str, np.ndarray]) -> dm_env.TimeStep: return timestep def _retrieve_render(self) -> np.ndarray: - if self._format == "video": - render = self._environment.render(mode="rgb_array") - elif self._format == "gif": - render = np.transpose( - self._environment.render(mode="rgb_array"), axes=(1, 0, 2) - ) + render = None + try: + if self._format == "video": + render = self._environment.render(mode="rgb_array") + elif self._format == "gif": + render = np.transpose( + self._environment.render(mode="rgb_array"), axes=(1, 0, 2) + ) + except Exception as ex: + print(f"Render frames exception: {ex}") + pass return render def _append_frame(self) -> None: From 14eeba1e50117eb85119e8f15dc54e10383a6b12 Mon Sep 17 00:00:00 2001 From: St John Grimbly Date: Wed, 26 May 2021 13:08:59 +0200 Subject: [PATCH 096/188] fix: restore new clipping method. --- mava/systems/tf/qmix/system.py | 1 - mava/systems/tf/qmix/training.py | 16 +++------------- mava/systems/tf/vdn/training.py | 6 ------ 3 files changed, 3 insertions(+), 20 deletions(-) diff --git a/mava/systems/tf/qmix/system.py b/mava/systems/tf/qmix/system.py index 2a0df9a1b..3a98cd1bc 100644 --- a/mava/systems/tf/qmix/system.py +++ b/mava/systems/tf/qmix/system.py @@ -157,7 +157,6 @@ def __init__( n_step=n_step, sequence_length=sequence_length, period=period, - clipping=clipping, discount=discount, optimizer=optimizer, target_update_period=target_update_period, diff --git a/mava/systems/tf/qmix/training.py b/mava/systems/tf/qmix/training.py index c0c1c45e1..888eea66a 100644 --- a/mava/systems/tf/qmix/training.py +++ b/mava/systems/tf/qmix/training.py @@ -56,7 +56,6 @@ def __init__( shared_weights: bool, exploration_scheduler: LinearExplorationScheduler, communication_module: Optional[BaseCommunicationModule] = None, - clipping: bool = True, max_gradient_norm: float = None, counter: counting.Counter = None, fingerprint: bool = False, @@ -81,7 +80,6 @@ def __init__( shared_weights=shared_weights, exploration_scheduler=exploration_scheduler, communication_module=communication_module, - clipping=clipping, max_gradient_norm=max_gradient_norm, counter=counter, fingerprint=fingerprint, @@ -264,8 +262,7 @@ def _backward(self) -> None: # Update agent networks variables = [*self._q_networks[agent_key].trainable_variables] gradients = self.tape.gradient(self.loss, variables) - if self._clipping: - gradients = tf.clip_by_global_norm(gradients, 40.0)[0] + gradients = tf.clip_by_global_norm(gradients, self._max_gradient_norm)[0] self._optimizers[agent_key].apply(gradients, variables) # Update mixing network @@ -274,16 +271,9 @@ def _backward(self) -> None: # *self._mixing_network._hypernetworks.trainable_variables, ] gradients = self.tape.gradient(self.loss, variables) - if self._clipping: - gradients = tf.clip_by_global_norm(gradients, 40.0)[0] - self._optimizer.apply(gradients, variables) - trainable_variables += self._mixing_network.trainable_variables - - # Compute gradients. - gradients = self.tape.gradient(self.loss, trainable_variables) - - # Clip gradients. gradients = tf.clip_by_global_norm(gradients, self._max_gradient_norm)[0] + self._optimizer.apply(gradients, variables) + variables += self._mixing_network.trainable_variable train_utils.safe_del(self, "tape") diff --git a/mava/systems/tf/vdn/training.py b/mava/systems/tf/vdn/training.py index 47a2db3db..ce6098fb9 100644 --- a/mava/systems/tf/vdn/training.py +++ b/mava/systems/tf/vdn/training.py @@ -51,7 +51,6 @@ def __init__( shared_weights: bool, exploration_scheduler: LinearExplorationScheduler, communication_module: Optional[BaseCommunicationModule] = None, - clipping: bool = True, max_gradient_norm: float = None, counter: counting.Counter = None, fingerprint: bool = False, @@ -75,7 +74,6 @@ def __init__( shared_weights=shared_weights, exploration_scheduler=exploration_scheduler, communication_module=communication_module, - clipping=clipping, max_gradient_norm=max_gradient_norm, counter=counter, fingerprint=fingerprint, @@ -180,10 +178,6 @@ def _backward(self) -> None: # Compute gradients. gradients = self.tape.gradient(self.loss, trainable_variables) - # Maybe clip gradients. - if self._clipping: - gradients = tf.clip_by_global_norm(gradients, 40.0)[0] - # Clip gradients. gradients = tf.clip_by_global_norm(gradients, self._max_gradient_norm)[0] From 6c167d81a1024d2b88b7a93fb02443e844b3e2ca Mon Sep 17 00:00:00 2001 From: Dries Date: Wed, 26 May 2021 15:54:13 +0200 Subject: [PATCH 097/188] fix: Epsilon value in MADQNFeedForwardExecutor. --- examples/smac/run_feedforward_madqn.py | 168 +++++++++++++++++++++++++ mava/systems/tf/madqn/execution.py | 5 +- 2 files changed, 172 insertions(+), 1 deletion(-) create mode 100644 examples/smac/run_feedforward_madqn.py diff --git a/examples/smac/run_feedforward_madqn.py b/examples/smac/run_feedforward_madqn.py new file mode 100644 index 000000000..2c061fb5d --- /dev/null +++ b/examples/smac/run_feedforward_madqn.py @@ -0,0 +1,168 @@ +# python3 +# Copyright 2021 [...placeholder...]. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Example running recurrent MADQN on multi-agent Starcraft 2 (SMAC) environment.""" + +import functools +from datetime import datetime +from typing import Any, Dict, Mapping, Optional, Sequence, Union + +import launchpad as lp +import sonnet as snt +import tensorflow as tf +from absl import app, flags +from acme import types +from acme.tf import networks +from launchpad.nodes.python.local_multi_processing import PythonProcess + +from mava import specs as mava_specs +from mava.components.tf.modules.exploration import LinearExplorationScheduler +from mava.components.tf.networks import epsilon_greedy_action_selector +from mava.systems.tf import madqn +from mava.utils import lp_utils +from mava.utils.environments import smac_utils +from mava.utils.loggers import logger_utils + +FLAGS = flags.FLAGS +flags.DEFINE_string( + "map_name", + "3m", + "Starcraft 2 micromanagement map name (str).", +) + +flags.DEFINE_string( + "mava_id", + str(datetime.now()), + "Experiment identifier that can be used to continue experiments.", +) +flags.DEFINE_string("base_dir", "~/mava/", "Base dir to store experiments.") + + +def make_networks( + environment_spec: mava_specs.MAEnvironmentSpec, + q_networks_layer_sizes: Union[Dict[str, Sequence], Sequence] = ( + 512, + 512, + 256, + ), + shared_weights: bool = True, +) -> Mapping[str, types.TensorTransformation]: + """Creates networks used by the agents.""" + + specs = environment_spec.get_agent_specs() + + # Create agent_type specs + if shared_weights: + type_specs = {key.split("_")[0]: specs[key] for key in specs.keys()} + specs = type_specs + + if isinstance(q_networks_layer_sizes, Sequence): + q_networks_layer_sizes = {key: q_networks_layer_sizes for key in specs.keys()} + + def action_selector_fn( + q_values: types.NestedTensor, + legal_actions: types.NestedTensor, + epsilon: Optional[tf.Variable] = None, + ) -> types.NestedTensor: + return epsilon_greedy_action_selector( + action_values=q_values, legal_actions_mask=legal_actions, epsilon=epsilon + ) + + q_networks = {} + action_selectors = {} + for key in specs.keys(): + + # Get total number of action dimensions from action spec. + num_dimensions = specs[key].actions.num_values + + # Create the policy network. + q_network = snt.Sequential( + [ + networks.LayerNormMLP( + q_networks_layer_sizes[key], activate_final=False + ), + networks.NearZeroInitializedLinear(num_dimensions), + ] + ) + + # epsilon greedy action selector + action_selector = action_selector_fn + + q_networks[key] = q_network + action_selectors[key] = action_selector + + return { + "q_networks": q_networks, + "action_selectors": action_selectors, + } + + +def main(_: Any) -> None: + + # environment + environment_factory = functools.partial( + smac_utils.make_environment, map_name=FLAGS.map_name + ) + + # networks + network_factory = lp_utils.partial_kwargs(make_networks) + + # Checkpointer appends "Checkpoints" to checkpoint_dir + checkpoint_dir = f"{FLAGS.base_dir}/{FLAGS.mava_id}" + + # loggers + log_every = 10 + logger_factory = functools.partial( + logger_utils.make_logger, + directory=FLAGS.base_dir, + to_terminal=True, + to_tensorboard=True, + time_stamp=FLAGS.mava_id, + time_delta=log_every, + ) + + # distributed program + program = madqn.MADQN( + environment_factory=environment_factory, + network_factory=network_factory, + logger_factory=logger_factory, + num_executors=2, + exploration_scheduler_fn=LinearExplorationScheduler, + epsilon_min=0.05, + epsilon_decay=5e-4, + optimizer=snt.optimizers.Adam(learning_rate=1e-4), + checkpoint_subpath=checkpoint_dir, + n_step=1, + batch_size=32, + ).build() + + # launch + gpu_id = -1 + env_vars = {"CUDA_VISIBLE_DEVICES": str(gpu_id)} + local_resources = { + "trainer": [], + "evaluator": PythonProcess(env=env_vars), + "executor": PythonProcess(env=env_vars), + } + lp.launch( + program, + lp.LaunchType.LOCAL_MULTI_PROCESSING, + terminal="current_terminal", + local_resources=local_resources, + ) + + +if __name__ == "__main__": + app.run(main) diff --git a/mava/systems/tf/madqn/execution.py b/mava/systems/tf/madqn/execution.py index 16c0f665d..3f072655f 100644 --- a/mava/systems/tf/madqn/execution.py +++ b/mava/systems/tf/madqn/execution.py @@ -154,7 +154,8 @@ def select_actions( if not self._evaluator: epsilon = self._trainer.get_epsilon() else: - epsilon = 0.0 + # Note (dries): For some reason 0 epsilon breaks on StarCraft + epsilon = 1e-10 epsilon = tf.convert_to_tensor(epsilon) @@ -176,6 +177,8 @@ def select_actions( actions[agent] = tf2_utils.to_numpy_squeeze(action) + print("actions: ", actions, ". Evaluator: ", self._evaluator) + # Return a numpy array with squeezed out batch dimension. return actions From 6822b0d84b050dc0bd0bb0d845dcc75d652422da Mon Sep 17 00:00:00 2001 From: Dries Date: Wed, 26 May 2021 15:59:47 +0200 Subject: [PATCH 098/188] fix: Remove print. --- mava/systems/tf/madqn/execution.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/mava/systems/tf/madqn/execution.py b/mava/systems/tf/madqn/execution.py index 3f072655f..5de845b59 100644 --- a/mava/systems/tf/madqn/execution.py +++ b/mava/systems/tf/madqn/execution.py @@ -177,8 +177,6 @@ def select_actions( actions[agent] = tf2_utils.to_numpy_squeeze(action) - print("actions: ", actions, ". Evaluator: ", self._evaluator) - # Return a numpy array with squeezed out batch dimension. return actions From 17d2d9d4bf013b6a8f080da7a6cd757462a46047 Mon Sep 17 00:00:00 2001 From: Dries Date: Wed, 26 May 2021 16:03:25 +0200 Subject: [PATCH 099/188] fix: Small fix. --- examples/README.md | 4 ++++ mava/systems/tf/madqn/execution.py | 2 +- 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/examples/README.md b/examples/README.md index 553ee3f7c..6ecb9c8d6 100644 --- a/examples/README.md +++ b/examples/README.md @@ -73,3 +73,7 @@ We also include a number of systems running on discrete action space environment a QMIX system on the two step custom environment. - [VDN](petting_zoo/run_feedforward_vdn.py): a VDN system on the two step custom environment. +- [MA-DQN (feedforward)](smac/run_feedforward_madqn.py): + an MA-DQN system running on the StarCraft 3m environment. +- [MA-DQN (recurrent)](smac/run_recurrent_madqn.py): + an MA-DQN system running on the StarCraft 3m environment. diff --git a/mava/systems/tf/madqn/execution.py b/mava/systems/tf/madqn/execution.py index 5de845b59..1e7fed0da 100644 --- a/mava/systems/tf/madqn/execution.py +++ b/mava/systems/tf/madqn/execution.py @@ -154,7 +154,7 @@ def select_actions( if not self._evaluator: epsilon = self._trainer.get_epsilon() else: - # Note (dries): For some reason 0 epsilon breaks on StarCraft + # Note (dries): For some reason 0 epsilon breaks on StarCraft. epsilon = 1e-10 epsilon = tf.convert_to_tensor(epsilon) From f64ccef87749c4e70bba6305666e0549498a4179 Mon Sep 17 00:00:00 2001 From: Kale-ab Date: Wed, 26 May 2021 23:20:06 +0200 Subject: [PATCH 100/188] fix: New version of acme breaks adders. Hardcoded acme version for now. --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index d24212070..8bc32a08a 100644 --- a/setup.py +++ b/setup.py @@ -91,7 +91,7 @@ keywords="multi-agent reinforcement-learning python machine learning", packages=find_packages(), install_requires=[ - "dm-acme", + "dm-acme==0.2.0", "absl-py", "dm_env", "dm-tree", From 071742c8aa70d2c71bb496dfef803c82d4dd4782 Mon Sep 17 00:00:00 2001 From: Kale-ab Date: Thu, 27 May 2021 09:21:18 +0200 Subject: [PATCH 101/188] feat: Example custom network spec. --- ...ntralised_feedforward_maddpg_continuous.py | 2 +- ...networked_feedforward_maddpg_continuous.py | 203 ++++++++++++++++++ 2 files changed, 204 insertions(+), 1 deletion(-) create mode 100644 examples/petting_zoo/run_networked_feedforward_maddpg_continuous.py diff --git a/examples/petting_zoo/run_decentralised_feedforward_maddpg_continuous.py b/examples/petting_zoo/run_decentralised_feedforward_maddpg_continuous.py index 23435eb9b..84a5f76e3 100644 --- a/examples/petting_zoo/run_decentralised_feedforward_maddpg_continuous.py +++ b/examples/petting_zoo/run_decentralised_feedforward_maddpg_continuous.py @@ -13,7 +13,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -"""Example running continous MADDPG on pettinzoo MPE environments.""" +"""Example running continous MADDPG on pettinzoo SISL environments.""" import functools from datetime import datetime diff --git a/examples/petting_zoo/run_networked_feedforward_maddpg_continuous.py b/examples/petting_zoo/run_networked_feedforward_maddpg_continuous.py new file mode 100644 index 000000000..1f623e7df --- /dev/null +++ b/examples/petting_zoo/run_networked_feedforward_maddpg_continuous.py @@ -0,0 +1,203 @@ +# python3 +# Copyright 2021 [...placeholder...]. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Example running continous,networked MADDPG on pettinzoo SISL environments.""" + +import functools +from datetime import datetime +from typing import Any, Dict, List, Mapping, Sequence, Union + +import launchpad as lp +import numpy as np +import sonnet as snt +from absl import app, flags +from acme import types +from acme.tf import networks +from acme.tf import utils as tf2_utils +from launchpad.nodes.python.local_multi_processing import PythonProcess + +from mava import specs as mava_specs +from mava.components.tf.architectures.networked import NetworkedQValueCritic +from mava.systems.tf import maddpg +from mava.utils import lp_utils +from mava.utils.environments import pettingzoo_utils +from mava.utils.loggers import logger_utils + +FLAGS = flags.FLAGS + +flags.DEFINE_string( + "env_class", + "sisl", + "Pettingzoo environment class, e.g. atari (str).", +) + +flags.DEFINE_string( + "env_name", + "multiwalker_v7", + "Pettingzoo environment name, e.g. pong (str).", +) +flags.DEFINE_string( + "mava_id", + str(datetime.now()), + "Experiment identifier that can be used to continue experiments.", +) +flags.DEFINE_string("base_dir", "~/mava/", "Base dir to store experiments.") + + +def make_networks( + environment_spec: mava_specs.MAEnvironmentSpec, + policy_networks_layer_sizes: Union[Dict[str, Sequence], Sequence] = ( + 256, + 256, + 256, + ), + critic_networks_layer_sizes: Union[Dict[str, Sequence], Sequence] = (512, 512, 256), + shared_weights: bool = True, + sigma: float = 0.3, +) -> Mapping[str, types.TensorTransformation]: + """Creates networks used by the agents.""" + specs = environment_spec.get_agent_specs() + + # Create agent_type specs + if shared_weights: + type_specs = {key.split("_")[0]: specs[key] for key in specs.keys()} + specs = type_specs + + if isinstance(policy_networks_layer_sizes, Sequence): + policy_networks_layer_sizes = { + key: policy_networks_layer_sizes for key in specs.keys() + } + if isinstance(critic_networks_layer_sizes, Sequence): + critic_networks_layer_sizes = { + key: critic_networks_layer_sizes for key in specs.keys() + } + + observation_networks = {} + policy_networks = {} + critic_networks = {} + for key in specs.keys(): + + # Get total number of action dimensions from action spec. + num_dimensions = np.prod(specs[key].actions.shape, dtype=int) + + # Create the shared observation network; here simply a state-less operation. + observation_network = tf2_utils.to_sonnet_module(tf2_utils.batch_concat) + + # Create the policy network. + policy_network = snt.Sequential( + [ + observation_network, + networks.LayerNormMLP( + policy_networks_layer_sizes[key], activate_final=True + ), + networks.NearZeroInitializedLinear(num_dimensions), + networks.TanhToSpec(specs[key].actions), + networks.ClippedGaussian(sigma), + networks.ClipToSpec(specs[key].actions), + ] + ) + + # Create the critic network. + critic_network = snt.Sequential( + [ + # The multiplexer concatenates the observations/actions. + networks.CriticMultiplexer(), + networks.LayerNormMLP( + critic_networks_layer_sizes[key], activate_final=False + ), + snt.Linear(1), + ] + ) + observation_networks[key] = observation_network + policy_networks[key] = policy_network + critic_networks[key] = critic_network + + return { + "policies": policy_networks, + "critics": critic_networks, + "observations": observation_networks, + } + + +def custom_connected_network_spec( + agents_by_type: Dict[str, List[str]] +) -> Dict[str, List[str]]: + del agents_by_type + + return { + "walker_0": ["walker_0", "walker_1"], + "walker_1": ["walker_1", "walker_0", "walker_2"], + "walker_2": ["walker_2", "walker_1"], + } + + +def main(_: Any) -> None: + + environment_factory = functools.partial( + pettingzoo_utils.make_environment, + env_class=FLAGS.env_class, + env_name=FLAGS.env_name, + remove_on_fall=False, + ) + + network_factory = lp_utils.partial_kwargs(make_networks) + + # Checkpointer appends "Checkpoints" to checkpoint_dir + checkpoint_dir = f"{FLAGS.base_dir}/{FLAGS.mava_id}" + + # loggers + log_every = 10 + logger_factory = functools.partial( + logger_utils.make_logger, + directory=FLAGS.base_dir, + to_terminal=True, + to_tensorboard=True, + time_stamp=FLAGS.mava_id, + time_delta=log_every, + ) + + program = maddpg.MADDPG( + environment_factory=environment_factory, + network_factory=network_factory, + logger_factory=logger_factory, + num_executors=2, + policy_optimizer=snt.optimizers.Adam(learning_rate=1e-4), + critic_optimizer=snt.optimizers.Adam(learning_rate=1e-4), + checkpoint_subpath=checkpoint_dir, + connection_spec=custom_connected_network_spec, + shared_weights=False, + architecture=NetworkedQValueCritic, + trainer_fn=maddpg.MADDPGNetworkedTrainer, + ).build() + + # Launch gpu config - let trainer use gpu. + gpu_id = -1 + env_vars = {"CUDA_VISIBLE_DEVICES": str(gpu_id)} + local_resources = { + "trainer": [], + "evaluator": PythonProcess(env=env_vars), + "executor": PythonProcess(env=env_vars), + } + + lp.launch( + program, + lp.LaunchType.LOCAL_MULTI_PROCESSING, + terminal="current_terminal", + local_resources=local_resources, + ) + + +if __name__ == "__main__": + app.run(main) From a0fd62965d6ff2accecb45a4b12c0c2e74414bf7 Mon Sep 17 00:00:00 2001 From: Dries Date: Thu, 27 May 2021 09:38:29 +0200 Subject: [PATCH 102/188] fix: Fix _get_dpg_feed inside MADDPGNetworkedTrainer. --- mava/systems/tf/maddpg/training.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/mava/systems/tf/maddpg/training.py b/mava/systems/tf/maddpg/training.py index 58ef9c29e..c05ada9f7 100644 --- a/mava/systems/tf/maddpg/training.py +++ b/mava/systems/tf/maddpg/training.py @@ -763,15 +763,16 @@ def _get_dpg_feed( dpg_a_t: np.ndarray, agent: str, ) -> tf.Tensor: - # Centralised and StateBased DPG - # Note (dries): Copy has to be made because the input - # variables cannot be changed. + # Networked based tree.map_structure(tf.stop_gradient, a_t) dpg_a_t_feed = copy.copy(a_t) dpg_a_t_feed[agent] = dpg_a_t - dpg_a_t_feed = tf.squeeze( - tf.stack([dpg_a_t_feed[agent] for agent in self._agents], 1) - ) + + connections = self._connection_spec[agent] + a_t_vals = [] + for connected_agent in connections: + a_t_vals.append(dpg_a_t_feed[connected_agent]) + dpg_a_t_feed = tf.squeeze(tf.stack(a_t_vals, 1)) return dpg_a_t_feed From 5123782e94c88f187cb641d3e36aee569f2984ed Mon Sep 17 00:00:00 2001 From: Dries Date: Thu, 27 May 2021 09:41:05 +0200 Subject: [PATCH 103/188] fix: Fix _get_dpg_feed inside MADDPGNetworkedTrainer. --- examples/README.md | 3 ++- ...ontinuous.py => run_networked_feedforward_maddpg_sparse.py} | 0 2 files changed, 2 insertions(+), 1 deletion(-) rename examples/petting_zoo/{run_networked_feedforward_maddpg_continuous.py => run_networked_feedforward_maddpg_sparse.py} (100%) diff --git a/examples/README.md b/examples/README.md index 6ecb9c8d6..909c8fb6b 100644 --- a/examples/README.md +++ b/examples/README.md @@ -41,7 +41,8 @@ We include a number of systems running on continuous control tasks. an MA-DDPG system on the continuous action space multiwalker_v7 PettingZoo environment. - [MA-DDPG (decentralised, feedforward)](petting_zoo/run_feedforward_maddpg_record_video.py): an MA-DDPG system (with video wrapping) on the continuous action space multiwalker_v7 PettingZoo environment. - +- [MA-DDPG (networked, feedforward)](petting_zoo/run_networked_feedforward_maddpg_sparse.py): + an MA-DDPG system oon the continuous action space multiwalker_v7 PettingZoo environm [petting_zoo]: https://github.com/PettingZoo-Team/PettingZoo ## Discrete control diff --git a/examples/petting_zoo/run_networked_feedforward_maddpg_continuous.py b/examples/petting_zoo/run_networked_feedforward_maddpg_sparse.py similarity index 100% rename from examples/petting_zoo/run_networked_feedforward_maddpg_continuous.py rename to examples/petting_zoo/run_networked_feedforward_maddpg_sparse.py From 8af2d45398fb164ea4285250e134b9eddb5af5ad Mon Sep 17 00:00:00 2001 From: Dries Date: Thu, 27 May 2021 09:47:08 +0200 Subject: [PATCH 104/188] feature: Add assert to test if networked architecture is setup correctly. --- mava/systems/tf/maddpg/training.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/mava/systems/tf/maddpg/training.py b/mava/systems/tf/maddpg/training.py index c05ada9f7..139fba243 100644 --- a/mava/systems/tf/maddpg/training.py +++ b/mava/systems/tf/maddpg/training.py @@ -745,6 +745,9 @@ def _get_critic_feed( a_tm1_vals = [] a_t_vals = [] + # The agent has to be in the connections for training to work + assert agent in connections + for connected_agent in connections: o_tm1_vals.append(o_tm1_trans[connected_agent]) o_t_vals.append(o_t_trans[connected_agent]) @@ -769,6 +772,10 @@ def _get_dpg_feed( dpg_a_t_feed[agent] = dpg_a_t connections = self._connection_spec[agent] + + # The agent has to be in the connections for training to work + assert agent in connections + a_t_vals = [] for connected_agent in connections: a_t_vals.append(dpg_a_t_feed[connected_agent]) From fba8c91621f9d89462427ef5d894d4be82e3131a Mon Sep 17 00:00:00 2001 From: St John Grimbly Date: Thu, 27 May 2021 10:48:33 +0200 Subject: [PATCH 105/188] fix: change trainer from VDN to QMIX --- mava/systems/tf/qmixV2/training.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/mava/systems/tf/qmixV2/training.py b/mava/systems/tf/qmixV2/training.py index c54e7017b..7616b80d2 100644 --- a/mava/systems/tf/qmixV2/training.py +++ b/mava/systems/tf/qmixV2/training.py @@ -299,11 +299,11 @@ def _forward(self, inputs: Any) -> None: q_tot_mixed = self._mixer(q_acts, s_tm1) # [B, 1, 1] q_tot_target_mixed = self._target_mixer(q_targets, s_t) # [B, 1, 1] - q_tot_mixed = tf.reduce_sum(q_acts, axis=1) # [B, 1, 1] - q_tot_target_mixed = tf.reduce_sum(q_targets, axis=1) # [B, 1, 1] + # q_tot_mixed = tf.reduce_sum(q_acts, axis=1) # [B, 1, 1] + # q_tot_target_mixed = tf.reduce_sum(q_targets, axis=1) # [B, 1, 1] - # q_tot_mixed = tf.reshape(q_tot_mixed, (-1,)) - # q_tot_target_mixed = tf.reshape(q_tot_target_mixed, (-1,)) + q_tot_mixed = tf.reshape(q_tot_mixed, (-1,)) + q_tot_target_mixed = tf.reshape(q_tot_target_mixed, (-1,)) # Calculate Q loss. targets = rewards + pcont * q_tot_target_mixed From 2cb1346cd2116aba6ad894fcd9a622ed60d2a782 Mon Sep 17 00:00:00 2001 From: Dries Date: Mon, 31 May 2021 19:26:39 +0200 Subject: [PATCH 106/188] feature: Add robocup docker setup to Mava code. --- Dockerfile.robocup | 54 ++ Makefile | 4 + examples/robocup/run_robocup.py | 98 +++ .../environments/RoboCup_env/RoboCup2D_env.py | 445 +++++++++++ mava/utils/environments/RoboCup_env/helper.py | 51 ++ .../robocup_agents/custom_executor.py | 46 ++ .../RoboCup_env/robocup_agents/fixed_agent.py | 145 ++++ .../RoboCup_env/robocup_agents/nn_agent.py | 279 +++++++ .../environments/RoboCup_env/robocup_base.py | 107 +++ .../RoboCup_env/robocup_utils/__init__.py | 0 .../RoboCup_env/robocup_utils/agent.py | 306 ++++++++ .../robocup_utils/extract_policy.py | 53 ++ .../RoboCup_env/robocup_utils/game_object.py | 193 +++++ .../RoboCup_env/robocup_utils/handler.py | 706 +++++++++++++++++ .../RoboCup_env/robocup_utils/license.txt | 19 + .../robocup_utils/message_parser.py | 164 ++++ .../robocup_utils/player_world_model.py | 697 +++++++++++++++++ .../RoboCup_env/robocup_utils/sock.py | 46 ++ .../robocup_utils/sp_exceptions.py | 36 + .../RoboCup_env/robocup_utils/trainer.py | 379 ++++++++++ .../robocup_utils/trainer_world_model.py | 434 +++++++++++ .../robocup_utils/util_functions.py | 715 ++++++++++++++++++ 22 files changed, 4977 insertions(+) create mode 100755 Dockerfile.robocup create mode 100644 examples/robocup/run_robocup.py create mode 100755 mava/utils/environments/RoboCup_env/RoboCup2D_env.py create mode 100644 mava/utils/environments/RoboCup_env/helper.py create mode 100644 mava/utils/environments/RoboCup_env/robocup_agents/custom_executor.py create mode 100755 mava/utils/environments/RoboCup_env/robocup_agents/fixed_agent.py create mode 100755 mava/utils/environments/RoboCup_env/robocup_agents/nn_agent.py create mode 100644 mava/utils/environments/RoboCup_env/robocup_base.py create mode 100755 mava/utils/environments/RoboCup_env/robocup_utils/__init__.py create mode 100755 mava/utils/environments/RoboCup_env/robocup_utils/agent.py create mode 100644 mava/utils/environments/RoboCup_env/robocup_utils/extract_policy.py create mode 100755 mava/utils/environments/RoboCup_env/robocup_utils/game_object.py create mode 100755 mava/utils/environments/RoboCup_env/robocup_utils/handler.py create mode 100755 mava/utils/environments/RoboCup_env/robocup_utils/license.txt create mode 100755 mava/utils/environments/RoboCup_env/robocup_utils/message_parser.py create mode 100755 mava/utils/environments/RoboCup_env/robocup_utils/player_world_model.py create mode 100755 mava/utils/environments/RoboCup_env/robocup_utils/sock.py create mode 100755 mava/utils/environments/RoboCup_env/robocup_utils/sp_exceptions.py create mode 100755 mava/utils/environments/RoboCup_env/robocup_utils/trainer.py create mode 100755 mava/utils/environments/RoboCup_env/robocup_utils/trainer_world_model.py create mode 100644 mava/utils/environments/RoboCup_env/robocup_utils/util_functions.py diff --git a/Dockerfile.robocup b/Dockerfile.robocup new file mode 100755 index 000000000..78b49a3c3 --- /dev/null +++ b/Dockerfile.robocup @@ -0,0 +1,54 @@ +FROM mava:latest + +RUN apt-get update \ + && apt-get install -y --no-install-recommends \ + apt-utils \ + build-essential \ + curl \ + xvfb \ + ffmpeg \ + xorg-dev \ + libsdl2-dev \ + swig \ + cmake \ + python-opengl + +# RoboCup +ARG VERSION=16.0.0 + +# fetch repo / ppa packages, etc +RUN apt-get -y update --fix-missing +# Install package, that hangs the operation, separately +RUN DEBIAN_FRONTEND=noninteractive apt install -y tzdata + +RUN apt update && \ + apt -y install autoconf bison clang flex libboost-dev libboost-all-dev libc6-dev make wget + +RUN apt -y install build-essential libboost-all-dev qt5-default libfontconfig1-dev libaudio-dev libxt-dev libglib2.0-dev libxi-dev libxrender-dev libboost-all-dev + +RUN wget https://github.com/rcsoccersim/rcssserver/archive/rcssserver-$VERSION.tar.gz && \ + tar xfz rcssserver-$VERSION.tar.gz && \ + cd rcssserver-rcssserver-$VERSION && \ + ./bootstrap && \ + ./configure && \ + make && \ + make install && \ + ldconfig + +RUN wget https://github.com/rcsoccersim/rcssmonitor/archive/rcssmonitor-$VERSION.tar.gz && \ + tar xfz rcssmonitor-$VERSION.tar.gz && \ + cd rcssmonitor-rcssmonitor-$VERSION && \ + ./bootstrap && \ + ./configure && \ + make && \ + make install && \ + ldconfig + +RUN ldconfig && \ + apt update && \ + apt install -y libboost-filesystem1.65.1 libboost-system1.65.1 libboost-program-options-dev tmux + +RUN apt-get install -y libqt5widgets5 +# RUN pip install hydra-core + +# ENV PYTHONPATH '/home' diff --git a/Makefile b/Makefile index b56c8a67a..2d87d5960 100755 --- a/Makefile +++ b/Makefile @@ -68,6 +68,10 @@ record: build: docker build --tag $(IMAGE) . +build_robocup: + docker build --tag $(IMAGE) -f ./Dockerfile.robocup . + # sudo rm -r ./*.rcl ./*.rcg ./rcssserver-16.0.0* ./rcssserver-rcssserver-16.0.0 ./rcssmonitor* + push: docker login -docker push $(IMAGE) diff --git a/examples/robocup/run_robocup.py b/examples/robocup/run_robocup.py new file mode 100644 index 000000000..16c78324c --- /dev/null +++ b/examples/robocup/run_robocup.py @@ -0,0 +1,98 @@ +# type: ignore +"""Run recurrent MA-D4PG on RoboCup.""" +import functools +from datetime import datetime +from typing import Any + +import dm_env +import launchpad as lp +from absl import app, flags +from launchpad.nodes.python.local_multi_processing import PythonProcess + +from mava.components.tf.architectures import StateBasedQValueCritic +from mava.systems.tf import mad4pg +from mava.systems.tf.mad4pg.execution import MAD4PGRecurrentExecutor +from mava.systems.tf.mad4pg.training import MAD4PGStateBasedRecurrentTrainer +from mava.utils import lp_utils +from mava.utils.environments.RoboCup_env.robocup_agents.nn_agent import ( + make_recurrent_networks as make_networks, +) +from mava.utils.environments.RoboCup_env.robocup_base import create_robocup_environment +from mava.utils.loggers import logger_utils + +FLAGS = flags.FLAGS +flags.DEFINE_string( + "mava_id", + str(datetime.now()), + "Experiment identifier that can be used to continue experiments.", +) + +flags.DEFINE_string("num_executors", "6", "The number of executors to run.") +flags.DEFINE_string("base_dir", "~/mava/", "Base dir to store experiments.") + + +def make_environment(**kwargs) -> dm_env.Environment: + """Creates a MPE environment.""" + + def environment_fn(**kwargs): + return create_robocup_environment(game_name="domain_randomisation", **kwargs) + + return environment_fn + + +def main(_: Any) -> None: + # Environment + # Create an environment, grab the spec, and use it to create networks. + environment_fn = make_environment() + + # Neural Networks + # Create the networks + environment_factory = lp_utils.partial_kwargs(environment_fn) + network_factory = lp_utils.partial_kwargs(make_networks) + + # Checkpointer appends "Checkpoints" to checkpoint_dir + checkpoint_dir = f"{FLAGS.base_dir}/robocup" + + # loggers + log_every = 10 + logger_factory = functools.partial( + logger_utils.make_logger, + directory=FLAGS.base_dir, + to_terminal=True, + to_tensorboard=True, + time_stamp=FLAGS.mava_id, + time_delta=log_every, + ) + + program = mad4pg.MAD4PG( + architecture=StateBasedQValueCritic, + environment_factory=environment_factory, + network_factory=network_factory, + logger_factory=logger_factory, + num_executors=int(FLAGS.num_executors), + samples_per_insert=None, + trainer_fn=MAD4PGStateBasedRecurrentTrainer, + executor_fn=MAD4PGRecurrentExecutor, + shared_weights=True, + checkpoint_subpath=checkpoint_dir, + batch_size=265, + ).build() + + # launch + gpu_id = -1 + env_vars = {"CUDA_VISIBLE_DEVICES": str(gpu_id)} + local_resources = { + "trainer": [], + "evaluator": PythonProcess(env=env_vars), + "executor": PythonProcess(env=env_vars), + } + lp.launch( + program, + lp.LaunchType.LOCAL_MULTI_PROCESSING, + terminal="current_terminal", + local_resources=local_resources, + ) + + +if __name__ == "__main__": + app.run(main) diff --git a/mava/utils/environments/RoboCup_env/RoboCup2D_env.py b/mava/utils/environments/RoboCup_env/RoboCup2D_env.py new file mode 100755 index 000000000..e0bb78594 --- /dev/null +++ b/mava/utils/environments/RoboCup_env/RoboCup2D_env.py @@ -0,0 +1,445 @@ +# type: ignore +import multiprocessing as mp +import os +import time +from typing import Dict + +import numpy as np + +from mava.utils.environments.RoboCup_env.robocup_utils.agent import Agent as basic_agent +from mava.utils.environments.RoboCup_env.robocup_utils.trainer import Trainer +from mava.utils.environments.RoboCup_env.robocup_utils.util_functions import ( + wait_for_next_observations, +) + + +# spawn an agent of team_name, with position +def spawn_agent(team_name, team_id, agent_id, num_players, port): + """ + Used to run an agent in a seperate physical process. + """ + # return type of agent by position, construct + a = basic_agent( + teamname=team_name, team_id=team_id, agent_id=agent_id, num_players=num_players + ) + a.connect("localhost", port) + return a + + +def run_server_thread(game_setting, include_wait, port): + # ./usr/local/bin/rcssmonitor + command = ( + "/usr/local/bin/rcssserver -server::coach_w_referee=on" + " -server::half_time=99999999 -server::game_logging=false" + " -server::game_log_dated=false" + " -server::keepaway_log_dated=false" + " -server::keepaway_logging=false" + " -server::text_log_dated=false" + " -server::port=" + + str(port) + + " -server::coach_port=" + + str(port + 1) + + " -server::olcoach_port=" + + str(port + 2) + ) + + if game_setting == "domain_randomisation" or game_setting == "fixed_opponent": + command += " -server::drop_ball_time=60000" + pass + elif game_setting == "reward_shaping": + pass + else: + raise NotImplementedError( + "Should drop_ball_time be included for this game_setting?" + ) + + if include_wait: + command += " -server::synch_mode=false" + else: + command += " -server::synch_mode=true" + os.system(command) + + +def start_trainer(port): + trainer = Trainer() + trainer.connect("localhost", port + 1) + return trainer + + +def run_monitor_thread(port): + # ./usr/local/bin/rcssmonitor + os.system("/usr/local/bin/rcssmonitor --server-port=" + str(port)) + + +def start_server(game_setting, include_wait, port): + # Wait for server to startup completely + # print("mode: ", mode) + at = mp.Process(target=run_server_thread, args=(game_setting, include_wait, port)) + at.daemon = True + at.start() + time.sleep(0.2) + + +def start_monitor(port): + # Wait for monitor process to complete + at = mp.Process(target=run_monitor_thread, args=(port,)) + at.daemon = True + at.start() + time.sleep(0.2) + + +def connect_agents(team_name, team_id, num_per_team, num_players, start_id, port): + # spawn all agents as seperate processes for maximum processing efficiency + agents = [] + for agent_id in range(start_id, start_id + num_per_team): + agents.append( + spawn_agent( + team_name=team_name, + team_id=team_id, + agent_id=agent_id, + num_players=num_players, + port=port, + ) + ) + time.sleep(0.01) + + print("Spawned %d agents." % len(agents)) + return agents + + +class RoboCup2D: + def __init__( + self, + game_setting="reward_shaping", + include_wait=False, + team_names=["Team1", "Team2"], + players_per_team=11, + render_game=False, + game_length=6000, + beta=0.1, + port=6000, + ): + + self.game_setting = game_setting + if type(players_per_team) is not list: + players_per_team = [players_per_team, players_per_team] + + self.players_per_team = players_per_team + self.num_players = players_per_team[0] + players_per_team[1] + + assert ( + type(players_per_team) == list + and 0 < players_per_team[0] <= 11 + and 0 <= players_per_team[1] <= 11 + ) + + if game_setting == "domain_randomisation": + self.game_diff = 0.0 + game_length = 100 + elif game_setting == "reward_shaping": + game_length = 6000 + elif game_setting == "fixed_opponent": + self.game_diff = 0.0 + game_length = 200 + else: + raise NotImplementedError( + "Game setting not implemented yet: ", game_setting + ) + + self.beta = 0.01 + + # max_dist = 200 + # self.alpha = 1/(max_dist*game_length*(1+beta)) + + # Start the server + start_server(game_setting, include_wait, port) + + self.team_names = team_names + + # Connect trainer which is used to control the RoboCup server + self.trainer = start_trainer(port) + + # Initial reset to move agent to correct position + if game_setting == "single_agent_score": + time.sleep(0.001) + self.trainer.reset_game( + players_per_team, team_names, game_setting, self.game_diff + ) + + self.game_length = game_length + self.game_step = 0 + self.game_scores = [0, 0] + + self.end_step = 0 + + self.tot_out_step = 0.0 + self.tot_in_step_no_wait = 0.0 + self.tot_server_wait = 0.0 + + # Render the game on a monitor + if render_game: + start_monitor(port) + + self.id_dict = {"l": 0, "r": 1} + + # Connect teams + self.agents = {} + # self.agent_ids = [] + self.previous_scores = [0, 0] + start_id = 0 + for t_i in range(2): + if players_per_team[t_i] > 0: + agents = connect_agents( + team_name=team_names[t_i], + team_id=t_i, + num_per_team=players_per_team[t_i], + num_players=self.num_players, + start_id=start_id, + port=port, + ) + for agent in agents: + self.agents["player_" + str(agent.agent_id)] = agent + start_id += players_per_team[t_i] + + def step(self, actions: Dict[str, str]): + # Do agent update + # start_step = time.time() + self.game_step += 1 + + # if self.game_step > 1: + # self.tot_out_step += start_step-self.end_step + + for agent_key, agent in self.agents.items(): + agent.do_action(actions[agent_key]) + + # Send done to show that trainer is done. + if self.trainer: + self.trainer.send_done() + + # if self.game_step > 1: + # inside_step_done = time.time() + # self.tot_in_step_no_wait += inside_step_done-start_step + + # Wait for the environment to step and provide the next observations + wait_for_next_observations([self.trainer]) + + # Check if done with game + done = self.game_step > self.game_length + + # if done: + # print("Time outside step: ", self.tot_out_step) + # print("Time inside step without wait: ", self.tot_in_step_no_wait) + # print("Time waiting for the server: ", self.tot_server_wait) + # + # self.tot_out_step = 0.0 + # self.tot_in_step_no_wait = 0.0 + # self.tot_server_wait = 0.0 + + # Calculate rewards + rewards = {} + score_add = [ + self.trainer.wm.score_l - self.previous_scores[0], + self.trainer.wm.score_r - self.previous_scores[1], + ] + self.previous_scores[0] = self.trainer.wm.score_l + self.previous_scores[1] = self.trainer.wm.score_r + self.game_scores[0] += score_add[0] + self.game_scores[1] += score_add[1] + + # Reset the game if a goal is scored + if ( + self.game_setting == "reward_shaping" + and score_add[0] != 0 + or score_add[1] != 0 + ): + self.trainer.reset_game( + self.players_per_team, + self.team_names, + self.game_setting, + reset_stamina=False, + ) + + wm = self.trainer.wm + for agent_key, agent in self.agents.items(): + agent.do_action(actions[agent_key]) + team_id = agent.team_id + opponent_id = (team_id + 1) % 2 + + # Calculate rewards + player = wm.players[agent.agent_id] + + if self.game_setting == "reward_shaping": + if player["teamname"] == "Team_A": + destination_coords = wm.goals[0]["coords"] + elif player["teamname"] == "Team_B": + destination_coords = wm.goals[1]["coords"] + else: + raise ValueError("Unknown team: ", player["teamname"]) + + ball_goal_dist = wm.euclidean_distance( + wm.ball["coords"], destination_coords + ) + next_x = float(wm.ball["coords"][0]) + float(wm.ball["delta_coords"][0]) + next_y = float(wm.ball["coords"][1]) + float(wm.ball["delta_coords"][1]) + ball_goal_delta_dist = wm.euclidean_distance( + (next_x, next_y), destination_coords + ) + ball_towards_goal = ball_goal_dist - ball_goal_delta_dist + + # player_ball_dist = wm.euclidean_distance( + # player["coords"], wm.ball["coords"] + # ) + next_x = float(player["coords"][0]) + float(player["delta_coords"][0]) + next_y = float(player["coords"][1]) + float(player["delta_coords"][1]) + # player_ball_delta_dist = wm.euclidean_distance( + # (next_x, next_y), wm.ball["coords"] + # ) + # player_towards_ball = player_ball_dist - player_ball_delta_dist + + scored = score_add[team_id] - score_add[opponent_id] + + # TODO: Change back + # if scored > 0.1: + # if self.beta < 0.001: + # self.beta = 0.0 + # print("Beta minimum reached.") + # else: + # self.beta *= 0.99 + # print("Beta: ", self.beta) + + self.beta = 0.01 + reward = ( + scored + ball_towards_goal * self.beta + ) # + player_towards_ball * self.beta + scored + elif self.game_setting == "domain_randomisation": + reward = score_add[team_id] - score_add[opponent_id] + + # # TODONE: Remove this. Masking zero rewards! + # if reward < 0: + # reward = 0 + + else: + raise NotImplementedError("Unknown game setting: ", self.game_setting) + + rewards[agent_key] = np.array(reward, np.float32) + + # self.end_step = time.time() + # if self.game_step > 1: + # self.tot_server_wait += self.end_step - inside_step_done + + # Return latest observations and states + + # obs = self.__get_latest_obs() + # state = self.__get_state() + + # if "estimated_coords" in obs["player_0"]: + # print("Coords: ", (obs["player_0"]["estimated_coords"].tolist(), + # state["players"][0]["coords"]), "Body angle: ", + # (obs["player_0"]["ang_offs"], state["players"][0]["body_angle"])) + # else: + # print("No location info.") + + return self.__get_latest_obs(), rewards, self.__get_state(), done + + def reset(self): + self.game_step = 0 + if self.game_setting == "domain_randomisation": + goal_diff = self.game_scores[0] - self.game_scores[1] + if goal_diff != 0: + if self.game_diff < 1.0: + self.game_diff += 0.01 + # print("Game difficulty increased: ", self.game_diff) + else: + self.game_diff = 1.0 + print("Max game difficulty surpassed!") + elif self.game_diff < 1.0: + if self.game_diff > 0.0: + self.game_diff -= 0.01 + + if self.game_diff < 0.0: + # Sometimes the game_diff can be slightly smaller than zero + # which causes problems. + self.game_diff = 0.0 + # print("Game difficulty decreased: ", self.game_diff) + + self.game_length = int(100 + self.game_diff * 900) + # print("Reset game length: ", self.game_length) + # TODO: Update alpha + # self.alpha = 1 / (max_dist * game_length * (1 + beta)) + + self.trainer.reset_game( + self.players_per_team, + self.team_names, + self.game_setting, + self.game_diff, + ) + elif self.game_setting == "reward_shaping": + self.trainer.reset_game( + self.players_per_team, self.team_names, self.game_setting + ) + else: + raise NotImplementedError("Unknown game setting: ", self.game_setting) + + self.game_scores = [0, 0] + + # print("Resetting.") + # Switch teams + # TODO: Add team switching for better training. Also maybe shuffle agents in a + # single team. Therefore change the agent each policy is controlling at every + # reset step. Make sure that the dictionary stuff does not break. + + # Only shuffle if teams are equal for now. + # if self.players_per_team[0] == self.players_per_team[1]: + # team1 = self.agents[0:self.players_per_team[0]] + # team2 = self.agents[self.players_per_team[0]:] + # + # random.shuffle(team1) + # random.shuffle(team2) + # + # self.agents = team2 + # self.agents.extend(team1) + rewards = [None] * len(self.agents) + + # Wait for the environment to step and provide the next observations + wait_for_next_observations([self.trainer]) + return self.__get_latest_obs(), rewards, self.__get_state() + + def __get_state(self): + # Return latest observations to agents + state_dict = self.trainer.get_state_dict() + # TODO: Return per agent states. The states should be mirrored. So a critic and + # policy can train on only one side but execute on both sides. + # state_dict + state_dict["game_step"] = self.game_step + state_dict["game_length"] = self.game_length + return state_dict + + def __get_latest_obs(self): + # Return latest observations to agents + obs = {} + for agent_key, agent in self.agents.items(): + obs_dict = agent.get_latest_observation() + if obs_dict: + obs_dict["game_step"] = self.game_step + obs_dict["game_length"] = self.game_length + + obs[agent_key] = obs_dict + + # TODO: Delete the if else below + # if len(obs) == 1: + # state = self.trainer.get_state_dict() + # + # if len(state["players"]) > 0: + # obs["player_0"]["estimated_coords"] = + # np.array(state["players"][0]["coords"]) + # obs["player_0"]["ang_offs"] = state["players"][0]["body_angle"] + # else: + # obs["player_0"]["estimated_coords"] = [0.0, 0.0] + # obs["player_0"]["ang_offs"] = 0.0 + # + # # print("Agent: ", obs["player_0"]) + # # print("Trainer: ", self.trainer.get_state_dict()) + # else: + # print("Remove this trainer overwrite this!!!") + # exit() + return obs diff --git a/mava/utils/environments/RoboCup_env/helper.py b/mava/utils/environments/RoboCup_env/helper.py new file mode 100644 index 000000000..f982c2917 --- /dev/null +++ b/mava/utils/environments/RoboCup_env/helper.py @@ -0,0 +1,51 @@ +# python3 +# Copyright 2018 DeepMind Technologies Limited. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""OpenAI Gym environment factory.""" + +import dm_env +import gym +from acme import wrappers + +TASKS = { + "debug": ["MountainCarContinuous-v0"], + "default": [ + "HalfCheetah-v2", + "Hopper-v2", + "InvertedDoublePendulum-v2", + "InvertedPendulum-v2", + "Reacher-v2", + "Swimmer-v2", + "Walker2d-v2", + ], +} + + +def make_environment( + evaluation: bool = False, task: str = "MountainCarContinuous-v0" +) -> dm_env.Environment: + """Creates an OpenAI Gym environment.""" + del evaluation + + # Load the gym environment. + environment = gym.make(task) + + # Make sure the environment obeys the dm_env.Environment interface. + environment = wrappers.GymWrapper(environment) + # Clip the action returned by the agent to the environment spec. + environment = wrappers.CanonicalSpecWrapper(environment, clip=True) + environment = wrappers.SinglePrecisionWrapper(environment) + + return environment diff --git a/mava/utils/environments/RoboCup_env/robocup_agents/custom_executor.py b/mava/utils/environments/RoboCup_env/robocup_agents/custom_executor.py new file mode 100644 index 000000000..912fbf774 --- /dev/null +++ b/mava/utils/environments/RoboCup_env/robocup_agents/custom_executor.py @@ -0,0 +1,46 @@ +# type: ignore +from typing import Dict, Optional + +import dm_env +from acme import types + +from mava.utils.environments.RoboCup_env.robocup_utils.game_object import Flag + +true_flag_coords = Flag.FLAG_COORDS + + +class CustomExecutor: + """A fixed executor to test in the environments are working.""" + + def __init__(self, agents): + # Convert action and observation specs. + self.agents = agents + + def observe_first( + self, + timestep: dm_env.TimeStep, + extras: Dict[str, types.NestedArray] = {"": ()}, + ) -> None: + pass + + def observe( + self, + actions: Dict[str, types.NestedArray], + next_timestep: dm_env.TimeStep, + next_extras: Optional[Dict[str, types.NestedArray]] = {}, + ) -> None: + pass + + def select_actions( + self, observations: Dict[str, types.NestedArray] + ) -> Dict[str, types.NestedArray]: + actions = {} + for agent, observation in observations.items(): + # Pass the observation through the policy network. + actions[agent] = self.agents[agent].get_action(observation.observation) + + # Return a numpy array with squeezed out batch dimension. + return actions + + def update(self, wait: bool = False) -> None: + pass diff --git a/mava/utils/environments/RoboCup_env/robocup_agents/fixed_agent.py b/mava/utils/environments/RoboCup_env/robocup_agents/fixed_agent.py new file mode 100755 index 000000000..8c431485a --- /dev/null +++ b/mava/utils/environments/RoboCup_env/robocup_agents/fixed_agent.py @@ -0,0 +1,145 @@ +# type: ignore +import numpy as np + +from mava.utils.environments.RoboCup_env.robocup_utils.util_functions import ( + SpecWrapper, + deg_rot_to_xy, +) + + +class NaiveBot(object): + def __init__(self, agent_type, num_players): + # Convert action and observation specs. + + spec_wrapper = SpecWrapper(num_players) + self.scaling = spec_wrapper.scaling + self.num_players = num_players + assert self.scaling == 200.0 + self.kicked = False + # self.obs_norm = 200.0 + # self.action_scaling = 180 + # self.obs_size = 17 + # self._action_size = 7 + + def reset_brain(self): + pass + + def get_action(self, observation): + obs_dict = { + "time_left": 0, + "side": 1, + "sense_self": 2, + "coords": (3, 5), + "body_dir": (5, 7), + "head_dir": (7, 9), + "width": (9, 12), + "quality": 12, + "stamina": 13, + "effort": 14, + "speed_amount": 15, + "speed_direction": (16, 18), + "neck_direction": (18, 20), + "see_ball": 20, + "ball_dist": 21, + "ball_dir": (22, 24), + "ball_dist_change": 24, + "ball_dir_change": 25, + "ball_speed": 26, + } + s, e = obs_dict["coords"] + p_loc = observation[s:e] * self.scaling + + s, e = obs_dict["body_dir"] + p_deg = np.arctan2(observation[e - 1], observation[s]) * 180 / np.pi + + # see_ball, ball_distance, ball_direction (x, y format), + see_ball = observation[obs_dict["see_ball"]] + b_dist = observation[obs_dict["ball_dist"]] * self.scaling + s, e = obs_dict["ball_dir"] + b_deg = np.arctan2(observation[e - 1], observation[s]) * 180 / np.pi + # Compute the policy, conditioned on the observation. + # dash (speed), turn (direction in x,y format), + # kick (direction in x,y format, power) + command = { + "do_dash": 0, + "do_kick": 1, + "do_change_view": 2, + "do_tackle": 3, + "do_turn": 4, + "do_turn_neck": 5, + "do_none": 6, + "dash_power": 7, + "dash_dir_x": 8, + "dash_dir_y": 9, + "turn_dir_x": 18, + "turn_dir_y": 19, + "kick_power": 10, + "kick_dir_x": 11, + "kick_dir_y": 12, + } + action = np.zeros(22) + if see_ball < 0.5: + # Search for the ball + # print("Searching..") + action[command["do_turn"]] = 1 + x, y = deg_rot_to_xy(20) + action[command["turn_dir_x"]] = x + action[command["turn_dir_y"]] = y + elif b_dist < 0.5: + if not self.kicked: + # Kick the ball + self.kicked = True + # goal_locs = np.array([np.random.randn()*20, np.random.randn()*20]) + goal_locs = np.array([52.5, 0]) + + x_diff, y_diff = goal_locs - p_loc + + kick_abs_dir = -np.arctan2(y_diff, x_diff) * 180 / np.pi + + kick_dir = p_deg - 90 + kick_abs_dir + + action[command["do_kick"]] = 1 + action[command["kick_power"]] = 50 / self.scaling + + x, y = deg_rot_to_xy(kick_dir) + action[command["kick_dir_x"]] = x + action[command["kick_dir_y"]] = y + else: + action[command["do_none"]] = 1 + # print("Waiting..") + elif np.abs(b_deg) > 10: + # Turn towards the ball + # print("Turning..") + action[command["do_turn"]] = 1 + x, y = deg_rot_to_xy(b_deg / 3) + action[command["turn_dir_x"]] = x + action[command["turn_dir_y"]] = y + else: + # Dash towards the ball + action[command["do_dash"]] = 1 + # print("Dashing..") + speed = 100 + + if b_dist < 5: + speed = 20 + + action[command["dash_power"]] = speed / self.scaling + x, y = deg_rot_to_xy(0) + action[command["dash_dir_x"]] = x + action[command["dash_dir_y"]] = y + + if b_dist > 5.0: + self.kicked = False + + return action + + +# class RotateBot(object): +# def __init__(self, num_actions=9): +# pass +# +# def reset_brain(self): +# pass +# +# def get_action(self, observations, last_action, reward=None): +# return "(turn 30)" diff --git a/mava/utils/environments/RoboCup_env/robocup_agents/nn_agent.py b/mava/utils/environments/RoboCup_env/robocup_agents/nn_agent.py new file mode 100755 index 000000000..4a736b7df --- /dev/null +++ b/mava/utils/environments/RoboCup_env/robocup_agents/nn_agent.py @@ -0,0 +1,279 @@ +# type: ignore +import os +from typing import Any, Dict, Mapping, MutableSequence, Sequence, Union + +import numpy as np +import sonnet as snt + +# import logging +import tensorflow as tf +import tensorflow_probability as tfp +from acme import types +from acme.tf import networks +from acme.tf import savers as tf2_savers +from acme.tf import utils as tf2_utils + +from mava import specs as mava_specs +from mava.components.tf.architectures import StateBasedQValueCritic +from mava.components.tf.networks.mad4pg import DiscreteValuedHead +from mava.utils.environments.RoboCup_env.robocup_utils.util_functions import SpecWrapper + +tfd = tfp.distributions +# log = logging.getLogger(__name__) + + +def set_gpu_affinity(gpus: Any) -> Any: + devices = tf.config.list_physical_devices("GPU") + + # process the requested gpus + if gpus is None or len(devices) == 0: + gpus = {} + elif isinstance(gpus, int): + gpus = set(devices if gpus == -1 else [devices[gpus]]) + elif isinstance(gpus, MutableSequence): + gpus = {devices[k] for k in gpus} + else: + raise ValueError( + f"Can't parse requested gpus, got {gpus} of type {type(gpus)}." + ) + + # set the visibility + # if the set is empty, the function will be called on an empty list + # so that no `LogicalDevice` will be created on the physical devices. + tf.config.set_visible_devices(list(gpus), "GPU") + os.environ["CUDA_VISIBLE_DEVICES"] = ",".join( + [gpu.name.split(":")[-1] for gpu in gpus] + ) + + # set memory growth + for gpu in gpus: + try: + tf.config.experimental.set_memory_growth(gpu, True) + except RuntimeError as e: + print(e) + + # log.info( + # f"Devices: {len(devices)} physical and " + # f"{len(tf.config.list_logical_devices('GPU'))} logical devices" + # ) + return gpus + + +def make_networks( + environment_spec: mava_specs.MAEnvironmentSpec, + policy_networks_layer_sizes: Union[Dict[str, Sequence], Sequence] = ( + 256, + 256, + 256, + ), + critic_networks_layer_sizes: Union[Dict[str, Sequence], Sequence] = (512, 512, 256), + shared_weights: bool = True, + sigma: float = 0.3, +) -> Mapping[str, types.TensorTransformation]: + """Creates networks used by the agents.""" + specs = environment_spec.get_agent_specs() + + # Create agent_type specs + if shared_weights: + type_specs = {key.split("_")[0]: specs[key] for key in specs.keys()} + specs = type_specs + + if isinstance(policy_networks_layer_sizes, Sequence): + policy_networks_layer_sizes = { + key: policy_networks_layer_sizes for key in specs.keys() + } + if isinstance(critic_networks_layer_sizes, Sequence): + critic_networks_layer_sizes = { + key: critic_networks_layer_sizes for key in specs.keys() + } + + observation_networks = {} + policy_networks = {} + critic_networks = {} + for key in specs.keys(): + + # Get total number of action dimensions from action spec. + num_dimensions = np.prod(specs[key].actions.shape, dtype=int) + + # Create the shared observation network; here simply a state-less operation. + observation_network = tf2_utils.to_sonnet_module(tf2_utils.batch_concat) + + # Create the policy network. + policy_network = snt.Sequential( + [ + observation_network, + networks.LayerNormMLP( + policy_networks_layer_sizes[key], activate_final=True + ), + networks.NearZeroInitializedLinear(num_dimensions), + networks.TanhToSpec(specs[key].actions), + networks.ClippedGaussian(sigma), + networks.ClipToSpec(specs[key].actions), + ] + ) + + # Create the critic network. + critic_network = snt.Sequential( + [ + # The multiplexer concatenates the observations/actions. + networks.CriticMultiplexer(), + networks.LayerNormMLP( + critic_networks_layer_sizes[key], activate_final=False + ), + snt.Linear(1), + ] + ) + observation_networks[key] = observation_network + policy_networks[key] = policy_network + critic_networks[key] = critic_network + + return { + "policies": policy_networks, + "critics": critic_networks, + "observations": observation_networks, + } + + +def make_recurrent_networks( + environment_spec: mava_specs.MAEnvironmentSpec, + policy_networks_layer_sizes: Union[Dict[str, Sequence], Sequence] = ( + 256, + 256, + 256, + ), + critic_networks_layer_sizes: Union[Dict[str, Sequence], Sequence] = (512, 512, 256), + shared_weights: bool = True, + sigma: float = 0.3, + vmin: float = -150.0, + vmax: float = 150.0, + num_atoms: int = 51, +) -> Mapping[str, types.TensorTransformation]: + """Creates networks used by the agents.""" + specs = environment_spec.get_agent_specs() + + # Create agent_type specs + if shared_weights: + type_specs = {key.split("_")[0]: specs[key] for key in specs.keys()} + specs = type_specs + + if isinstance(policy_networks_layer_sizes, Sequence): + policy_networks_layer_sizes = { + key: policy_networks_layer_sizes for key in specs.keys() + } + if isinstance(critic_networks_layer_sizes, Sequence): + critic_networks_layer_sizes = { + key: critic_networks_layer_sizes for key in specs.keys() + } + + observation_networks = {} + policy_networks = {} + critic_networks = {} + for key in specs.keys(): + + # Get total number of action dimensions from action spec. + num_dimensions = np.prod(specs[key].actions.shape, dtype=int) + + # Create the observation network. + observation_network = tf2_utils.to_sonnet_module(tf2_utils.batch_concat) + + # Create the policy network. + policy_network = snt.DeepRNN( + [ + observation_network, + snt.Flatten(), + snt.nets.MLP(policy_networks_layer_sizes[key]), + snt.LSTM(25), + snt.nets.MLP([128]), + networks.NearZeroInitializedLinear(num_dimensions), + networks.TanhToSpec(specs[key].actions), + networks.ClippedGaussian(sigma), + networks.ClipToSpec(specs[key].actions), + ] + ) + + # Create the critic network. + critic_network = snt.Sequential( + [ + # The multiplexer concatenates the observations/actions. + networks.CriticMultiplexer(), + networks.LayerNormMLP( + critic_networks_layer_sizes[key], activate_final=False + ), + DiscreteValuedHead(vmin, vmax, num_atoms), + ] + ) + + observation_networks[key] = observation_network + policy_networks[key] = policy_network + critic_networks[key] = critic_network + + return { + "observations": observation_networks, + "policies": policy_networks, + "critics": critic_networks, + } + + +class NNBot(object): + def __init__( + self, agent_type, num_players, checkpoint_subpath: str = "Checkpoints" + ): + # Convert action and observation specs. + spec_wrapper = SpecWrapper(num_players) + + # setup the resources + set_gpu_affinity(0) + + environment_spec = mava_specs.MAEnvironmentSpec(spec_wrapper) + + # Create networks + shared_weights = True + system_networks = make_networks( + environment_spec=environment_spec, shared_weights=shared_weights + ) + + architecture = StateBasedQValueCritic( + environment_spec=environment_spec, + policy_networks=system_networks["policies"], + critic_networks=system_networks["critics"], + observation_networks=system_networks["observations"], + shared_weights=shared_weights, + ) + + # Create the policy_networks + policy_networks = architecture.create_actor_variables() + before_sum = policy_networks["policies"][agent_type].variables[1].numpy().sum() + # print("Weights before: ", before_sum) + objects_to_save = { + "policy": policy_networks["policies"][agent_type], + "observation": policy_networks["observations"][agent_type], + } + + checkpointer_dir = os.path.join(checkpoint_subpath, agent_type) + tf2_savers.Checkpointer( + time_delta_minutes=1, + add_uid=False, + directory=checkpointer_dir, + objects_to_save=objects_to_save, + enable_checkpointing=True, + ) + after_sum = policy_networks["policies"][agent_type].variables[1].numpy().sum() + + assert before_sum != after_sum + + self.policy = policy_networks["policies"][agent_type] + self.num_players = num_players + + def reset_brain(self): + pass + + def get_action(self, observation): + # Add a dummy batch dimension and as a side effect convert numpy to TF. + batched_observation = tf2_utils.add_batch_dim(observation) + + # Compute the policy, conditioned on the observation. + policy = self.policy(batched_observation) + + # Sample from the policy if it is stochastic. + action = policy.sample() if isinstance(policy, tfd.Distribution) else policy + return action.numpy()[0] diff --git a/mava/utils/environments/RoboCup_env/robocup_base.py b/mava/utils/environments/RoboCup_env/robocup_base.py new file mode 100644 index 000000000..e3d185752 --- /dev/null +++ b/mava/utils/environments/RoboCup_env/robocup_base.py @@ -0,0 +1,107 @@ +# type: ignore +from typing import Tuple + +import dm_env +import gym +import numpy as np +from acme import types + +from mava.utils.environments.RoboCup_env.RoboCup2D_env import RoboCup2D +from mava.utils.environments.RoboCup_env.robocup_utils.util_functions import SpecWrapper + + +class RoboCupWrapper(SpecWrapper): + """Environment wrapper for 2D RoboCup environment.""" + + # Note: we don't inherit from base.EnvironmentWrapper because that class + # assumes that the wrapped environment is a dm_env.Environment. + + def __init__(self, environment: RoboCup2D): + self._environment = environment + self._reset_next_step = True + assert environment.game_setting in ["reward_shaping", "domain_randomisation"] + + super().__init__(environment.num_players) + + def reset(self) -> Tuple[dm_env.TimeStep, np.array]: + """Resets the episode.""" + self._reset_next_step = False + raw_obs, _, state = self._environment.reset() + proc_obs = self._proc_robocup_obs(observations=raw_obs, done=False) + proccessed_state = self._proc_robocup_state(state, proc_obs) + timestep = dm_env.restart(proc_obs) + return timestep, {"env_state": proccessed_state} + + def step(self, nn_actions: types.NestedArray) -> Tuple[dm_env.TimeStep, np.array]: + """Steps the environment.""" + if self._reset_next_step: + return self.reset() + + actions = self._proc_robocup_actions(nn_actions) + raw_obs, rewards, state, done = self._environment.step(actions) + self._reset_next_step = done + + proc_obs = self._proc_robocup_obs( + observations=raw_obs, done=done, nn_actions=nn_actions + ) + proccessed_state = self._proc_robocup_state(state, proc_obs) + + if done: + self._step_type = dm_env.StepType.LAST + else: + self._step_type = dm_env.StepType.MID + + return ( + dm_env.TimeStep( + observation=proc_obs, + reward=rewards, + discount=self._discount, + step_type=self._step_type, + ), + {"env_state": proccessed_state}, + ) + + @property + def environment(self) -> gym.Env: + """Returns the wrapped environment.""" + return self._environment + + def close(self): + self._environment.close() + + +def create_robocup_environment( + game_name: str, evaluation: bool = False +) -> dm_env.Environment: + """Wraps the Robocup environment with some basic preprocessing. + + Args: + game_name: str, the name of the Robocup game setting. + evaluation: bool, to change the behaviour during evaluation. + + Returns: + A Robocup environment with some standard preprocessing. + """ + + # Create environment + if game_name == "domain_randomisation": + players_per_team = [1, 0] + elif game_name == "reward_shaping": + players_per_team = [1, 0] + # elif game_name == "fixed_opponent": + # players_per_team = [2, 2] + else: + raise NotImplementedError("Game type not implemented: ", game_name) + + # TODO: Change this to better assign ports + rand_port = np.random.randint(6000, 60000) + robocup_env = RoboCup2D( + game_setting=game_name, + team_names=["Team_A", "Team_B"], + players_per_team=players_per_team, + render_game=True, + include_wait=False, + game_length=1000, + port=rand_port, + ) + return RoboCupWrapper(robocup_env) diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/__init__.py b/mava/utils/environments/RoboCup_env/robocup_utils/__init__.py new file mode 100755 index 000000000..e69de29bb diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/agent.py b/mava/utils/environments/RoboCup_env/robocup_utils/agent.py new file mode 100755 index 000000000..d7fc879fd --- /dev/null +++ b/mava/utils/environments/RoboCup_env/robocup_utils/agent.py @@ -0,0 +1,306 @@ +# type: ignore +# !/usr/bin/env python3 + +import threading +import time + +# import math +import numpy as np + +from mava.utils.environments.RoboCup_env.robocup_utils import ( + handler, + sock, + sp_exceptions, +) +from mava.utils.environments.RoboCup_env.robocup_utils.player_world_model import ( + WorldModel, +) +from mava.utils.environments.RoboCup_env.robocup_utils.util_functions import ( + SpecWrapper, + wait_for_next_observations, +) + +# import copy +# import os +# os.environ['PYGAME_HIDE_SUPPORT_PROMPT'] = "hide" +# import pygame + +# Define some colours +green = (0, 255, 50) +light_blue = (0, 255, 255) +blue = (0, 0, 255) +red = (255, 0, 0) +yellow = (255, 255, 0) +white = (255, 255, 255) + +# R G B +field_col = (0, 200, 50) +team_player = blue +opp_player = light_blue +own_goal = (255, 140, 0) +opp_goal = (128, 0, 128) + + +class Agent: + def __init__(self, teamname, team_id, agent_id, num_players, agent_controller=None): + # whether we're connected to a server yet or not + self.__connected = False + + self.teamname = teamname + self.agent_id = agent_id + self.team_id = team_id + self.agent_controller = agent_controller + + if self.agent_controller is not None: + self.specWrapper = SpecWrapper(num_players=num_players) + + # set all variables and important objects to appropriate values for + # pre-connect state. + + # the socket used to communicate with the server + self.__sock = None + + # models and the message handler for parsing and storing information + self.wm = None + self.msg_handler = None + + # parse thread and control variable + self.__parsing = False + self.__msg_thread = None + + self.__thinking = False # think thread and control variable + self.__think_thread = None + + # whether we should send commands + self.__send_commands = False + + # adding goal post markers + self.enemy_goal_pos = None + self.own_goal_pos = None + + def connect(self, host, port, version=11): + """ + Gives us a connection to the server as one player on a team. This + immediately connects the agent to the server and starts receiving and + parsing the information it sends. + """ + self.start_comm_threads(host, port, self.teamname, version) + + def start_comm_threads(self, host, port, teamname, version): + # if already connected, raise an error since user may have wanted to + # connect again to a different server. + if self.__connected: + msg = "Cannot connect while already connected, disconnect first." + raise sp_exceptions.AgentConnectionStateError(msg) + + # the pipe through which all of our communication takes place + # p rint("Start comms: ", port) + self.__sock = sock.Socket(host, port) + + # our models of the world and our body + self.wm = WorldModel(handler.ActionHandler(self.__sock)) + + # set the team name of the world model to the given name + self.wm.teamname = teamname + + # handles all messages received from the server + self.msg_handler = handler.MessageHandler(self.wm) + + # set up our threaded message receiving system + self.__parsing = True # tell thread that we're currently running + + init_address = self.__sock.address + # send the init message and allow the message handler to handle further + # responses. + init_msg = "(init %s (version %d))" + self.__sock.send(init_msg % (teamname, version)) + + self.__message_tread = threading.Thread( + target=self.__message_loop, name="message_loop" + ) + self.__message_tread.daemon = True + self.__message_tread.start() + # wait until the socket receives a response from the server and gets its + # assigned port. + # print("Agent init address: ", init_address) + while self.__sock.address == init_address: + time.sleep(0.0001) + + # print("Agent new address: ", self.__sock.address) + # exit() + # create our thinking thread. this will perform the actions necessary + # to play a game of robo-soccer. + self.__thinking = True + # self.__think_thread.daemon = True + + # set connected state. done last to prevent state inconsistency if + # something goes wrong beforehand. + self.__connected = True + + self.last_action = None + + # Move to random starting position + + x = np.random.randint(-52, 53) + y = np.random.randint(-34, 35) + + self.__sock.send("(move " + str(x) + " " + str(y) + ")") + self.__sock.send("(done)") + + if self.agent_controller is not None: + self.__think_thread = threading.Thread( + target=self.__think_loop, name="think_loop" + ) + self.__think_thread.daemon = True + self.__think_thread.start() + + # def disconnect(self): + # """ + # Tell the loop threads to stop and signal the server that we're + # disconnecting, then join the loop threads and destroy all our inner + # methods. + # + # Since the message loop thread can conceiveably block indefinitely while + # waiting for the server to respond, we only allow it (and the think loop + # for good measure) a short time to finish before simply giving up. + # + # Once an agent has been disconnected, it is 'dead' and cannot be used + # again. All of its methods get replaced by a method that raises an + # exception every time it is called. + # """ + # + # # don't do anything if not connected + # if not self.__connected: + # return + # + # # tell the loops to terminate + # self.__parsing = False + # self.__thinking = False + # + # # tell the server that we're quitting + # self.__sock.send("(bye)") + # + # # tell our threads to join, but only wait breifly for them to do so. + # # don't join them if they haven't been started (this can happen if + # # disconnect is called very quickly after connect). + # if self.__msg_thread.is_alive(): + # self.__msg_thread.join(0.01) + # + # # if self.__think_thread.is_alive(): + # # self.__think_thread.join(0.01) + # + # # reset all standard variables in this object. self.__connected gets + # # reset here, along with all other non-user defined internal variables. + # Agent.__init__(self) + + def __message_loop(self): + """ + Handles messages received from the server. + + This SHOULD NOT be called externally, since it's used as a threaded loop + internally by this object. Calling it externally is a BAD THING! + """ + + # loop until we're told to stop + while self.__parsing: + # receive message data from the server and pass it along to the + # world model as-is. the world model parses it and stores it within + # itself for perusal at our leisure. + raw_msg = self.__sock.recv() + + # if b'goal' in raw_msg: + # print("Agent message: '" , raw_msg, "'") + + msg_type = self.msg_handler.handle_message(raw_msg) + if msg_type is not None: + # we send commands all at once every cycle, ie. whenever a + # 'sense_body' command is received + if msg_type == handler.ActionHandler.CommandType.SENSE_BODY: + self.__send_commands = True + + def __think_loop(self): + """ + Performs world model analysis and sends appropriate commands to the + server to allow the agent to participate in the current game. + + Like the message loop, this SHOULD NOT be called externally. Use the + play method to start play, and the disconnect method to end it. + """ + + while self.__thinking: + # Get and process the latest observation + observation = self.specWrapper.proc_agent_env_obs( + env_agent_obs=self.get_latest_observation(), + last_action=self.last_action, + ) + + # Get the agent's action and covert it to a RoboCup environment command. + + self.last_action = self.agent_controller.get_action(observation) + action = self.specWrapper.proc_agent_action(self.last_action) + + # Send the action + self.__sock.send(action) + self.__sock.send("(done)") + + # Wait for a new timestep indicator + wait_for_next_observations([self]) + + def get_latest_observation(self): + # Generate new observation + obs_dict = {} + obs_dict["players"] = self.wm.players + + # Ball + obs_dict["ball"] = self.wm.ball + + # Team side + obs_dict["side"] = self.wm.side + + # Score + obs_dict["scores"] = [self.wm.score_l, self.wm.score_r] + + obs_dict["view_width"] = self.wm.view_width + obs_dict["view_quality"] = self.wm.view_quality + obs_dict["stamina"] = self.wm.stamina + obs_dict["effort"] = self.wm.effort + obs_dict["speed_amount"] = self.wm.speed_amount + + # TODO: Is these two measurements relative? I think they are. + obs_dict["speed_direction"] = self.wm.speed_direction + obs_dict["neck_direction"] = self.wm.neck_direction + + # self.wm.obs_updated = False + obs_dict["estimated_abs_coords"] = self.wm.abs_coords + obs_dict["estimated_abs_body_dir"] = self.wm.abs_body_dir + obs_dict["estimated_abs_neck_dir"] = self.wm.abs_neck_dir + + # Fix observations so that it is side almost agnostic. A agent can + # be trained on one side and still execute what it has learned on + # the other side of the field. + if obs_dict["side"] == "r": + if obs_dict["estimated_abs_coords"][0] is not None: + obs_dict["estimated_abs_coords"] = [ + -obs_dict["estimated_abs_coords"][0], + -obs_dict["estimated_abs_coords"][1], + ] + obs_dict["estimated_abs_neck_dir"] += 180 + obs_dict["estimated_abs_body_dir"] += 180 + + else: + assert obs_dict["side"] == "l" + + return obs_dict + + def do_action(self, action): + # tell the ActionHandler to send its enqueued messages if it is time + # only think if new data has arrived + if self.agent_controller: + action = self.agent_controller.get_action( + self.get_latest_observation(), None + ) + print("When is this actually used?") + exit() + + self.__sock.send(action) + self.__sock.send("(done)") diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/extract_policy.py b/mava/utils/environments/RoboCup_env/robocup_utils/extract_policy.py new file mode 100644 index 000000000..8bd16d250 --- /dev/null +++ b/mava/utils/environments/RoboCup_env/robocup_utils/extract_policy.py @@ -0,0 +1,53 @@ +# type: ignore +import os +import pickle + +from acme.tf import savers as tf2_savers + + +def save_policy_and_obs(architecture, agent_type, checkpoint_subpath, pickle_save_loc): + policy_networks = architecture.create_actor_variables() + before_sum = policy_networks["policies"][agent_type].variables[1].numpy().sum() + # print("Weights before: ", before_sum) + objects_to_save = { + "policy": policy_networks["policies"][agent_type], + "observation": policy_networks["observations"][agent_type], + } + + checkpointer_dir = os.path.join(checkpoint_subpath, agent_type) + tf2_savers.Checkpointer( + time_delta_minutes=1, + add_uid=False, + directory=checkpointer_dir, + objects_to_save=objects_to_save, + enable_checkpointing=True, + ) + after_sum = policy_networks["policies"][agent_type].variables[1].numpy().sum() + + assert before_sum != after_sum + + # Save policy variables + print(policy_networks["policies"][agent_type]) + exit() + policy = policy_networks["policies"][agent_type].set.variables + policy_file = open(os.path.join(pickle_save_loc, "policy.obj"), "wb") + pickle.dump(policy, policy_file) + policy_file.close() + + # So observation variables + observation = policy_networks["observations"][agent_type].variables + obs_file = open(os.path.join(pickle_save_loc, "observations.obj"), "wb") + pickle.dump(observation, obs_file) + obs_file.close() + + +def load_policy_and_obs(pickle_save_loc): + # Load policy variables + filehandler = open(os.path.join(pickle_save_loc, "policy.obj"), "rb") + policy_net = pickle.load(filehandler) + + # Load observation variables + filehandler = open(os.path.join(pickle_save_loc, "policy.obj"), "rb") + obs_net = pickle.load(filehandler) + + return policy_net, obs_net diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/game_object.py b/mava/utils/environments/RoboCup_env/robocup_utils/game_object.py new file mode 100755 index 000000000..5b6700664 --- /dev/null +++ b/mava/utils/environments/RoboCup_env/robocup_utils/game_object.py @@ -0,0 +1,193 @@ +# type: ignore +class GameObject: + """ + Root class for all percievable objects in the world model. + """ + + def __init__(self, distance, direction): + """ + All objects have a distance and direction to the player, at a minimum. + """ + + self.distance = distance + self.direction = direction + + +class Line(GameObject): + """ + Represents a line on the soccer field. + """ + + def __init__(self, distance, direction, line_id): + self.line_id = line_id + + GameObject.__init__(self, distance, direction) + + +class Goal(GameObject): + """ + Represents a goal object on the field. + """ + + def __init__(self, distance, direction, goal_id): + self.goal_id = goal_id + + GameObject.__init__(self, distance, direction) + + +class Flag(GameObject): + """ + A flag on the field. Can be used by the agent to determine its position. + """ + + # a dictionary mapping all flag_ids to their on-field (x, y) coordinates + # TODO: Add real values form code here and do not use estimates. + # TODO: these are educated guesses based on Figure 4.2 in the documentation. + # where would one find the actual coordinates, besides in the server + # code? + # Location: + # https://github.com/rcsoccersim/rcssserver/blob/master/src/landmarkreader.cpp + # https://rcsoccersim.github.io/manual/soccerserver.html Fig 4.2 + + pitch_x = 52.5 + pitch_y = 34.0 + outside_add = 5 + goal_y = 7.01 + pen_x = 36.0 + pen_y = 20.16 + + out_x = pitch_x + outside_add + out_y = pitch_y + outside_add + + FLAG_COORDS = { + # perimiter flags + # center flag + "c": (0, 0), + "ct": (0, pitch_y), + "cb": (0, -pitch_y), + # field boundary flags (on boundary lines) + "r": (pitch_x, 0), + "rt": (pitch_x, pitch_y), + "rb": (pitch_x, -pitch_y), + "l": (-pitch_x, 0), + "lt": (-pitch_x, pitch_y), + "lb": (-pitch_x, -pitch_y), + # goal flags ('t' and 'b' flags can change based on server parameter + # 'goal_width', but we leave their coords as the default values. + # TODO: make goal flag coords dynamic based on server_params + "grb": (pitch_x, -goal_y), + "gr": (pitch_x, 0), + "grt": (pitch_x, goal_y), + "glb": (-pitch_x, -goal_y), + "gl": (-pitch_x, 0), + "glt": (-pitch_x, goal_y), + # penalty flags + "prb": (pen_x, -pen_y), + "prc": (pen_x, 0), + "prt": (pen_x, pen_y), + "plb": (-pen_x, -pen_y), + "plc": (-pen_x, 0), + "plt": (-pen_x, pen_y), + "t0": (0, out_y), + "tr10": (10, out_y), + "tr20": (20, out_y), + "tr30": (30, out_y), + "tr40": (40, out_y), + "tr50": (50, out_y), + "tl10": (-10, out_y), + "tl20": (-20, out_y), + "tl30": (-30, out_y), + "tl40": (-40, out_y), + "tl50": (-50, out_y), + "b0": (0, -out_y), + "br10": (10, -out_y), + "br20": (20, -out_y), + "br30": (30, -out_y), + "br40": (40, -out_y), + "br50": (50, -out_y), + "bl10": (-10, -out_y), + "bl20": (-20, -out_y), + "bl30": (-30, -out_y), + "bl40": (-40, -out_y), + "bl50": (-50, -out_y), + "r0": (out_x, 0), + "rt10": (out_x, 10), + "rt20": (out_x, 20), + "rt30": (out_x, 30), + "rb10": (out_x, -10), + "rb20": (out_x, -20), + "rb30": (out_x, -30), + "l0": (-out_x, 0), + "lt10": (-out_x, 10), + "lt20": (-out_x, 20), + "lt30": (-out_x, 30), + "lb10": (-out_x, -10), + "lb20": (-out_x, -20), + "lb30": (-out_x, -30), + } + + def __init__(self, distance, direction, flag_id): + """ + Adds a flag id for this field object. Every flag has a unique id. + """ + + self.flag_id = flag_id + + GameObject.__init__(self, distance, direction) + + +class MobileObject(GameObject): + """ + Represents objects that can move. + """ + + def __init__(self, distance, direction, dist_change, dir_change, speed): + """ + Adds variables for distance and direction deltas. + """ + + self.dist_change = dist_change + self.dir_change = dir_change + self.speed = speed + + GameObject.__init__(self, distance, direction) + + +class Ball(MobileObject): + """ + A spcial instance of a mobile object representing the soccer ball. + """ + + def __init__(self, distance, direction, dist_change, dir_change, speed): + MobileObject.__init__(self, distance, direction, dist_change, dir_change, speed) + + +class Player(MobileObject): + """ + Represents a friendly or enemy player in the game. + """ + + def __init__( + self, + distance, + direction, + dist_change, + dir_change, + speed, + team, + side, + uniform_number, + body_direction, + neck_direction, + ): + """ + Adds player-specific information to a mobile object. + """ + + self.team = team + self.side = side + self.uniform_number = uniform_number + self.body_direction = body_direction + self.neck_direction = neck_direction + + MobileObject.__init__(self, distance, direction, dist_change, dir_change, speed) diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/handler.py b/mava/utils/environments/RoboCup_env/robocup_utils/handler.py new file mode 100755 index 000000000..052f0793f --- /dev/null +++ b/mava/utils/environments/RoboCup_env/robocup_utils/handler.py @@ -0,0 +1,706 @@ +# type: ignore +import collections +import queue + +from mava.utils.environments.RoboCup_env.robocup_utils import ( + game_object, + message_parser, + sp_exceptions, +) +from mava.utils.environments.RoboCup_env.robocup_utils.player_world_model import ( + WorldModel, +) + +# import time + + +# from multiprocessing import Queue as queue + + +# should we print messages received from the server? +PRINT_SERVER_MESSAGES = False + +# should we print commands sent to the server? +PRINT_SENT_COMMANDS = False + + +class MessageHandler: + """ + Handles all incoming messages from the server. Parses their data and puts + it into the given WorldModel. + + All '_handle_*' functions deal with their appropriate message types + as received from a server. This allows adding a message handler to be as + simple as adding a new '_handle_*' function to this object. + """ + + # an inner class used for creating named tuple 'hear' messages + Message = collections.namedtuple("Message", "time sender message") + + def __init__(self, world_model): + self.wm = world_model + + def handle_message(self, msg): + """ + Takes a raw message direct from the server, parses it, and stores its + data in the world and body model objects given at init. Returns the + type of message received. + """ + + # get all the expressions contained in the given message + parsed = message_parser.parse(msg) + + if parsed is not None: + if PRINT_SERVER_MESSAGES: + print(parsed[0] + ":", parsed[1:], "\n") + + # this is the name of the function that should be used to handle + # this message type. we pull it from this object dynamically to + # avoid having a huge if/elif/.../else statement. + + if parsed[0] != "ok" and parsed[0] != "think": + msg_func = "_handle_%s" % parsed[0] + + if hasattr(self, msg_func): + # call the appropriate function with this message + getattr(self, msg_func).__call__(parsed) + + # throw an exception if we don't know about the given message type + else: + m = "Can't handle message type '%s', function '%s' not found." + raise sp_exceptions.MessageTypeError(m % (parsed[0], msg_func)) + + # return the type of message received + return parsed[0] + else: + return parsed + + def _handle_see(self, msg): # noqa: C901 + """ + Parses visual information in a message and turns it into useful data. + + This comes to us as a list of lists. Each list contains another list as + its first element, the contents of which describe a particular object. + The other items of the list are data pertaining to the object. We parse + each list into its own game object, then insert those game objects into + the world model. + """ + + # the simulation cycle of the soccer server + # TODO: we should probably use this somewhere... + # sim_time = msg[1] + + # store new values before changing those in the world model. all new + # values replace those in the world model at the end of parsing. + new_ball = None + new_flags = [] + new_goals = [] + new_lines = [] + new_players = [] + + # iterate over all the objects given to us in the last see message + for obj in msg[2:]: + name = obj[0] + members = obj[1:] + + # get basic information from the object. different numbers of + # parameters (inconveniently) specify different types and + # arrangements of data received for the object. + + # default values for object data + distance = None + direction = None + dist_change = None + dir_change = None + body_dir = None + neck_dir = None + + # a single item object means only direction + if len(members) == 1: + direction = members[0] + + # objects with more items follow a regular pattern + elif len(members) >= 2: + distance = members[0] + direction = members[1] + + # include delta values if present + if len(members) >= 4: + dist_change = members[2] + dir_change = members[3] + + # include body/neck values if present + if len(members) >= 6: + body_dir = members[4] + neck_dir = members[5] + + # parse flags + if name[0] == "f": + # since the flag's name sometimes contains a number, the parser + # recognizes it as such and converts it into an int. it's + # always the last item when it's a number, so we stringify the + # last item of the name to convert any numbers back. + name[-1] = str(name[-1]) + + # the flag's id is its name's members following the f as a string + flag_id = "".join(name[1:]) + + new_flags.append(game_object.Flag(distance, direction, flag_id)) + + # parse players + elif name[0] == "p": + # extract any available information from the player object's name + teamname = None + uniform_number = None + + if len(name) >= 2: + teamname = name[1] + if len(name) >= 3: + uniform_number = name[2] + # if len(name) >= 4: + # position = name[3] + + # figure out the player's side + side = None + if teamname is not None: + # if they're on our team, they're on our side + if teamname == self.wm.teamname: + side = self.wm.side + # otherwise, set side to the other team's side + else: + if self.wm.side == WorldModel.SIDE_L: + side = WorldModel.SIDE_R + else: + side = WorldModel.SIDE_L + + # calculate player's speed + speed = None + # TODO: calculate player's speed! + + new_players.append( + game_object.Player( + distance, + direction, + dist_change, + dir_change, + speed, + teamname, + side, + uniform_number, + body_dir, + neck_dir, + ) + ) + + # parse goals + elif name[0] == "g": + # see if we know which side's goal this is + goal_id = None + if len(name) > 1: + goal_id = name[1] + + new_goals.append(game_object.Goal(distance, direction, goal_id)) + + # parse lines + elif name[0] == "l": + # see if we know which line this is + line_id = None + if len(name) > 1: + line_id = name[1] + + new_lines.append(game_object.Line(distance, direction, line_id)) + + # parse the ball + elif name[0] == "b": + # TODO: handle speed! + new_ball = game_object.Ball( + distance, direction, dist_change, dir_change, None + ) + + # object very near to but not viewable by the player are 'blank' + + # the out-of-view ball + elif name[0] == "B": + new_ball = game_object.Ball(None, None, None, None, None) + + # an out-of-view flag + elif name[0] == "F": + new_flags.append(game_object.Flag(None, None, None)) + + # an out-of-view goal + elif name[0] == "G": + new_goals.append(game_object.Goal(None, None, None)) + + # an out-of-view player + elif name[0] == "P": + new_players.append( + game_object.Player( + None, None, None, None, None, None, None, None, None, None + ) + ) + + # an unhandled object type + else: + raise ValueError("Unknown object: '" + str(obj) + "'") + + # tell the WorldModel to update any internal variables based on the + # newly gleaned information. + self.wm.process_new_info(new_ball, new_flags, new_goals, new_players, new_lines) + + def _handle_see_global(self, msg): + """ + Parses global visual information in a message and turns it into useful data. + + This comes to us as a list of lists. Each list contains another list as + its first element, the contents of which describe a particular object. + The other items of the list are data pertaining to the object. We parse + each list into its own game object, then insert those game objects into + the world model. + """ + + # the simulation cycle of the soccer server + # print("msg: ", msg) + # sim_time = msg[1] + + # store new values before changing those in the world model. all new + # values replace those in the world model at the end of parsing. + ball = None + goals = [] + players = [] + + # iterate over all the objects given to us in the last see message + for obj in msg[2:]: + name = obj[0] + info = obj[1:] + + # parse players + if name[0] == "p": + assert len(name) == 3 + teamname = name[1] + uniform_number = name[2] + + if len(info) == 6: + x, y, delta_x, delta_y, body_angle, neck_angle = info + elif len(info) == 7: + # TODO: Figure out what this extra is + x, y, delta_x, delta_y, body_angle, neck_angle, extra = info + assert extra == "t" + else: + raise NotImplementedError("Info size to large: ", len(info)) + + players.append( + { + "teamname": teamname, + "uniform_number": uniform_number, + "coords": (x, y), + "delta_coords": (delta_x, delta_y), + "body_angle": body_angle, + "neck_angle": neck_angle, + } + ) + + # parse goals + elif name[0] == "g": + assert len(name) == 2 + assert len(info) == 2 + + goal_side = name[1] + x, y = info + + goals.append({"side": goal_side, "coords": (x, y)}) + + # parse the ball + elif name[0] == "b": + assert len(name) == 1 + assert len(info) == 4 + + x, y, delta_x, delta_y = info + ball = {"coords": (x, y), "delta_coords": (delta_x, delta_y)} + else: + raise ValueError("Unknown object: '" + str(obj) + "'") + + # tell the WorldModel to update any internal variables based on the + # newly gleaned information. + self.wm.process_new_info(ball, goals, players) + self.wm.new_data = True + + def _handle_hear(self, msg): + """ + Parses audible information and turns it into useful information. + """ + + if str(msg[1]).isdigit(): + # Player + time_recvd = msg[1] # server cycle when message was heard + sender = msg[2] # name (or direction) of who sent the message + else: + # Trainer + time_recvd = msg[2] # server cycle when message was heard + sender = msg[1] # name (or direction) of who sent the message + + message = msg[3] # message string + # if message.startswith("goal_"): + # print("time_recvd: ", time_recvd) + # print("sender: ", sender) + # print("message: ", message) + + # ignore messages sent by self (NOTE: would anybody really want these?) + if sender == "self": + return + + # handle messages from the referee, to update game state + elif sender == "referee": + # change the name for convenience's sake + mode = message + + # deal first with messages that shouldn't be passed on to the agent + + # keep track of scores by setting them to the value reported. this + # precludes any possibility of getting out of sync with the server. + if mode.startswith(WorldModel.RefereeMessages.GOAL_L): + # split off the number, the part after the rightmost '_' + self.wm.score_l = int(mode.rsplit("_", 1)[1]) + return + elif mode.startswith(WorldModel.RefereeMessages.GOAL_R): + self.wm.score_r = int(mode.rsplit("_", 1)[1]) + return + + # ignore these messages, but pass them on to the agent. these don't + # change state but could still be useful. + elif ( + mode == WorldModel.RefereeMessages.FOUL_L + or mode == WorldModel.RefereeMessages.FOUL_R + or mode == WorldModel.RefereeMessages.GOALIE_CATCH_BALL_L + or mode == WorldModel.RefereeMessages.GOALIE_CATCH_BALL_R + or mode == WorldModel.RefereeMessages.TIME_UP_WITHOUT_A_TEAM + or mode == WorldModel.RefereeMessages.HALF_TIME + or mode == WorldModel.RefereeMessages.TIME_EXTENDED + ): + + # messages are named 3-tuples of (time, sender, message) + ref_msg = self.Message(time_recvd, sender, message) + + # pass this message on to the player and return + self.wm.last_message = ref_msg + return + + # deal with messages that indicate game mode, but that the agent + # doesn't need to know about specifically. + else: + # set the mode to the referee reported mode string + self.wm.play_mode = mode + return + + # all other messages are treated equally + else: + # update the model's last heard message + new_msg = MessageHandler.Message(time_recvd, sender, message) + self.wm.prev_message = new_msg + + def _handle_sense_body(self, msg): + """ + Deals with the agent's body model information. + """ + + # update the body model information when received. each piece of info is + # a list with the first item as the name of the data, and the rest as + # the values. + for info in msg[2:]: + name = info[0] + values = info[1:] + + if name == "view_mode": + self.wm.view_quality = values[0] + self.wm.view_width = values[1] + elif name == "stamina": + self.wm.stamina = values[0] + self.wm.effort = values[1] + elif name == "speed": + self.wm.speed_amount = values[0] + self.wm.speed_direction = values[1] + elif name == "head_angle": + self.wm.neck_direction = values[0] + + # these update the counts of the basic actions taken + elif name == "kick": + self.wm.kick_count = values[0] + elif name == "dash": + self.wm.dash_count = values[0] + elif name == "turn": + self.wm.turn_count = values[0] + elif name == "say": + self.wm.say_count = values[0] + elif name == "turn_neck": + self.wm.turn_neck_count = values[0] + elif name == "catch": + self.wm.catch_count = values[0] + elif name == "move": + self.wm.move_count = values[0] + elif name == "change_view": + self.wm.change_view_count = values[0] + + # we leave unknown values out of the equation + else: + pass + self.wm.new_data = True + + def _handle_change_player_type(self, msg): + """ + Handle player change messages. + """ + + def _handle_player_param(self, msg): + """ + Deals with player parameter information. + """ + + def _handle_player_type(self, msg): + """ + Handles player type information. + """ + + def _handle_server_param(self, msg): + """ + Stores server parameter information. + """ + + # each list is two items: a value name and its value. we add them all + # to the ServerParameters class inside WorldModel programmatically. + for param in msg[1:]: + # put all [param, value] pairs into the server settings object + # by setting the attribute programmatically. + if len(param) != 2: + continue + + # the parameter and its value + key = param[0] + value = param[1] + + # set the attribute if it was accounted for, otherwise alert the user + if hasattr(self.wm.server_parameters, key): + setattr(self.wm.server_parameters, key, value) + else: + raise AttributeError( + "Couldn't find a matching parameter in " + "ServerParameters class: '%s'" % key + ) + + def _handle_init(self, msg): + """ + Deals with initialization messages sent by the server. + """ + if len(msg) > 2: + # Player message + # set the player's uniform number, side, and the play mode as returned + # by the server directly after connecting. + side = msg[1] + uniform_number = msg[2] + play_mode = msg[3] + + self.wm.side = side + self.wm.uniform_number = uniform_number + self.wm.play_mode = play_mode + + def _handle_error(self, msg): + """ + Deals with error messages by raising them as exceptions. + """ + + m = "Server returned an error: '%s'" % msg[1] + raise sp_exceptions.SoccerServerError(m) + + def _handle_warning(self, msg): + """ + Deals with warnings issued by the server. + """ + + m = "Server issued a warning: '%s'" % msg[1] + print(sp_exceptions.SoccerServerWarning(m)) + + +class ActionHandler: + """ + Provides facilities for sending commands to the soccer server. Contains all + possible commands that can be sent, as well as everything needed to send + them. All basic command methods are aliases for placing that command in the + internal queue and sending it at the appropriate time. + """ + + class CommandType: + """ + A static class that defines all basic command types. + """ + + # whether the command can only be sent once per cycle or not + TYPE_PRIMARY = 0 + TYPE_SECONDARY = 1 + + # command types corresponding to valid commands to send to the server + CATCH = "catch" + CHANGE_VIEW = "change_view" + DASH = "dash" + KICK = "kick" + MOVE = "move" + SAY = "say" + SENSE_BODY = "sense_body" + TURN = "turn" + TURN_NECK = "turn_neck" + + def __init__(self): + raise NotImplementedError( + "Can't instantiate a CommandType, access " + "its members through ActionHandler instead." + ) + + # a command for our queue containing an id and command text + Command = collections.namedtuple("Command", "cmd_type text") + + def __init__(self, server_socket): + """ + Save the socket that connects us to the soccer server to allow us to + send it commands. + """ + + self.sock = server_socket + + # this contains all requested actions for the current and future cycles + self.q = queue.Queue() + + def send_commands(self): + """ + Sends all the enqueued commands. + """ + + # we only send the most recent primary command + primary_cmd = None + + # dequeue all enqueued commands and send them + while 1: + try: + cmd = self.q.get_nowait() + except queue.Empty: + break + + # save the most recent primary command and send it at the very end + if cmd.cmd_type == ActionHandler.CommandType.TYPE_PRIMARY: + primary_cmd = cmd + # send other commands immediately + else: + if PRINT_SENT_COMMANDS: + print("sent:", cmd.text, "\n") + + self.sock.send(cmd.text) + + # indicate that we finished processing a command + self.q.task_done() + + # send the saved primary command, if there was one + if primary_cmd is not None: + if PRINT_SENT_COMMANDS: + print("sent:", primary_cmd.text, "\n") + + self.sock.send(primary_cmd.text) + # self.__sock.send("(turn 30)\x00", append_null_terminator=False) + # time.sleep(0.01) + self.sock.send("(done)") + + def move(self, x, y): + """ + Teleport the player to some location on the field. Only works before + play begins, ie. pre-game, before starting again at half-time, and + post-goal. If an invalid location is specified, player is teleported to + a random location on their side of the field. + """ + + msg = "(move {:.10f} {:.10f})".format(x, y) + + # create the command object for insertion into the queue + cmd_type = ActionHandler.CommandType.TYPE_PRIMARY + cmd = ActionHandler.Command(cmd_type, msg) + + self.q.put(cmd) + + def turn(self, relative_degrees): + """ + Turns the player's body some number of degrees relative to its current + angle. + """ + + # disallow unreasonable turning + assert -180 <= relative_degrees <= 180 + + msg = "(turn %.10f)" % relative_degrees + + # create the command object for insertion into the queue + cmd_type = ActionHandler.CommandType.TYPE_PRIMARY + cmd = ActionHandler.Command(cmd_type, msg) + + self.q.put(cmd) + + def dash(self, power): + """ + Accelerate the player in the direction its body currently faces. + """ + + msg = "(dash %.10f)" % power + + # create the command object for insertion into the queue + cmd_type = ActionHandler.CommandType.TYPE_PRIMARY + cmd = ActionHandler.Command(cmd_type, msg) + + self.q.put(cmd) + + def kick(self, power, relative_direction): + """ + Accelerates the ball with the given power in the given direction, + relative to the current direction of the player's body. + """ + msg = "(kick {:.10f} {:.10f})".format(power, relative_direction) + + # create the command object for insertion into the queue + cmd_type = ActionHandler.CommandType.TYPE_PRIMARY + cmd = ActionHandler.Command(cmd_type, msg) + + self.q.put(cmd) + + def catch(self, relative_direction): + """ + Attempts to catch the ball and put it in the goalie's hand. The ball + remains there until the goalie kicks it away. + """ + + msg = "(catch %.10f)" % relative_direction + + # create the command object for insertion into the queue + cmd_type = ActionHandler.CommandType.TYPE_PRIMARY + cmd = ActionHandler.Command(cmd_type, msg) + + self.q.put(cmd) + + def say(self, message): + """ + Says something to other players on the field. Messages are restricted + in length, but that isn't enforced here. + """ + + msg = "(say %s)" % message + + # create the command object for insertion into the queue + cmd_type = ActionHandler.CommandType.TYPE_SECONDARY + cmd = ActionHandler.Command(cmd_type, msg) + + self.q.put(cmd) + + def turn_neck(self, relative_direction): + """ + Rotates the player's neck relative to its previous direction. Neck + angle is relative to body angle. + """ + + msg = "(turn_neck %.10f)" % relative_direction + + # create the command object for insertion into the queue + cmd_type = ActionHandler.CommandType.TYPE_SECONDARY + cmd = ActionHandler.Command(cmd_type, msg) + + self.q.put(cmd) diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/license.txt b/mava/utils/environments/RoboCup_env/robocup_utils/license.txt new file mode 100755 index 000000000..cc9a2a2f8 --- /dev/null +++ b/mava/utils/environments/RoboCup_env/robocup_utils/license.txt @@ -0,0 +1,19 @@ +Copyright (c) 2010 Jason T. Bradshaw (http://jasontbradshaw.com) + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in +all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +THE SOFTWARE. diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/message_parser.py b/mava/utils/environments/RoboCup_env/robocup_utils/message_parser.py new file mode 100755 index 000000000..c8eb24e20 --- /dev/null +++ b/mava/utils/environments/RoboCup_env/robocup_utils/message_parser.py @@ -0,0 +1,164 @@ +# type: ignore +import re + +# used to convert server value strings into actual python values +pattern_int = re.compile(r"^-?\d+$") +pattern_float = re.compile(r"^-?\d*[.]\d+$") + + +def parse(text): # noqa: C901 + """ + This is what amounts to a simple lisp parser for turning the server's + returned messages into an intermediate format that's easier to deal + with than the raw (often poorly formatted) text. + + This parses generally, taking any lisp-like string and turning it into a + list of nested lists, where each nesting indicates a parenthesized + expression. holding multiple top-level parenthesized expressions. Ex: "(baz + 0 (foo 1.5))" becomes ['baz', 0, ['foo', 1.5]]. + """ + + text = text.decode() # Added for python 3 + + # make sure all of our parenthesis match + if text.count("(") == text.count(")"): + # result acts as a stack that holds the strings grouped by nested parens. + # result will only ever contain one item, the first level of indenting + # encountered. this is because the server (hopefully!) only ever sends one + # message at a time. + # TODO: make sure that the server only ever sends one message at a time! + result = [] + + # the current level of indentation, used to append chars to correct level + indent = 0 + + # the non-indenting characters we find. these are kept in a buffer until + # we indent or dedent, and then are added to the current indent level all + # at once, for efficiency. + s = [] + + # whether we're currently in the middle of parsing a string + in_string = False + + # the last character seen, None to begin with + prev_c = None + + # raise ValueError("Message text has unmatching parenthesis: ", text) + for c in text: + # prevent parsing parens when inside a string (also ignores escaped + # '"'s as well). doesn't add the quotes so we don't have to recognize + # that value as a string via a regex. + if c == '"' and prev_c != "\\": + in_string = not in_string + + # we only indent/dedent if not in the middle of parsing a string + elif c == "(" and not in_string: + # recurse into current level of nesting + cur = result + for i in range(indent): + cur = cur[-1] + + # add our buffered string onto the previous level, then clear it + # for the next. + if len(s) > 0: + val = "".join(s) + + # try to convert our string into a value and append it to our + # list. failing that, simply append it as an attribute name. + if pattern_int.match(val): + cur.append(int(val)) + elif pattern_float.match(val): + cur.append(float(val)) + else: + cur.append(val) + + s = [] + + # append a new level of nesting to our list + cur.append([]) + + # increase the indent level so we can get back to this level later + indent += 1 + + elif c == ")" and not in_string: + # append remaining string buffer before dedenting + if len(s) > 0: + cur = result + for i in range(indent): + cur = cur[-1] + + val = "".join(s) + + # try to convert our string into a value and append it to our + # list. failing that, simply append it as an attribute name. + if pattern_int.match(val): + cur.append(int(val)) + elif pattern_float.match(val): + cur.append(float(val)) + else: + cur.append(val) + + s = [] + + # we finished with one level, so dedent back to the previous one + indent -= 1 + + # append non-space characters to the buffer list. spaces are delimiters + # for expressions, hence are special. + elif c != " ": + # append the current string character to the buffer list. + s.append(c) + + # we separate expressions by spaces + elif c == " " and len(s) > 0: + cur = result + for i in range(indent): + cur = cur[-1] + + val = "".join(s) + + # try to convert our string into a value and append it to our + # list. failing that, simply append it as an attribute name. + if pattern_int.match(val): + cur.append(int(val)) + elif pattern_float.match(val): + cur.append(float(val)) + else: + cur.append(val) + + s = [] + + # save the previous character. used to determine if c is escaped + prev_c = c + + # this returns the first and only message found. result is a list simply + # because it makes adding new levels of indentation simpler as it avoids + # the 'if result is None' corner case that would come up when trying to + # append the first '('. + return result[0] + else: + # print("Ignored messages because of unequal parenthesis") + return None + + +# if __name__ == "__main__": +# import sys + +# # interactive mode if any args were specified +# if len(sys.argv) > 2: +# from pprint import pprint + +# with open(sys.argv[1], "r") as f: +# for line in f: +# print("raw message:\n", line.strip()) +# print() +# print("parsed message:") +# pprint(parse(line.strip())) +# print("----") +# raw_input() +# print() +# else: +# # just parse the message file +# with open(sys.argv[1], "r") as f: +# for line in f: +# parse(line.strip()) diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/player_world_model.py b/mava/utils/environments/RoboCup_env/robocup_utils/player_world_model.py new file mode 100755 index 000000000..b6ac5942c --- /dev/null +++ b/mava/utils/environments/RoboCup_env/robocup_utils/player_world_model.py @@ -0,0 +1,697 @@ +# type: ignore +import math + +import numpy as np + +from mava.utils.environments.RoboCup_env.robocup_utils.game_object import Flag +from mava.utils.environments.RoboCup_env.robocup_utils.util_functions import ( + rad_rot_to_xy, +) + +true_flag_coords = Flag.FLAG_COORDS + + +def rotate(origin, point, angle): + """ + Rotate a point counterclockwise by a given angle around a given origin. + + The angle should be given in radians. + """ + + # print("origin: ", origin) + + ox, oy = origin + px, py = point + + qx = ox + math.cos(angle) * (px - ox) - math.sin(angle) * (py - oy) + qy = oy + math.sin(angle) * (px - ox) + math.cos(angle) * (py - oy) + return qx, qy + + +def rel_to_abs_coords(obj): + obj_x = obj.distance * math.sin(-obj.direction * math.pi / 180) + obj_y = -obj.distance * math.cos(-obj.direction * math.pi / 180) + return obj_x, obj_y + + +class WorldModel: + """ + Holds and updates the model of the world as known from current and past + data. + """ + + # constants for team sides + SIDE_L = "l" + SIDE_R = "r" + + class PlayModes: + """ + Acts as a static class containing variables for all valid play modes. + The string values correspond to what the referee calls the game modes. + """ + + BEFORE_KICK_OFF = "before_kick_off" + PLAY_ON = "play_on" + TIME_OVER = "time_over" + KICK_OFF_L = "kick_off_l" + KICK_OFF_R = "kick_off_r" + KICK_IN_L = "kick_in_l" + KICK_IN_R = "kick_in_r" + FREE_KICK_L = "free_kick_l" + FREE_KICK_R = "free_kick_r" + CORNER_KICK_L = "corner_kick_l" + CORNER_KICK_R = "corner_kick_r" + GOAL_KICK_L = "goal_kick_l" + GOAL_KICK_R = "goal_kick_r" + DROP_BALL = "drop_ball" + OFFSIDE_L = "offside_l" + OFFSIDE_R = "offside_r" + + def __init__(self): + raise NotImplementedError( + "Don't instantiate a PlayModes class," + " access it statically through WorldModel instead." + ) + + class RefereeMessages: + """ + Static class containing possible non-mode messages sent by a referee. + """ + + # these are referee messages, not play modes + FOUL_L = "foul_l" + FOUL_R = "foul_r" + GOALIE_CATCH_BALL_L = "goalie_catch_ball_l" + GOALIE_CATCH_BALL_R = "goalie_catch_ball_r" + TIME_UP_WITHOUT_A_TEAM = "time_up_without_a_team" + TIME_UP = "time_up" + HALF_TIME = "half_time" + TIME_EXTENDED = "time_extended" + + # these are special, as they are always followed by '_' and an int of + # the number of goals scored by that side so far. these won't match + # anything specifically, but goals WILL start with these. + GOAL_L = "goal_l_" + GOAL_R = "goal_r_" + + def __init__(self): + raise NotImplementedError( + "Don't instantiate a RefereeMessages class," + " access it statically through WorldModel instead." + ) + + def __init__(self, action_handler): + """ + Create the world model with default values and an ActionHandler class it + can use to complete requested actions. + """ + # we use the action handler to complete complex commands + self.ah = action_handler + + # these variables store all objects for any particular game step + self.ball = None + self.flags = [] + self.goals = [] + self.players = [] + self.lines = [] + + # the default position of this player, its home position + self.home_point = (None, None) + + # scores for each side + self.score_l = 0 + self.score_r = 0 + + # the name of the agent's team + self.teamname = None + + # handle player information, like uniform number and side + self.side = None + self.uniform_number = None + + # stores the most recent message heard + self.last_message = None + + # the mode the game is currently in (default to not playing yet) + self.play_mode = WorldModel.PlayModes.BEFORE_KICK_OFF + + # Obs updated + # self.obs_updated = False + + # body state + self.view_width = None + self.view_quality = None + self.stamina = None + self.effort = None + self.speed_amount = None + self.speed_direction = None + self.neck_direction = None + self.new_data = False + + # counts of actions taken so far + self.kick_count = None + self.dash_count = None + self.turn_count = None + self.say_count = None + self.turn_neck_count = None + self.catch_count = None + self.move_count = None + self.change_view_count = None + + # apparent absolute player coordinates and neck/body directions + self.abs_coords = (None, None) + self.abs_body_dir = None + self.abs_neck_dir = None + + # create a new server parameter object for holding all server params + self.server_parameters = ServerParameters() + + # def triangulate_direction(self, flags, flag_dict): + # """ + # Determines absolute view angle for the player given a list of visible + # flags. We find the absolute angle to each flag, then return the average + # of those angles. Returns 'None' if no angle could be determined. + # """ + # + # # average all flag angles together and save that as absolute angle + # abs_angles = [] + # for f in self.flags: + # # if the flag has useful data, consider it + # if f.distance is not None and f.flag_id in flag_dict: + # flag_point = flag_dict[f.flag_id] + # abs_dir = self.angle_between_points(self.abs_coords, flag_point) + # abs_angles.append(abs_dir) + # + # # return the average if available + # if len(abs_angles) > 0: + # return sum(abs_angles) / len(abs_angles) + # + # return None + + # def triangulate_position(self, flags, flag_dict, angle_step=36): + # """ + # Returns a best-guess position based on the triangulation via distances + # to all flags in the flag list given. 'angle_step' specifies the + # increments between angles for projecting points onto the circle + # surrounding a flag. + # """ + # + # points = [] + # for f in flags: + # # skip flags without distance information or without a specific id + # if f.distance is None or f.flag_id not in flag_dict: + # continue + # + # # generate points every 'angle_step' degrees around each flag, + # # discarding those off-field. + # for i in range(0, 360, angle_step): + # dy = f.distance * math.sin(math.radians(i)) + # dx = f.distance * math.cos(math.radians(i)) + # + # fcoords = flag_dict[f.flag_id] + # new_point = (fcoords[0] + dx, fcoords[1] + dy) + # + # # skip points with a coordinate outside the play boundaries + # if (new_point[0] > 60 or new_point[0] < -60 or + # new_point[1] < -40 or new_point[1] > 40): + # continue + # + # # add point to list of all points + # points.append(new_point) + # + # # get the dict of clusters mapped to centers + # clusters = self.cluster_points(points) + # + # # return the center that has the most points as an approximation to our + # # absolute position. + # center_with_most_points = (0, 0) + # max_points = 0 + # for c in clusters: + # if len(clusters[c]) > max_points: + # center_with_most_points = c + # max_points = len(clusters[c]) + # + # return center_with_most_points + + # def cluster_points(self, points, num_cluster_iterations=15): + # """ + # Cluster a set of points into a dict of centers mapped to point lists. + # Uses the k-means clustering algorithm with random initial centers and a + # fixed number of iterations to find clusters. + # """ + # + # # generate initial random centers, ignoring identical ones + # centers = set([]) + # for i in range(int(math.sqrt(len(points) / 2))): + # # a random coordinate somewhere within the field boundaries + # rand_center = (random.randint(-55, 55), random.randint(-35, 35)) + # centers.add(rand_center) + # + # # cluster for some iterations before the latest result + # latest = {} + # cur = {} + # for i in range(num_cluster_iterations): + # # initialze cluster lists + # for c in centers: + # cur[c] = [] + # + # # put every point into the list of its nearest cluster center + # for p in points: + # # get a list of (distance to center, center coords) tuples + # c_dists = map(lambda c: (self.euclidean_distance(c, p), c), + # centers) + # + # # find the smallest tuple's c (second item) + # nearest_center = min(c_dists)[1] + # + # # add point to this center's cluster + # cur[nearest_center].append(p) + # + # # recompute centers + # new_centers = set([]) + # for cluster in cur.values(): + # tot_x = 0 + # tot_y = 0 + # + # # remove empty clusters + # if len(cluster) == 0: + # continue + # + # # generate average center of cluster + # for p in cluster: + # tot_x += p[0] + # tot_y += p[1] + # + # # get average center and add to new centers set + # ave_center = (tot_x / len(cluster), tot_y / len(cluster)) + # new_centers.add(ave_center) + # + # # move on to next iteration + # centers = new_centers + # latest = cur + # cur = {} + # + # # return latest cluster iteration + # return latest + + # def euclidean_distance(self, point1, point2): + # """ + # Returns the Euclidean distance between two points on a plane. + # """ + # + # try: + # x1 = point1[0] + # y1 = point1[1] + # x2 = point2[0] + # y2 = point2[1] + # + # return math.sqrt((x1 - x2) ** 2 + (y1 - y2) ** 2) + # except: + # return 200 + + # def angle_between_points(self, point1, point2): + # """ + # Returns the angle from the first point to the second, assuming that + # these points exist on a plane, and that the positive x-axis is 0 degrees + # and the positive y-axis is 90 degrees. All returned angles are positive + # and relative to the positive x-axis. + # """ + # + # try: + # x1 = point1[0] + # y1 = point1[1] + # x2 = point2[0] + # y2 = point2[1] + # + # # get components of vector between the points + # dx = x2 - x1 + # dy = y2 - y1 + # + # # return the angle in degrees + # a = math.degrees(math.atan2(dy, dx)) + # if a < 0: + # a = 360 + a + # + # return a + # except: + # return 0 + + def __calculate_abs_info(self): + if self.flags is not None: + rel_coords = [] + true_coords = [] + for flag in self.flags: + if ( + flag is not None + and flag.direction is not None + and flag.distance is not None + and flag.flag_id is not None + ): + obs_coords = rel_to_abs_coords(flag) + rel_coords.append(obs_coords) + true_coords.append(true_flag_coords[flag.flag_id]) + + for goal in self.goals: + if ( + goal is not None + and goal.direction is not None + and goal.distance is not None + and goal.goal_id is not None + ): + obs_coords = rel_to_abs_coords(goal) + rel_coords.append(obs_coords) + true_coords.append(true_flag_coords[goal.goal_id]) + + if len(true_coords) > 1 and len(rel_coords) > 1: + # Get means + rel_mean = np.mean(rel_coords, axis=0) + true_mean = np.mean(true_coords, axis=0) + + mean_off = rel_mean - true_mean + + # Get rotation + rel_de_mean = np.array(rel_coords) - rel_mean + true_de_mean = np.array(true_coords) - true_mean + + if len(true_de_mean) > 1: + ang_offs = np.arctan2( + true_de_mean[:, 1], true_de_mean[:, 0] + ) - np.arctan2(rel_de_mean[:, 1], rel_de_mean[:, 0]) + x, y = rad_rot_to_xy(ang_offs) + + x_mean = np.mean(x) + y_mean = np.mean(y) + + ang_offs = np.arctan2(y_mean, x_mean) + + true_agent_loc = rotate(rel_mean, (0, 0), ang_offs) - mean_off + self.abs_coords = true_agent_loc + self.abs_body_dir = (ang_offs / math.pi) * 180 + + # TODO: Is this correct? + self.abs_neck_dir = self.abs_body_dir + self.neck_direction + + def process_new_info(self, ball, flags, goals, players, lines): + """ + Update any internal variables based on the currently available + information. This also calculates information not available directly + from server-reported messages, such as player coordinates. + """ + + # update basic information + self.ball = ball + self.flags = flags + self.goals = goals + self.players = players + self.lines = lines + + self.__calculate_abs_info() + + def is_playon(self): + """ + Tells us whether it's play time + """ + return ( + self.play_mode == WorldModel.PlayModes.PLAY_ON + or self.play_mode == WorldModel.PlayModes.KICK_OFF_L + or self.play_mode == WorldModel.PlayModes.KICK_OFF_R + or self.play_mode == WorldModel.PlayModes.KICK_IN_L + or self.play_mode == WorldModel.PlayModes.KICK_IN_R + or self.play_mode == WorldModel.PlayModes.FREE_KICK_L + or self.play_mode == WorldModel.PlayModes.FREE_KICK_R + or self.play_mode == WorldModel.PlayModes.CORNER_KICK_L + or self.play_mode == WorldModel.PlayModes.CORNER_KICK_R + or self.play_mode == WorldModel.PlayModes.GOAL_KICK_L + or self.play_mode == WorldModel.PlayModes.GOAL_KICK_R + or self.play_mode == WorldModel.PlayModes.DROP_BALL + or self.play_mode == WorldModel.PlayModes.OFFSIDE_L + or self.play_mode == WorldModel.PlayModes.OFFSIDE_R + ) + + def is_before_kick_off(self): + """ + Tells us whether the game is in a pre-kickoff state. + """ + + return self.play_mode == WorldModel.PlayModes.BEFORE_KICK_OFF + + def is_kick_off_us(self): + """ + Tells us whether it's our turn to kick off. + """ + + ko_left = WorldModel.PlayModes.KICK_OFF_L + ko_right = WorldModel.PlayModes.KICK_OFF_R + + # print self.play_mode + + # return whether we're on the side that's kicking off + return ( + self.side == WorldModel.SIDE_L + and self.play_mode == ko_left + or self.side == WorldModel.SIDE_R + and self.play_mode == ko_right + ) + + def is_dead_ball_them(self): + """ + Returns whether the ball is in the other team's posession and it's a + free kick, corner kick, or kick in. + """ + + # shorthand for verbose constants + kil = WorldModel.PlayModes.KICK_IN_L + kir = WorldModel.PlayModes.KICK_IN_R + fkl = WorldModel.PlayModes.FREE_KICK_L + fkr = WorldModel.PlayModes.FREE_KICK_R + ckl = WorldModel.PlayModes.CORNER_KICK_L + ckr = WorldModel.PlayModes.CORNER_KICK_R + + # shorthand for whether left team or right team is free to act + pm = self.play_mode + free_left = pm == kil or pm == fkl or pm == ckl + free_right = pm == kir or pm == fkr or pm == ckr + + # return whether the opposing side is in a dead ball situation + if self.side == WorldModel.SIDE_L: + return free_right + else: + return free_left + + def is_ball_kickable(self): + """ + Tells us whether the ball is in reach of the current player. + """ + + # ball must be visible, not behind us, and within the kickable margin + return ( + self.ball is not None + and self.ball.distance is not None + and self.ball.distance <= self.server_parameters.kickable_margin + ) + + def get_ball_speed_max(self): + """ + Returns the maximum speed the ball can be kicked at. + """ + + return self.server_parameters.ball_speed_max + + def get_stamina(self): + """ + Returns the agent's current stamina amount. + """ + + return self.stamina + + def get_stamina_max(self): + """ + Returns the maximum amount of stamina a player can have. + """ + + return self.server_parameters.stamina_max + + def turn_body_to_object(self, obj): + """ + Turns the player's body to face a particular object. + """ + + self.ah.turn(obj.direction) + + +class ServerParameters: + """ + A storage container for all the settings of the soccer server. + """ + + def __init__(self): + """ + Initialize default parameters for a server. + """ + + self.audio_cut_dist = 50 + self.auto_mode = 0 + self.back_passes = 1 + self.ball_accel_max = 2.7 + self.ball_decay = 0.94 + self.ball_rand = 0.05 + self.ball_size = 0.085 + self.ball_speed_max = 2.7 + self.ball_stuck_area = 3 + self.ball_weight = 0.2 + self.catch_ban_cycle = 5 + self.catch_probability = 1 + self.catchable_area_l = 2 + self.catchable_area_w = 1 + self.ckick_margin = 1 + self.clang_advice_win = 1 + self.clang_define_win = 1 + self.clang_del_win = 1 + self.clang_info_win = 1 + self.clang_mess_delay = 50 + self.clang_mess_per_cycle = 1 + self.clang_meta_win = 1 + self.clang_rule_win = 1 + self.clang_win_size = 300 + self.coach = 0 + self.coach_port = 6001 + self.coach_w_referee = 0 + self.connect_wait = 300 + self.control_radius = 2 + self.dash_power_rate = 0.006 + self.drop_ball_time = 200 + self.effort_dec = 0.005 + self.effort_dec_thr = 0.3 + self.effort_inc = 0.01 + self.effort_inc_thr = 0.6 + self.effort_init = 1 + self.effort_min = 0.6 + self.forbid_kick_off_offside = 1 + self.free_kick_faults = 1 + self.freeform_send_period = 20 + self.freeform_wait_period = 600 + self.fullstate_l = 0 + self.fullstate_r = 0 + self.game_log_compression = 0 + self.game_log_dated = 1 + self.game_log_dir = "./" + self.game_log_fixed = 0 + self.game_log_fixed_name = "rcssserver" + self.game_log_version = 3 + self.game_logging = 1 + self.game_over_wait = 100 + self.goal_width = 14.02 + self.goalie_max_moves = 2 + self.half_time = 300 + self.hear_decay = 1 + self.hear_inc = 1 + self.hear_max = 1 + self.inertia_moment = 5 + self.keepaway = 0 + self.keepaway_length = 20 + self.keepaway_log_dated = 1 + self.keepaway_log_dir = "./" + self.keepaway_log_fixed = 0 + self.keepaway_log_fixed_name = "rcssserver" + self.keepaway_logging = 1 + self.keepaway_start = -1 + self.keepaway_width = 20 + self.kick_off_wait = 100 + self.kick_power_rate = 0.027 + self.kick_rand = 0 + self.kick_rand_factor_l = 1 + self.kick_rand_factor_r = 1 + self.kickable_margin = 0.7 + self.landmark_file = "~/.rcssserver-landmark.xml" + self.log_date_format = "%Y%m%d%H%M-" + self.log_times = 0 + self.max_goal_kicks = 3 + self.maxmoment = 180 + self.maxneckang = 90 + self.maxneckmoment = 180 + self.maxpower = 100 + self.minmoment = -180 + self.minneckang = -90 + self.minneckmoment = -180 + self.minpower = -100 + self.nr_extra_halfs = 2 + self.nr_normal_halfs = 2 + self.offside_active_area_size = 2.5 + self.offside_kick_margin = 9.15 + self.olcoach_port = 6002 + self.old_coach_hear = 0 + self.pen_allow_mult_kicks = 1 + self.pen_before_setup_wait = 30 + self.pen_coach_moves_players = 1 + self.pen_dist_x = 42.5 + self.pen_max_extra_kicks = 10 + self.pen_max_goalie_dist_x = 14 + self.pen_nr_kicks = 5 + self.pen_random_winner = 0 + self.pen_ready_wait = 50 + self.pen_setup_wait = 100 + self.pen_taken_wait = 200 + self.penalty_shoot_outs = 1 + self.player_accel_max = 1 + self.player_decay = 0.4 + self.player_rand = 0.1 + self.player_size = 0.3 + self.player_speed_max = 1.2 + self.player_weight = 60 + self.point_to_ban = 5 + self.point_to_duration = 20 + self.port = 6000 + self.prand_factor_l = 1 + self.prand_factor_r = 1 + self.profile = 0 + self.proper_goal_kicks = 0 + self.quantize_step = 0.1 + self.quantize_step_l = 0.01 + self.record_messages = 0 + self.recover_dec = 0.002 + self.recover_dec_thr = 0.3 + self.recover_init = 1 + self.recover_min = 0.5 + self.recv_step = 10 + self.say_coach_cnt_max = 128 + self.say_coach_msg_size = 128 + self.say_msg_size = 10 + self.send_comms = 0 + self.send_step = 150 + self.send_vi_step = 100 + self.sense_body_step = 100 + self.simulator_step = 100 + self.slow_down_factor = 1 + self.slowness_on_top_for_left_team = 1 + self.slowness_on_top_for_right_team = 1 + self.stamina_inc_max = 45 + self.stamina_max = 4000 + self.start_goal_l = 0 + self.start_goal_r = 0 + self.stopped_ball_vel = 0.01 + self.synch_micro_sleep = 1 + self.synch_mode = 0 + self.synch_offset = 60 + self.tackle_back_dist = 0.5 + self.tackle_cycles = 10 + self.tackle_dist = 2 + self.tackle_exponent = 6 + self.tackle_power_rate = 0.027 + self.tackle_width = 1 + self.team_actuator_noise = 0 + self.text_log_compression = 0 + self.text_log_dated = 1 + self.text_log_dir = "./" + self.text_log_fixed = 0 + self.text_log_fixed_name = "rcssserver" + self.text_logging = 1 + self.use_offside = 1 + self.verbose = 0 + self.visible_angle = 90 + self.visible_distance = 3 + self.wind_ang = 0 + self.wind_dir = 0 + self.wind_force = 0 + self.wind_none = 0 + self.wind_rand = 0 + self.wind_random = 0 diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/sock.py b/mava/utils/environments/RoboCup_env/robocup_utils/sock.py new file mode 100755 index 000000000..7a6f23c05 --- /dev/null +++ b/mava/utils/environments/RoboCup_env/robocup_utils/sock.py @@ -0,0 +1,46 @@ +# type: ignore +import socket + + +class Socket: + """ + Handles the barest level of UDP communication with a server in a slightly + simpler way (for our purposes) than the default socket library. + """ + + def __init__(self, host, port, bufsize=8192): + """ + host: hostname of the server we want to connect to + port: port of the server we want to connect to + """ + + self.address = (host, port) + self.bufsize = bufsize + + # the socket communication with the server takes place on (ipv4, udp) + self.sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) + + def send(self, msg, append_null_terminator=True): + """ + Sends a message to the server. Appends a null terminator by default. + """ + + # append a null terminator if requested + if append_null_terminator: + msg = msg + "\0" + + self.sock.sendto(str.encode(msg), self.address) + + def recv(self, conform_address=True): + """ + Receives data from the given socket. Returns the data as a string. + If conform_address is True, the address the server sent its response + from replaces the address and port set at object creation. + """ + + data, address = self.sock.recvfrom(self.bufsize) + + if conform_address: + self.address = address + + return data diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/sp_exceptions.py b/mava/utils/environments/RoboCup_env/robocup_utils/sp_exceptions.py new file mode 100755 index 000000000..5d01b8eeb --- /dev/null +++ b/mava/utils/environments/RoboCup_env/robocup_utils/sp_exceptions.py @@ -0,0 +1,36 @@ +class SoccerServerError(Exception): + """ + Represents an error message returned by the soccer server. + """ + + +class SoccerServerWarning(Exception): + """ + Represents a warning message returned by the soccer server. + """ + + +class MessageTypeError(Exception): + """ + An exception for an unknown message type received from the soccer server. + """ + + +class AgentAlreadyPlayingError(Exception): + """ + Raised when a user calls an agent's play method after it has already started + playing. + """ + + +class ObjectTypeError(Exception): + """ + Raised when an unknown object type is encountered in a sense message. + """ + + +class AgentConnectionStateError(Exception): + """ + Raised when methods are called at an inappropriate time relative to the + connection state of the agent object. + """ diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/trainer.py b/mava/utils/environments/RoboCup_env/robocup_utils/trainer.py new file mode 100755 index 000000000..c086114c6 --- /dev/null +++ b/mava/utils/environments/RoboCup_env/robocup_utils/trainer.py @@ -0,0 +1,379 @@ +# type: ignore +# !/usr/bin/env python3 + +# import os +import random +import threading +import time + +import numpy as np + +from mava.utils.environments.RoboCup_env.robocup_utils import ( + handler, + sock, + sp_exceptions, +) +from mava.utils.environments.RoboCup_env.robocup_utils.game_object import Flag +from mava.utils.environments.RoboCup_env.robocup_utils.trainer_world_model import ( + WorldModel, +) + +max_x_rand = Flag.out_x +max_y_rand = Flag.out_y + +# Define some colours +green = (0, 255, 50) +light_blue = (0, 255, 255) +blue = (0, 0, 255) +red = (255, 0, 0) +yellow = (255, 255, 0) +white = (255, 255, 255) + +# R G B +field_col = (0, 200, 50) +team_player = blue +opp_player = light_blue +own_goal = (255, 140, 0) +opp_goal = (128, 0, 128) + + +class Trainer: + def __init__(self): + # whether we're connected to a server yet or not + self.__connected = False + + # set all variables and important objects to appropriate values for + # pre-connect state. + + # the socket used to communicate with the server + self.__sock = None + + # models and the message handler for parsing and storing information + self.wm = None + self.msg_handler = None + + # parse thread and control variable + self.__parsing = False + self.__msg_thread = None + + self.__thinking = False # think thread and control variable + self.__think_thread = None + + # whether we should run the think method + self.__should_think_on_data = False + + # whether we should send commands + self.__send_commands = True + + # adding goal post markers + self.enemy_goal_pos = None + self.own_goal_pos = None + + def connect(self, host, port, version=11): + """ + Gives us a connection to the server as one player on a team. This + immediately connects the agent to the server and starts receiving and + parsing the information it sends. + """ + + # if already connected, raise an error since user may have wanted to + # connect again to a different server. + if self.__connected: + msg = "Cannot connect while already connected, disconnect first." + raise sp_exceptions.AgentConnectionStateError(msg) + + # the pipe through which all of our communication takes place + self.__sock = sock.Socket(host, port) + + # our models of the world and our body + self.wm = WorldModel(handler.ActionHandler(self.__sock)) + + # handles all messages received from the server + self.msg_handler = handler.MessageHandler(self.wm) + + # set up our threaded message receiving system + self.__parsing = True # tell thread that we're currently running + self.__msg_thread = threading.Thread( + target=self.__message_loop, name="message_loop" + ) + self.__msg_thread.daemon = True # dies when parent thread dies + + # start processing received messages. this will catch the initial server + # response and all subsequent communication. + self.__msg_thread.start() + + # send the init message and allow the message handler to handle further + # responses. + # init_address = self.__sock.address + # teamname = "John" + init_msg = "(init (version %d))" + self.__sock.send(init_msg % (version)) + + # # wait until the socket receives a response from the server and gets its + # # assigned port. + # while self.__sock.address == init_address: + # time.sleep(0.0001) + + # create our thinking thread. this will perform the actions necessary + # to play a game of robo-soccer. + self.__thinking = False + # self.__think_thread = threading.Thread(target=self.__think_loop, + # name="think_loop") + # self.__think_thread.daemon = True + + # set connected state. done last to prevent state inconsistency if + # something goes wrong beforehand. + self.__connected = True + time.sleep(1) + self.__sock.send("(eye on)") + self.__sock.send("(ear on)") + + def run(self): + """ + Kicks off the thread that does the agent's thinking, allowing it to play + during the game. Throws an exception if called while the agent is + already playing. + """ + + # ensure we're connected before doing anything + if not self.__connected: + msg = "Must be connected to a server to begin play." + raise sp_exceptions.AgentConnectionStateError(msg) + + # throw exception if called while thread is already running + if self.__thinking: + raise sp_exceptions.AgentAlreadyPlayingError("Agent is already playing.") + + # run the method that sets up the agent's persistant variables + self.setup_environment() + + # tell the thread that it should be running, then start it + self.__thinking = True + self.__should_think_on_data = True + self.__think_loop() + + # def disconnect(self): + # """ + # Tell the loop threads to stop and signal the server that we're + # disconnecting, then join the loop threads and destroy all our inner + # methods. + + # Since the message loop thread can conceiveably block indefinitely while + # waiting for the server to respond, we only allow it (and the think loop + # for good measure) a short time to finish before simply giving up. + + # Once an agent has been disconnected, it is 'dead' and cannot be used + # again. All of its methods get replaced by a method that raises an + # exception every time it is called. + # """ + + # # don't do anything if not connected + # if not self.__connected: + # return + + # # tell the loops to terminate + # self.__parsing = False + # self.__thinking = False + + # # tell the server that we're quitting + # self.__sock.send("(bye)") + + # # tell our threads to join, but only wait breifly for them to do so. + # # don't join them if they haven't been started (this can happen if + # # disconnect is called very quickly after connect). + # if self.__msg_thread.is_alive(): + # self.__msg_thread.join(0.01) + + # # if self.__think_thread.is_alive(): + # # self.__think_thread.join(0.01) + + # # reset all standard variables in this object. self.__connected gets + # # reset here, along with all other non-user defined internal variables. + # Agent.__init__(self) + + def __message_loop(self): + """ + Handles messages received from the server. + + This SHOULD NOT be called externally, since it's used as a threaded loop + internally by this object. Calling it externally is a BAD THING! + """ + + # loop until we're told to stop + while self.__parsing: + # receive message data from the server and pass it along to the + # world model as-is. the world model parses it and stores it within + # itself for perusal at our leisure. + raw_msg = self.__sock.recv() + + msg_type = self.msg_handler.handle_message(raw_msg) + + # if b'goal_l' in raw_msg or b'goal_r' in raw_msg: + # print("Trainer message: '" , raw_msg, + # ". Scores: ", self.wm.score_l, self.wm.score_r) + # exit() + + # if msg_type is not None: + # print(type(raw_msg)) + + # we send commands all at once every cycle, ie. whenever a + # 'sense_body' command is received + if msg_type == handler.ActionHandler.CommandType.SENSE_BODY: + self.__send_commands = True + + # flag new data as needing the think loop's attention + self.__should_think_on_data = True + + # def __think_loop(self): + # """ + # Performs world model analysis and sends appropriate commands to the + # server to allow the agent to participate in the current game. + # + # Like the message loop, this SHOULD NOT be called externally. Use the + # play method to start play, and the disconnect method to end it. + # """ + # + # while self.__thinking: + # # tell the ActionHandler to send its enqueued messages if it is time + # if self.__send_commands: + # self.__send_commands = False + # self.wm.ah.send_commands() + # + # # only think if new data has arrived + # if self.__should_think_on_data: + # # flag that data has been processed. this shouldn't be a race + # # condition, since the only change would be to make it True + # # before changing it to False again, and we're already going to + # # process data, so it doesn't make any difference. + # self.__should_think_on_data = False + # + # self.think() + # else: + # # prevent from burning up all the cpu time while waiting for data + # time.sleep(0.0001) + + def setup_environment(self): + """ + Called before the think loop starts, this allows the user to store any + variables/objects they'll want access to across subsequent calls to the + think method. + """ + + self.in_kick_off_formation = False + + def send_done(self): + self.__sock.send("(done)") + + def reset_game( + self, + players_per_team, + team_names, + game_setting, + game_diff=None, + reset_stamina=True, + ): + self.__sock.send("(change_mode before_kick_off)") + if reset_stamina: + self.__sock.send("(recover)") + # github.com/rcsoccersim/rcssserver/blob/master/src/coach.cpp + + # game_easy = 1 - game_diff + if game_setting == "domain_randomisation": + # print("game_diff: ", game_diff) + assert game_diff is not None + + # Move the ball + + if players_per_team[0] > 0 and players_per_team[1] > 0: + rand_side = random.randint(0, 1) * 2 - 1 + elif players_per_team[0] > 0: + rand_side = 1 + else: + rand_side = 0 + s_x = 52 + # TODONE: Change this back to the harder setting! + x = str( + np.random.randint( + s_x - (s_x + max_x_rand) * game_diff, + s_x - (s_x - max_x_rand) * game_diff + 1, + ) + * rand_side + ) + # str(np.random.randint(s_x - (s_x + max_x_rand)*game_diff, + # s_x - (s_x - max_x_rand)*game_diff + 1)*rand_side) + # str(s_x*rand_side) + y = str( + np.random.randint(-max_y_rand * game_diff, 1 + max_y_rand * game_diff) + ) + # str(np.random.randint(-max_y_rand*game_diff, 1 + max_y_rand*game_diff)) + # str(0) # + self.__sock.send("(move (ball) " + x + " " + y + ")") + for t_i in range(len(players_per_team)): + for p_i in range(1, players_per_team[t_i] + 1): + s_x = 51 + x = str( + np.random.randint( + s_x - (s_x + max_x_rand) * game_diff, + s_x - (s_x - max_x_rand) * game_diff + 1, + ) + * (1 - t_i * 2) + ) + y = str( + np.random.randint( + -max_y_rand * game_diff, 1 + max_y_rand * game_diff + ) + ) + ang = str(np.random.randint(-180 * game_diff, 1 + 180 * game_diff)) + + command = ( + "(move (player " + + team_names[t_i] + + " " + + str(p_i) + + ") " + + str(x) + + " " + + str(y) + + " " + + str(ang) + + " 0 0)" + ) + self.__sock.send(command) + elif game_setting == "reward_shaping": + # Move the ball + x = str(np.random.randint(-max_x_rand, max_x_rand + 1)) + y = str(np.random.randint(-max_y_rand, max_y_rand + 1)) + self.__sock.send("(move (ball) " + x + " " + y + ")") + + for t, team_name in enumerate(team_names): + omt = 1 - t + for p_i in range(1, players_per_team[0] + 1): + x = str(np.random.randint(-51 * omt, 51 * t + 1)) + y = str(np.random.randint(-max_y_rand, max_y_rand + 1)) + ang = str(np.random.randint(-180, 180 + 1)) + + command = ( + "(move (player " + + team_name + + " " + + str(p_i) + + ") " + + str(x) + + " " + + str(y) + + " " + + str(ang) + + " 0 0)" + ) + self.__sock.send(command) + + else: + raise NotImplementedError( + "Game setting not implemented in trainer: ", game_setting + ) + self.__sock.send("(change_mode play_on)") + self.__sock.send("(start)") + self.__sock.send("(done)") + + def get_state_dict(self): + return self.wm.get_state() diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/trainer_world_model.py b/mava/utils/environments/RoboCup_env/robocup_utils/trainer_world_model.py new file mode 100755 index 000000000..a4e3f6ad2 --- /dev/null +++ b/mava/utils/environments/RoboCup_env/robocup_utils/trainer_world_model.py @@ -0,0 +1,434 @@ +# type: ignore +# import math + +# import random + +# from mava.utils.environments.RoboCup_env.robocup_utils import ( +# game_object, +# message_parser, +# sp_exceptions, +# ) + + +class WorldModel: + """ + Holds and updates the model of the world as known from current and past + data. + """ + + class PlayModes: + """ + Acts as a static class containing variables for all valid play modes. + The string values correspond to what the referee calls the game modes. + """ + + BEFORE_KICK_OFF = "before_kick_off" + PLAY_ON = "play_on" + TIME_OVER = "time_over" + KICK_OFF_L = "kick_off_l" + KICK_OFF_R = "kick_off_r" + KICK_IN_L = "kick_in_l" + KICK_IN_R = "kick_in_r" + FREE_KICK_L = "free_kick_l" + FREE_KICK_R = "free_kick_r" + CORNER_KICK_L = "corner_kick_l" + CORNER_KICK_R = "corner_kick_r" + GOAL_KICK_L = "goal_kick_l" + GOAL_KICK_R = "goal_kick_r" + DROP_BALL = "drop_ball" + OFFSIDE_L = "offside_l" + OFFSIDE_R = "offside_r" + + def __init__(self): + raise NotImplementedError( + "Don't instantiate a PlayModes class," + " access it statically through WorldModel instead." + ) + + class RefereeMessages: + """ + Static class containing possible non-mode messages sent by a referee. + """ + + # these are referee messages, not play modes + FOUL_L = "foul_l" + FOUL_R = "foul_r" + GOALIE_CATCH_BALL_L = "goalie_catch_ball_l" + GOALIE_CATCH_BALL_R = "goalie_catch_ball_r" + TIME_UP_WITHOUT_A_TEAM = "time_up_without_a_team" + TIME_UP = "time_up" + HALF_TIME = "half_time" + TIME_EXTENDED = "time_extended" + + # these are special, as they are always followed by '_' and an int of + # the number of goals scored by that side so far. these won't match + # anything specifically, but goals WILL start with these. + GOAL_L = "goal_l_" + GOAL_R = "goal_r_" + + def __init__(self): + raise NotImplementedError( + "Don't instantiate a RefereeMessages class," + " access it statically through WorldModel instead." + ) + + def __init__(self, action_handler): + """ + Create the world model with default values and an ActionHandler class it + can use to complete requested actions. + """ + # we use the action handler to complete complex commands + self.ah = action_handler + + # these variables store all objects for any particular game step + self.ball = None + self.goals = [] + self.players = [] + + # scores for each side + self.score_l = 0 + self.score_r = 0 + + # the name of the agent's team + self.teamnames = [None, None] + + # stores the most recent message heard + self.last_message = None + + self.new_global_data = False + + # the mode the game is currently in (default to not playing yet) + self.play_mode = WorldModel.PlayModes.BEFORE_KICK_OFF + + # create a new server parameter object for holding all server params + self.server_parameters = ServerParameters() + + # def euclidean_distance(self, point1, point2): + # """ + # Returns the Euclidean distance between two points on a plane. + # """ + + # if point1 is not None and point1[0] is not None and + # point1[1] is not None + # and point2 is not None and point2[0] is not None and + # point2[1] is not None: + # x1 = point1[0] + # y1 = point1[1] + # x2 = point2[0] + # y2 = point2[1] + + # return math.sqrt((x1 - x2) ** 2 + (y1 - y2) ** 2) + # except: + # return 200 + + def process_new_info(self, ball, goals, players): + """ + Update any internal variables based on the currently available + information. This also calculates information not available directly + from server-reported messages, such as player coordinates. + """ + + # update basic information + # Fix state + # TODO: Fix state offset problems. Why is the state values different? + + # Correct ball y coords + ball["coords"] = (float(ball["coords"][0]), -float(ball["coords"][1])) + ball["delta_coords"] = ( + float(ball["delta_coords"][0]), + -float(ball["delta_coords"][1]), + ) + for i in range(len(players)): + # Correct y coords + players[i]["coords"] = ( + float(players[i]["coords"][0]), + -float(players[i]["coords"][1]), + ) + players[i]["delta_coords"] = ( + float(players[i]["delta_coords"][0]), + -float(players[i]["delta_coords"][1]), + ) + # Correct body angle + players[i]["body_angle"] = -float(players[i]["body_angle"]) + 90 + + # Correct neck angle + players[i]["neck_angle"] = -float(players[i]["neck_angle"]) + 90 + + # Get side + team_to_id = {"Team_A": 0, "Team_B": 1} + players[i]["side"] = team_to_id[players[i]["teamname"]] + + self.ball = ball + self.goals = goals + self.players = players + + def get_state(self): + return {"ball": self.ball, "players": self.players} + + def is_playon(self): + """ + Tells us whether it's play time + """ + return ( + self.play_mode == WorldModel.PlayModes.PLAY_ON + or self.play_mode == WorldModel.PlayModes.KICK_OFF_L + or self.play_mode == WorldModel.PlayModes.KICK_OFF_R + or self.play_mode == WorldModel.PlayModes.KICK_IN_L + or self.play_mode == WorldModel.PlayModes.KICK_IN_R + or self.play_mode == WorldModel.PlayModes.FREE_KICK_L + or self.play_mode == WorldModel.PlayModes.FREE_KICK_R + or self.play_mode == WorldModel.PlayModes.CORNER_KICK_L + or self.play_mode == WorldModel.PlayModes.CORNER_KICK_R + or self.play_mode == WorldModel.PlayModes.GOAL_KICK_L + or self.play_mode == WorldModel.PlayModes.GOAL_KICK_R + or self.play_mode == WorldModel.PlayModes.DROP_BALL + or self.play_mode == WorldModel.PlayModes.OFFSIDE_L + or self.play_mode == WorldModel.PlayModes.OFFSIDE_R + ) + + def is_before_kick_off(self): + """ + Tells us whether the game is in a pre-kickoff state. + """ + print("is_before_kick_off!") + return self.play_mode == WorldModel.PlayModes.BEFORE_KICK_OFF + + def is_kick_off_us(self): + """ + Tells us whether it's our turn to kick off. + """ + print("is_kick_off_us!") + ko_left = WorldModel.PlayModes.KICK_OFF_L + ko_right = WorldModel.PlayModes.KICK_OFF_R + + # print self.play_mode + + # return whether we're on the side that's kicking off + return ( + self.side == WorldModel.SIDE_L + and self.play_mode == ko_left + or self.side == WorldModel.SIDE_R + and self.play_mode == ko_right + ) + + def is_dead_ball_them(self): + """ + Returns whether the ball is in the other team's posession and it's a + free kick, corner kick, or kick in. + """ + + # shorthand for verbose constants + kil = WorldModel.PlayModes.KICK_IN_L + kir = WorldModel.PlayModes.KICK_IN_R + fkl = WorldModel.PlayModes.FREE_KICK_L + fkr = WorldModel.PlayModes.FREE_KICK_R + ckl = WorldModel.PlayModes.CORNER_KICK_L + ckr = WorldModel.PlayModes.CORNER_KICK_R + + # shorthand for whether left team or right team is free to act + pm = self.play_mode + free_left = pm == kil or pm == fkl or pm == ckl + free_right = pm == kir or pm == fkr or pm == ckr + + # return whether the opposing side is in a dead ball situation + if self.side == WorldModel.SIDE_L: + return free_right + else: + return free_left + + def is_ball_kickable(self): + """ + Tells us whether the ball is in reach of the current player. + """ + + # ball must be visible, not behind us, and within the kickable margin + return ( + self.ball is not None + and self.ball.distance is not None + and self.ball.distance <= self.server_parameters.kickable_margin + ) + + def get_ball_speed_max(self): + """ + Returns the maximum speed the ball can be kicked at. + """ + + return self.server_parameters.ball_speed_max + + +class ServerParameters: + """ + A storage container for all the settings of the soccer server. + """ + + def __init__(self): + """ + Initialize default parameters for a server. + """ + + self.audio_cut_dist = 50 + self.auto_mode = 0 + self.back_passes = 1 + self.ball_accel_max = 2.7 + self.ball_decay = 0.94 + self.ball_rand = 0.05 + self.ball_size = 0.085 + self.ball_speed_max = 2.7 + self.ball_stuck_area = 3 + self.ball_weight = 0.2 + self.catch_ban_cycle = 5 + self.catch_probability = 1 + self.catchable_area_l = 2 + self.catchable_area_w = 1 + self.ckick_margin = 1 + self.clang_advice_win = 1 + self.clang_define_win = 1 + self.clang_del_win = 1 + self.clang_info_win = 1 + self.clang_mess_delay = 50 + self.clang_mess_per_cycle = 1 + self.clang_meta_win = 1 + self.clang_rule_win = 1 + self.clang_win_size = 300 + self.coach = 0 + self.coach_port = 6001 + self.coach_w_referee = 0 + self.connect_wait = 300 + self.control_radius = 2 + self.dash_power_rate = 0.006 + self.drop_ball_time = 200 + self.effort_dec = 0.005 + self.effort_dec_thr = 0.3 + self.effort_inc = 0.01 + self.effort_inc_thr = 0.6 + self.effort_init = 1 + self.effort_min = 0.6 + self.forbid_kick_off_offside = 1 + self.free_kick_faults = 1 + self.freeform_send_period = 20 + self.freeform_wait_period = 600 + self.fullstate_l = 0 + self.fullstate_r = 0 + self.game_log_compression = 0 + self.game_log_dated = 1 + self.game_log_dir = "./" + self.game_log_fixed = 0 + self.game_log_fixed_name = "rcssserver" + self.game_log_version = 3 + self.game_logging = 1 + self.game_over_wait = 100 + self.goal_width = 14.02 + self.goalie_max_moves = 2 + self.half_time = 300 + self.hear_decay = 1 + self.hear_inc = 1 + self.hear_max = 1 + self.inertia_moment = 5 + self.keepaway = 0 + self.keepaway_length = 20 + self.keepaway_log_dated = 1 + self.keepaway_log_dir = "./" + self.keepaway_log_fixed = 0 + self.keepaway_log_fixed_name = "rcssserver" + self.keepaway_logging = 1 + self.keepaway_start = -1 + self.keepaway_width = 20 + self.kick_off_wait = 100 + self.kick_power_rate = 0.027 + self.kick_rand = 0 + self.kick_rand_factor_l = 1 + self.kick_rand_factor_r = 1 + self.kickable_margin = 0.7 + self.landmark_file = "~/.rcssserver-landmark.xml" + self.log_date_format = "%Y%m%d%H%M-" + self.log_times = 0 + self.max_goal_kicks = 3 + self.maxmoment = 180 + self.maxneckang = 90 + self.maxneckmoment = 180 + self.maxpower = 100 + self.minmoment = -180 + self.minneckang = -90 + self.minneckmoment = -180 + self.minpower = -100 + self.nr_extra_halfs = 2 + self.nr_normal_halfs = 2 + self.offside_active_area_size = 2.5 + self.offside_kick_margin = 9.15 + self.olcoach_port = 6002 + self.old_coach_hear = 0 + self.pen_allow_mult_kicks = 1 + self.pen_before_setup_wait = 30 + self.pen_coach_moves_players = 1 + self.pen_dist_x = 42.5 + self.pen_max_extra_kicks = 10 + self.pen_max_goalie_dist_x = 14 + self.pen_nr_kicks = 5 + self.pen_random_winner = 0 + self.pen_ready_wait = 50 + self.pen_setup_wait = 100 + self.pen_taken_wait = 200 + self.penalty_shoot_outs = 1 + self.player_accel_max = 1 + self.player_decay = 0.4 + self.player_rand = 0.1 + self.player_size = 0.3 + self.player_speed_max = 1.2 + self.player_weight = 60 + self.point_to_ban = 5 + self.point_to_duration = 20 + self.port = 6000 + self.prand_factor_l = 1 + self.prand_factor_r = 1 + self.profile = 0 + self.proper_goal_kicks = 0 + self.quantize_step = 0.1 + self.quantize_step_l = 0.01 + self.record_messages = 0 + self.recover_dec = 0.002 + self.recover_dec_thr = 0.3 + self.recover_init = 1 + self.recover_min = 0.5 + self.recv_step = 10 + self.say_coach_cnt_max = 128 + self.say_coach_msg_size = 128 + self.say_msg_size = 10 + self.send_comms = 0 + self.send_step = 150 + self.send_vi_step = 100 + self.sense_body_step = 100 + self.simulator_step = 100 + self.slow_down_factor = 1 + self.slowness_on_top_for_left_team = 1 + self.slowness_on_top_for_right_team = 1 + self.stamina_inc_max = 45 + self.stamina_max = 4000 + self.start_goal_l = 0 + self.start_goal_r = 0 + self.stopped_ball_vel = 0.01 + self.synch_micro_sleep = 1 + self.synch_mode = 0 + self.synch_offset = 60 + self.tackle_back_dist = 0.5 + self.tackle_cycles = 10 + self.tackle_dist = 2 + self.tackle_exponent = 6 + self.tackle_power_rate = 0.027 + self.tackle_width = 1 + self.team_actuator_noise = 0 + self.text_log_compression = 0 + self.text_log_dated = 1 + self.text_log_dir = "./" + self.text_log_fixed = 0 + self.text_log_fixed_name = "rcssserver" + self.text_logging = 1 + self.use_offside = 1 + self.verbose = 0 + self.visible_angle = 90 + self.visible_distance = 3 + self.wind_ang = 0 + self.wind_dir = 0 + self.wind_force = 0 + self.wind_none = 0 + self.wind_rand = 0 + self.wind_random = 0 diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/util_functions.py b/mava/utils/environments/RoboCup_env/robocup_utils/util_functions.py new file mode 100644 index 000000000..ecbd26846 --- /dev/null +++ b/mava/utils/environments/RoboCup_env/robocup_utils/util_functions.py @@ -0,0 +1,715 @@ +# type: ignore +import time +from typing import Dict, NamedTuple + +import dm_env +import gym +import numpy as np +from acme import specs, types + + +def rad_rot_to_xy(rad_rot): + return np.cos(rad_rot), np.sin(rad_rot) + + +def deg_rot_to_xy(deg_rot): + return np.cos(deg_rot * np.pi / 180), np.sin(deg_rot * np.pi / 180) + + +def should_wait(wait_list): + should_wait = False + for entity in wait_list: + if not entity.wm.new_data: + should_wait = True + break + return should_wait + + +def wait_for_next_observations(obs_to_wait_for): + """ + Wait for next observation before agents should think again. + """ + # Wait for new data + waiting = should_wait(obs_to_wait_for) + + # start = time.time() + while waiting: + waiting = should_wait(obs_to_wait_for) + time.sleep(0.0001) + # end = time.time() + # print("Wait time: ", end-start) + + # Set new data false + for entity in obs_to_wait_for: + entity.wm.new_data = False + + +# Dummy class to get the observation in the correct format +class OLT(NamedTuple): + """Container for (observation, legal_actions, terminal) tuples.""" + + observation: types.Nest + legal_actions: types.Nest + terminal: types.Nest + + +class SpecWrapper(dm_env.Environment): + """Spec wrapper for 2D RoboCup environment.""" + + # Note: we don't inherit from base.EnvironmentWrapper because that class + # assumes that the wrapped environment is a dm_env.Environment. + + def __init__(self, num_players: int): + self._reset_next_step = True + + self.scaling = 200.0 + + # Chose action + act_min = [0.0] * 7 # 6 + No action + act_max = [1.0] * 7 # 6 + No action + + # Action continuous component + # All directions are in x, y format + act_min.extend( + [ + -100 / self.scaling, + -1, + -1, # dash (power, direction) + 0, + -1, + -1, # kick (power, direction) + 0, + 0, # change_view (width, quality) + -1, + -1, + 0, # tackle (direction, foul) + -1, + -1, # turn (direction) + -1, + -1, + ] + ) # turn_neck(direction) + + act_max.extend([100 / self.scaling, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) + + assert len(act_min) == len(act_max) + action_spec = specs.BoundedArray( + shape=(len(act_min),), + dtype="float32", + name="action", + minimum=act_min, + maximum=act_max, + ) + + self.action_size = action_spec.shape[0] + + # obs_dict = {"time_left": 0, "side": 1, "sense_self": 2, + # "coords": (3, 5), "body_dir": (5, 7), + # "head_dir": (7, 9), "width": (9, 12), + # "quality": 13, "stamina": 14, "effort": 15, + # "speed_amount": 16, "speed_direction": (17, 19), + # "neck_direction": (19, 21), + # "see_ball": 21, "ball_dist": 22, + # "ball_dir": (23, 25), "ball_dist_change": 25, + # "ball_dir_change": 26, "ball_speed": 27, + # "last_action": (28, 28 + self.action_size), + # } + + # TODO: Check if all bounds are correct + obs_min = [ + 0.0, # time_left + 0.0, # side + 0.0, # sense_self + -100 / self.scaling, + -50 / self.scaling, # coords + -1, + -1, # body_dir + -1, + -1, # head_dir + 0, + 0, + 0, # width + 0, # quality + 0, # stamina + 0, # effort + 0, # speed_amount + -1, + -1, # speed_direction + -1, + -1, # neck_direction + 0, # see_ball + 0, # ball_dist + -1, + -1, # ball_dir + -100 / self.scaling, # ball_dist_change + -180 / self.scaling, # ball_dir_change + 0, # ball_speed + ] + + obs_max = [ + 1.0, # time_left + 1.0, # side + 1.0, # sense_self + 100 / self.scaling, + 50 / self.scaling, # coords + 1, + 1, # body_dir + 1, + 1, # head_dir + 1, + 1, + 1, # width + 1, # quality + 1, # stamina + 1, # effort + 100 / self.scaling, # speed_amount + 1, + 1, # speed_direction + 1, + 1, # neck_direction + 1, # see_ball + 100 / self.scaling, # ball_dist + 1, + 1, # ball_dir + 100 / self.scaling, # ball_dist_change + 180 / self.scaling, # ball_dir_change + 100 / self.scaling, # ball_speed + ] + + # Last action + obs_min.extend(action_spec.minimum) + obs_max.extend(action_spec.maximum) + + # [see_player, is_on_team, player_distance, + # player_direction] for num_agents-1 + self.num_agents = num_players + + # TODO: Add this in again. + # for i in range(21): + # # [see_player, is_on_team, player_distance, + # player_direction (x, y format)] + # obs_min.extend([0, 0, -200 / self.scaling, -1, -1]) + # obs_max.extend([1, 1, +200 / self.scaling, 1, 1]) + + assert len(obs_min) == len(obs_max) + self.obs_size = len(obs_min) + + self.agents = ["player_" + str(r) for r in range(num_players)] + + self._observation_specs = {} + self._action_specs = {} + + obs_spec = specs.BoundedArray( + shape=(self.obs_size,), + dtype="float32", + name="observation", + minimum=obs_min, + maximum=obs_max, + ) + + # Time_left, ball coords, ball delta_coords + state_min = [0, -100 / self.scaling, -100 / self.scaling, -10, -10] + state_max = [1, 100 / self.scaling, 100 / self.scaling, 10, 10] + + # First player is the critic player + # Players sides, coords, delta_coords, body_angle (x, y format), + # head_angle (x, y format) + for i in range(num_players): + state_min.extend( + [ + 0.0, + -100 / self.scaling, + -100 / self.scaling, + -10, + -10, + -1, + -1, + -1, + -1, + ] + ) + state_max.extend( + [1.0, +100 / self.scaling, +100 / self.scaling, +10, +10, 1, 1, 1, 1] + ) + + # Add all observations to state info + for i in range(num_players): + state_min.extend(obs_min) + state_max.extend(obs_max) + + assert len(state_min) == len(state_max) + self._state_spec = specs.BoundedArray( + shape=(len(state_min),), + dtype="float32", + name="state", + minimum=state_min, + maximum=state_max, + ) + + self._discount = dict(zip(self.agents, [np.float32(1.0)] * len(self.agents))) + + # TODO: Delete this + # self.previous_act = {"player_0": None} + + for agent in self.agents: + # TODO: Why is the action spec in two places? + self._observation_specs[agent] = OLT( + observation=obs_spec, + legal_actions=action_spec, + terminal=specs.Array((1,), np.float32), + ) + + self._action_specs[agent] = action_spec + # Obs: BoundedArray(shape=(4,), dtype=dtype('float32'), name='observation', + # minimum=[-4.8000002e+00 -3.4028235e+38 -4.1887903e-01 -3.4028235e+38], + # maximum=[4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38]) + # Actions: DiscreteArray(shape=(), dtype=int64, name=action, minimum=0, + # maximum=1, num_values=2) + + def reset(self): + pass + + def step(self): + pass + + def observation_spec(self) -> types.NestedSpec: + return self._observation_specs + + def action_spec(self) -> types.NestedSpec: + return self._action_specs + + def reward_spec(self) -> Dict[str, specs.Array]: + reward_specs = {} + for agent in self.agents: + reward_specs[agent] = specs.Array((), np.float32) + return reward_specs + + def discount_spec(self) -> Dict[str, specs.BoundedArray]: + discount_specs = {} + for agent in self.agents: + discount_specs[agent] = specs.BoundedArray( + (), np.float32, minimum=0, maximum=1.0 + ) + return discount_specs + + def extra_spec(self) -> Dict[str, specs.BoundedArray]: + return {"s_t": self._state_spec} + + def _proc_robocup_obs( + self, observations: Dict, done: bool, nn_actions: Dict = None + ) -> Dict: + # TODO: Try to automatically normalise by min max boundries + processed_obs_dict = {} + for obs_i, agent_key in enumerate(self.agents): + env_agent_obs = observations[agent_key] + + if nn_actions: + last_action = nn_actions[agent_key] + else: + last_action = None + + proc_agent_obs = self.proc_agent_env_obs(env_agent_obs, last_action) + + observation = OLT( + observation=proc_agent_obs, + legal_actions=np.ones(self.action_size, dtype=np.float32), + terminal=np.asarray([done], dtype=np.float32), + ) + processed_obs_dict[agent_key] = observation + return processed_obs_dict + + def proc_agent_env_obs(self, env_agent_obs, last_action): # noqa: C901 + # All angles is in x,y format + proc_agent_obs = np.zeros(self.obs_size, dtype=np.float32) + obs_dict = { + "time_left": 0, + "side": 1, + "sense_self": 2, + "coords": (3, 5), + "body_dir": (5, 7), + "head_dir": (7, 9), + "width": (9, 12), + "quality": 12, + "stamina": 13, + "effort": 14, + "speed_amount": 15, + "speed_direction": (16, 18), + "neck_direction": (18, 20), + "see_ball": 20, + "ball_dist": 21, + "ball_dir": (22, 24), + "ball_dist_change": 24, + "ball_dir_change": 25, + "ball_speed": 26, + "last_action": (27, 27 + self.action_size), + } + + # TODO: Fix this when there is other players + assert self.obs_size == 27 + self.action_size + + # Time left obs + if "game_step" in env_agent_obs and "game_length" in env_agent_obs: + # Time left + proc_agent_obs[obs_dict["time_left"]] = ( + 1 - env_agent_obs["game_step"] / env_agent_obs["game_length"] + ) + + # Team side obs + side_to_id = {"l": 0.0, "r": 1.0} + proc_agent_obs[1] = side_to_id[env_agent_obs["side"]] + + if ( + "estimated_abs_coords" in env_agent_obs + and env_agent_obs["estimated_abs_coords"][0] is not None + ): + # see_own_stats + proc_agent_obs[obs_dict["sense_self"]] = 1.0 + + # coords + coords = env_agent_obs["estimated_abs_coords"] + s, e = obs_dict["coords"] + proc_agent_obs[s] = float(coords[0]) / self.scaling + proc_agent_obs[e - 1] = float(coords[1]) / self.scaling + + # body_angle + s, e = obs_dict["body_dir"] + proc_agent_obs[s:e] = deg_rot_to_xy(env_agent_obs["estimated_abs_body_dir"]) + + # head_angle + s, e = obs_dict["head_dir"] + proc_agent_obs[s:e] = deg_rot_to_xy(env_agent_obs["estimated_abs_neck_dir"]) + + # view_width + w_to_int = {"narrow": 0, "normal": 1, "wide": 2} + onehot = np.zeros(3) + onehot[w_to_int[env_agent_obs["view_width"]]] = 1 + s, e = obs_dict["width"] + proc_agent_obs[s:e] = onehot + + # view_quality + q_to_int = {"high": 0, "low": 1} + proc_agent_obs[obs_dict["quality"]] = q_to_int[ + env_agent_obs["view_quality"] + ] + + # stamina + proc_agent_obs[obs_dict["stamina"]] = env_agent_obs["stamina"] / 8000 + + # effort + proc_agent_obs[obs_dict["effort"]] = env_agent_obs["effort"] + + # speed_amount + proc_agent_obs[obs_dict["speed_amount"]] = ( + env_agent_obs["speed_amount"] / self.scaling + ) + + # speed_dir + s, e = obs_dict["speed_direction"] + proc_agent_obs[s:e] = deg_rot_to_xy(env_agent_obs["speed_direction"]) + + # Relative neck dir + s, e = obs_dict["neck_direction"] + proc_agent_obs[s:e] = deg_rot_to_xy(env_agent_obs["neck_direction"]) + + # See_ball, ball_distance, ball_direction, dist_change, dir_change, speed + if "ball" in env_agent_obs and env_agent_obs["ball"] is not None: + # Has ball flag + proc_agent_obs[obs_dict["see_ball"]] = 1.0 + + if env_agent_obs["ball"].distance is not None: + proc_agent_obs[obs_dict["ball_dist"]] = ( + env_agent_obs["ball"].distance / self.scaling + ) + + if env_agent_obs["ball"].direction is not None: + s, e = obs_dict["ball_dir"] + proc_agent_obs[s:e] = deg_rot_to_xy(env_agent_obs["ball"].direction) + + if env_agent_obs["ball"].dist_change is not None: + proc_agent_obs[obs_dict["ball_dist_change"]] = ( + env_agent_obs["ball"].dist_change / self.scaling + ) + + if env_agent_obs["ball"].dir_change is not None: + proc_agent_obs[obs_dict["ball_dir_change"]] = ( + env_agent_obs["ball"].dir_change / self.scaling + ) + + if env_agent_obs["ball"].speed is not None: + proc_agent_obs[obs_dict["ball_speed"]] = ( + env_agent_obs["ball"].speed / self.scaling + ) + + # Last player actions + if last_action is not None: + s, e = obs_dict["last_action"] + proc_agent_obs[s:e] = last_action + + # [see_player, is_on_team, distance, direction, dist_change, + # dir_change, speed, body_direction, neck_direction] + if "players" in env_agent_obs and len(env_agent_obs["players"]) > 0: + raise NotImplementedError( + "Obs for more than one player not implemented yet." + ) + # ["opponent", player.distance, player.direction (x, y format)] + num_see_players = len(env_agent_obs["players"]) + + # Place the players on the closest available spot + players = env_agent_obs["players"] + + # TODO: Get better sorting algorithm. This one is good, + # but also allows for player degrees not to + # always be assending. This is because a player can be + # selected for a slot and then the player with a + # bigger positive degree might be selected for a lower + # slot. Therefore this might happend: + # player[i].degree > player[i+1].degree. + + player_ids = list(range(num_see_players)) + spots = [[d_i, (d_i / 20) * 180 - 90] for d_i in range(21)] + player_spots = [-1] * num_see_players + for _ in range(num_see_players): + best_p_i = None + beset_spot = None + best_s_count = None + best_p_count = None + beset_dist = np.inf + + # All remaining players select the closest available spot + for p_count, p_i in enumerate(player_ids): + player_type, player_dist, player_dir = players[p_i] + + # Go through all the spots and get the closest one to the player + for s_count, spot in enumerate(spots): + s_i, s_dir = spot + + dist_to_spot = abs(s_dir - player_dir) + + if dist_to_spot < beset_dist: + beset_dist = dist_to_spot + best_p_i = p_i + beset_spot = s_i + best_p_count = p_count + best_s_count = s_count + + # Give the closest player its spot. + player_ids.pop(best_p_count) + spots.pop(best_s_count) + player_spots[best_p_i] = beset_spot + + assert len(player_ids) == 0 + # Calculate all the available slots to put player in + slots = [ + [11 + self.action_size + s_i * 5, 11 + self.action_size + s_i * 5 + 5] + for s_i in player_spots + ] + # slots = [i, i + 5] for i in + # slots = [all_slots[p_spot] for p_spot in player_spots] + + assert len(slots) == num_see_players + player_type_dict = {"opponent": 0.0, "team": 1.0} + + for p_i, player in enumerate(players): + player_type, player_dist, player_dir = player + + # pop a random element from the slots list + start_i, end_i = slots[p_i] + + # see_player, is_on_team, player_distance, + # player_direction (x, y format) + dir_x, dir_y = deg_rot_to_xy(player_dir) + + proc_agent_obs[start_i:end_i] = [ + 1.0, + player_type_dict[player_type], + player_dist / self.scaling, + dir_x, + dir_y, + ] + + # if not env_agent_obs["obs_updated"]: + # proc_agent_obs[2] = 0.0 + + return proc_agent_obs + + def _proc_robocup_state(self, state: Dict, proc_obs: Dict) -> np.array: + state_dict = { + "time_left": 0, + "ball_coords": (1, 3), + "ball_delta": (3, 5), + "player_offset": 5, + "p_obs_size": 9, + "p_side": 0, + "p_coords": (1, 3), + "p_delta": (3, 5), + "p_b_dir": (5, 7), + "p_n_dir": (7, 9), + } + offset = state_dict["player_offset"] + + proc_agent_state = np.zeros( + offset + self.num_agents * (9 + self.obs_size), dtype=np.float32 + ) + + # Time left + proc_agent_state[state_dict["time_left"]] = ( + 1 - state["game_step"] / state["game_length"] + ) + + # Ball: + ball = state["ball"] + s, e = state_dict["ball_coords"] + proc_agent_state[s:e] = [ + float(ball["coords"][0]) / self.scaling, + float(ball["coords"][1]) / self.scaling, + ] + s, e = state_dict["ball_delta"] + proc_agent_state[s:e] = [ + float(ball["delta_coords"][0]), + float(ball["delta_coords"][1]), + ] # TODO: Should delta coords have a normaliser? + + # TODO: Add check to see if players are in the correct order + # TODO: Include onehot indicating which player should be focussed on. + # Also use dictionary of states. Players should not be reordered as this + # messes with the action order provided to the Q value critic. + players = state["players"] + p_size = state_dict["p_obs_size"] + if players: + for i in range(self.num_agents): + if len(players) > i: + player = players[i] + # 'side': 0, 'coords': (52.6498, 0.54963), + # 'delta_coords': (0.000227909, 0.00371977), 'body_angle': 163, + # 'neck_angle': 0} + proc_agent_state[ + i * p_size + offset + state_dict["p_side"] + ] = player["side"] + s, e = state_dict["p_coords"] + proc_agent_state[ + i * p_size + offset + s : i * p_size + offset + e + ] = [ + player["coords"][0] / self.scaling, + player["coords"][1] / self.scaling, + ] + s, e = state_dict["p_delta"] + proc_agent_state[ + i * p_size + offset + s : i * p_size + offset + e + ] = player[ + "delta_coords" + ] # TODO: Should delta coords have a normaliser? + s, e = state_dict["p_b_dir"] + proc_agent_state[ + i * p_size + offset + s : i * p_size + offset + e + ] = deg_rot_to_xy(player["body_angle"]) + s, e = state_dict["p_n_dir"] + proc_agent_state[ + i * p_size + offset + s : i * p_size + offset + e + ] = deg_rot_to_xy(player["neck_angle"]) + + else: + break + + # Add agent observations + start_i = offset + self.num_agents * p_size + for agent_i, agent_key in enumerate(self.agents): + obs = proc_obs[agent_key].observation + proc_agent_state[ + start_i + + agent_i * self.obs_size : start_i + + (agent_i + 1) * self.obs_size + ] = obs + + return proc_agent_state + + def _proc_robocup_actions(self, actions: Dict) -> Dict: + # dash (speed), turn (direction), kick (power, direction) + processed_action_dict = {} + + for agent_key in actions.keys(): + action = actions[agent_key] + assert len(action) == self.action_size + processed_action_dict[agent_key] = self.proc_agent_action(action) + + return processed_action_dict + + def proc_agent_action(self, action): + # TODO: Add catch, tackle, move, pointto, attentionto and say commands as well. + int_to_command = [ + "dash", + "kick", + "change_view", + "tackle", + "turn", + "turn_neck", + "none", + ] + + # Remove change_view and turn_neck action + # TODONE: Remove this + # action[2] = 0 + # action[5] = 0 + + command = int_to_command[np.argmax(action[0 : len(int_to_command)])] + + # Do the command + assert len(int_to_command) == 7 + + act_dict = { + "dash_pow": 7, + "dash_dir": (8, 10), + "kick_pow": 10, + "kick_dir": (11, 13), + "width": 13, + "quality": 14, + "tackle_dir": (15, 17), + "tackle_foul": 17, + "turn": (18, 20), + "neck": (20, 22), + } + + if command == "dash": + power = action[act_dict["dash_pow"]] * self.scaling + s, e = act_dict["dash_dir"] + dir_x, dir_y = action[s:e] + dir = np.arctan2(dir_y, dir_x) * 180 / np.pi + robocup_action = "(dash " + str(power) + " " + str(dir) + ")" + elif command == "kick": + power = action[act_dict["kick_pow"]] * self.scaling + s, e = act_dict["kick_dir"] + dir_x, dir_y = action[s:e] + dir = np.arctan2(dir_y, dir_x) * 180 / np.pi + robocup_action = "(kick " + str(power) + " " + str(dir) + ")" + elif command == "change_view": + w_to_text = ["narrow", "normal", "wide"] + width = w_to_text[int(action[act_dict["width"]] * 2.99)] + q_to_text = ["high", "low"] + quality = q_to_text[int(action[act_dict["quality"]] * 1.99)] + robocup_action = "(change_view " + width + " " + quality + ")" + elif command == "tackle": + s, e = act_dict["tackle_dir"] + dir_x, dir_y = action[s:e] + dir = np.arctan2(dir_y, dir_x) * 180 / np.pi + f_to_text = ["true", "false"] + foul = f_to_text[int(action[act_dict["tackle_foul"]] * 1.99)] + robocup_action = "(tackle " + str(dir) + " " + foul + ")" + elif command == "turn": + s, e = act_dict["turn"] + dir_x, dir_y = action[s:e] + dir = np.arctan2(dir_y, dir_x) * 180 / np.pi + robocup_action = "(turn " + str(dir) + ")" + elif command == "turn_neck": + s, e = act_dict["neck"] + x, y = action[s:e] + turn_neck_dir = np.arctan2(y, x) * 180 / np.pi + robocup_action = "(turn_neck " + str(turn_neck_dir) + ")" + elif command == "none": + robocup_action = "(done)" + else: + raise NotImplementedError("Command not implemented: ", command) + return robocup_action + + @property + def possible_agents(self) -> gym.Env: + """Returns the number of possible agents.""" + return self.agents From f9061d667346925c6e1cb5b04ea0d647e09fa363 Mon Sep 17 00:00:00 2001 From: Dries Date: Tue, 1 Jun 2021 06:39:05 +0200 Subject: [PATCH 107/188] fix: In all trainers set Tensorflow to dynamically grow the gpu memory size. --- mava/systems/tf/dial/training.py | 3 +++ mava/systems/tf/mad4pg/training.py | 2 ++ mava/systems/tf/maddpg/training.py | 2 ++ mava/systems/tf/madqn/training.py | 2 ++ mava/systems/tf/mappo/training.py | 2 ++ mava/systems/tf/qmix/training.py | 2 ++ mava/systems/tf/vdn/training.py | 2 ++ mava/utils/training_utils.py | 9 +++++++++ 8 files changed, 24 insertions(+) diff --git a/mava/systems/tf/dial/training.py b/mava/systems/tf/dial/training.py index 080650edc..df967807d 100644 --- a/mava/systems/tf/dial/training.py +++ b/mava/systems/tf/dial/training.py @@ -31,6 +31,9 @@ LinearExplorationScheduler, ) from mava.systems.tf.madqn.training import MADQNRecurrentCommTrainer +from mava.utils import training_utils as train_utils + +train_utils.set_growing_gpu_memory() class DIALSwitchTrainer(MADQNRecurrentCommTrainer): diff --git a/mava/systems/tf/mad4pg/training.py b/mava/systems/tf/mad4pg/training.py index 0507ef564..b3364c3d3 100644 --- a/mava/systems/tf/mad4pg/training.py +++ b/mava/systems/tf/mad4pg/training.py @@ -37,6 +37,8 @@ ) from mava.utils import training_utils as train_utils +train_utils.set_growing_gpu_memory() + class MAD4PGBaseTrainer(MADDPGBaseTrainer): """MAD4PG trainer. diff --git a/mava/systems/tf/maddpg/training.py b/mava/systems/tf/maddpg/training.py index 139fba243..db57ce31a 100644 --- a/mava/systems/tf/maddpg/training.py +++ b/mava/systems/tf/maddpg/training.py @@ -36,6 +36,8 @@ from mava.systems.tf import savers as tf2_savers from mava.utils import training_utils as train_utils +train_utils.set_growing_gpu_memory() + class MADDPGBaseTrainer(mava.Trainer): """MADDPG trainer. diff --git a/mava/systems/tf/madqn/training.py b/mava/systems/tf/madqn/training.py index ab971894b..64d5a15aa 100644 --- a/mava/systems/tf/madqn/training.py +++ b/mava/systems/tf/madqn/training.py @@ -34,6 +34,8 @@ from mava.systems.tf import savers as tf2_savers from mava.utils import training_utils as train_utils +train_utils.set_growing_gpu_memory() + class MADQNTrainer(mava.Trainer): """MADQN trainer. diff --git a/mava/systems/tf/mappo/training.py b/mava/systems/tf/mappo/training.py index 793600218..d30458138 100644 --- a/mava/systems/tf/mappo/training.py +++ b/mava/systems/tf/mappo/training.py @@ -31,6 +31,8 @@ from mava.systems.tf import savers as tf2_savers from mava.utils import training_utils as train_utils +train_utils.set_growing_gpu_memory() + tfd = tfp.distributions diff --git a/mava/systems/tf/qmix/training.py b/mava/systems/tf/qmix/training.py index c06e0024b..f3319ac3e 100644 --- a/mava/systems/tf/qmix/training.py +++ b/mava/systems/tf/qmix/training.py @@ -32,6 +32,8 @@ from mava.systems.tf.madqn.training import MADQNTrainer from mava.utils import training_utils as train_utils +train_utils.set_growing_gpu_memory() + class QMIXTrainer(MADQNTrainer): """QMIX trainer. diff --git a/mava/systems/tf/vdn/training.py b/mava/systems/tf/vdn/training.py index 232468f68..9b1a8127b 100644 --- a/mava/systems/tf/vdn/training.py +++ b/mava/systems/tf/vdn/training.py @@ -28,6 +28,8 @@ from mava.systems.tf.madqn.training import MADQNTrainer from mava.utils import training_utils as train_utils +train_utils.set_growing_gpu_memory() + class VDNTrainer(MADQNTrainer): """VDN trainer. diff --git a/mava/utils/training_utils.py b/mava/utils/training_utils.py index adb9bb994..7cc8b9a14 100644 --- a/mava/utils/training_utils.py +++ b/mava/utils/training_utils.py @@ -1,3 +1,4 @@ +import os from typing import Any, Dict, Iterable, Sequence import sonnet as snt @@ -13,6 +14,14 @@ def checkpoint_networks(system_checkpointer: Dict) -> None: checkpointer.save() +def set_growing_gpu_memory() -> None: + # Solve gpu mem issues. + os.environ["TF_FORCE_GPU_ALLOW_GROWTH"] = "true" + physical_devices = tf.config.list_physical_devices("GPU") + if physical_devices: + tf.config.experimental.set_memory_growth(physical_devices[0], True) + + # Map critic and polic losses to dict, grouped by agent. def map_losses_per_agent_ac(critic_losses: Dict, policy_losses: Dict) -> Dict: assert len(policy_losses) > 0 and ( From c38cb60860814c18eaab7511d087552cb6041658 Mon Sep 17 00:00:00 2001 From: Dries Date: Tue, 1 Jun 2021 07:01:31 +0200 Subject: [PATCH 108/188] fix: Set render_game to False. --- mava/utils/environments/RoboCup_env/robocup_base.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mava/utils/environments/RoboCup_env/robocup_base.py b/mava/utils/environments/RoboCup_env/robocup_base.py index e3d185752..86f35ea27 100644 --- a/mava/utils/environments/RoboCup_env/robocup_base.py +++ b/mava/utils/environments/RoboCup_env/robocup_base.py @@ -99,7 +99,7 @@ def create_robocup_environment( game_setting=game_name, team_names=["Team_A", "Team_B"], players_per_team=players_per_team, - render_game=True, + render_game=False, include_wait=False, game_length=1000, port=rand_port, From 7b50ae16e504aa1fff64fce95060338ea3990e60 Mon Sep 17 00:00:00 2001 From: Dries Date: Tue, 1 Jun 2021 09:54:48 +0200 Subject: [PATCH 109/188] Fix: Updated fixed agent. --- .../environments/RoboCup_env/robocup_agents/fixed_agent.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mava/utils/environments/RoboCup_env/robocup_agents/fixed_agent.py b/mava/utils/environments/RoboCup_env/robocup_agents/fixed_agent.py index 8c431485a..8165718b3 100755 --- a/mava/utils/environments/RoboCup_env/robocup_agents/fixed_agent.py +++ b/mava/utils/environments/RoboCup_env/robocup_agents/fixed_agent.py @@ -22,7 +22,7 @@ def __init__(self, agent_type, num_players): # self._action_size = 7 def reset_brain(self): - pass + self.kicked = False def get_action(self, observation): obs_dict = { From ed614965f64a30b5e53d881ed491412fe4ccdd7a Mon Sep 17 00:00:00 2001 From: Dries Date: Tue, 1 Jun 2021 12:15:33 +0200 Subject: [PATCH 110/188] fix: Update set_growing_gpu_memory to loop over all GPU devices. --- mava/utils/training_utils.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/mava/utils/training_utils.py b/mava/utils/training_utils.py index 7cc8b9a14..01d57dcc7 100644 --- a/mava/utils/training_utils.py +++ b/mava/utils/training_utils.py @@ -19,7 +19,8 @@ def set_growing_gpu_memory() -> None: os.environ["TF_FORCE_GPU_ALLOW_GROWTH"] = "true" physical_devices = tf.config.list_physical_devices("GPU") if physical_devices: - tf.config.experimental.set_memory_growth(physical_devices[0], True) + for device in physical_devices: + tf.config.experimental.set_memory_growth(device, True) # Map critic and polic losses to dict, grouped by agent. 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diff --git a/README.md b/README.md index a0a2ddc8a..f4d550b32 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ - + # Mava: a research framework for multi-agent reinforcement learning @@ -8,34 +8,34 @@ ![pytest](https://github.com/arnupretorius/mava/workflows/format_and_test/badge.svg) -Mava is a library for building multi-agent reinforcement learning (MARL) systems. Mava builds off of Acme and in a similar way strives to expose simple, efficient, and readable components, as well as examples that serve both as reference implementations of popular algorithms and as strong +Mava is a library for building multi-agent reinforcement learning (MARL) systems. Mava builds off of [Acme][Acme] and in a similar way strives to expose simple, efficient, and readable components, as well as examples that serve both as reference implementations of popular algorithms and as strong baselines, while still providing enough flexibility to do novel research. +## Overview +### Systems and the Executor-Trainer paradigm +At the core of the Mava framework is the concept of a `system`. A system refers to a full multi-agent reinforcement learning algorithm consisting of the following specific components: an `Executor`, a `Trainer` and a `Dataset`. -## Overview +

+ +         + +

-If you just want to get started using Mava quickly, the main thing to know about -the library is that we expose a number of system implementations and an -`EnvironmentLoop` primitive similar to Acme that can be used as follows: +The `Executor` is the part of the system that interacts with the environment, takes actions for each agent and observes the next state as a collection of observations, one for each agent in the system. Essentially, executors are the multi-agent version of the Actor class in Acme and are themselves constructed through feeding to the executor a dictionary of policy networks. The `Trainer` is responsible for sampling data from the Dataset originally collected from the executor and updating the parameters for every agent in the system. Trainers are therefore the multi-agent version of the Learner class in Acme. The `Dataset` stores all of the information collected by the executors in the form of a collection of dictionaries for the actions, observations and rewards with keys corresponding to the individual agent ids. +Several examples of system implementations can be viewed [here][Systems]. -```python -loop = mava.EnvironmentLoop(environment, system) -loop.run() -``` +### Distributed system training -This will run a simple loop in which the given multi-agent system interacts with its -environment and and each agent learns from this interaction. This assumes a `system` instance -(implementations of which you can find [here][Systems]) and a multi-agent `environment` -instance which implements the [DeepMind Environment API][dm_env]. +Mava shares much of the design philosophy of Acme for the same reason: to allow a high level of composability for novel research (i.e. building new systems) as well as making it possible to scale systems in a simple way, using the same underlying multi-agent RL system code. In the latter case, the system executor (which is responsible for data collection) is distributed across multiple processes each with a copy of the environment. Each process collects and stores data which the Trainer uses to update the parameters of the actor networks used within each executor. -For a deeper dive, take a look at the detailed working code -examples found in our [examples] subdirectory which show how to instantiate a few MARL systems and environments. +### Supported environments and the system-environment loop -> :information_source: Mava heavily relies on Acme, therefore as is the case with Acme, we make the same statement regarding reliability: mava is a framework for MARL research written by -> researchers, for researchers. We will make every attempt to keep everything in good -> working order, but things may break occasionally. If they do, we will make our best -> effort to fix them as quickly as possible! +A given multi-agent system interacts with its environment via an `EnvironmentLoop`. This loop takes as input a `system` instance and a multi-agent `environment` +instance which implements the [DeepMind Environment API][dm_env]. Mava currently supports multi-agent environment loops and environment wrappers for the following environments and environment suites: [PettingZoo][pettingzoo], [SMAC][smac], [Flatland][flatland] and [2D RoboCup][robocup]. +### Examples +For a deeper dive, take a look at the detailed working code +examples found in our [examples] subdirectory which show how to instantiate a few MARL systems and environments. ## Installation We have tested `mava` on Python 3.6, 3.7 and 3.8. @@ -53,20 +53,16 @@ We have tested `mava` on Python 3.6, 3.7 and 3.8. ``` For example, `make run EXAMPLE=examples/petting_zoo/run_decentralised_feedforward_maddpg_continous.py`. - Alternatively, you can also run a specific system that is defined in the `Makefile`: - ```bash - make run-maddpg - ``` - Or run bash inside a docker container with mava installed, `make bash`, and from there examples can be run as follows: `python dir/to/example/example.py`. + Alternatively, run bash inside a docker container with mava installed, `make bash`, and from there examples can be run as follows: `python dir/to/example/example.py`. - For viewing results through tensorboard, you can run + To run an example with tensorboard viewing enabled, you can run ```bash make run-tensorboard EXAMPLE=dir/to/example/example.py ``` and navigate to `http://127.0.0.1:6006/`. 3. Install multi-agent Starcraft 2 environment [Optional]: - To install the environment, please run the provided bash script. + To install the environment, please run the provided bash script, which is a slightly modified version of the script found [here][pymarl]. ```bash ./install_sc2.sh @@ -140,6 +136,9 @@ We have tested `mava` on Python 3.6, 3.7 and 3.8. We also have a list of [optional installs](OPTIONAL_INSTALL.md) for extra functionality such as the use of Atari environments, environment wrappers, gpu support and agent episode recording. +## Debugging + +Simple spread debugging environment. ## Contributing Please read our [contributing docs](./CONTRIBUTING.md) for details on how to submit pull requests, our Contributor License Agreement and community guidelines. @@ -148,6 +147,7 @@ Please read our [contributing docs](./CONTRIBUTING.md) for details on how to sub Please read our [troubleshooting and FAQs guide](./TROUBLESHOOTING.md). +[Acme]: https://github.com/deepmind/acme [Systems]: mava/systems/ [Examples]: examples/ [Tutorial]: https://arxiv.org @@ -155,6 +155,9 @@ Please read our [troubleshooting and FAQs guide](./TROUBLESHOOTING.md). [Documentation]: www.mava.rl [Paper]: https://arxiv.org [pettingzoo]: https://github.com/PettingZoo-Team/PettingZoo +[smac]: https://github.com/oxwhirl/smac [openspiel]: https://github.com/deepmind/open_spiel [flatland]: https://gitlab.aicrowd.com/flatland/flatland +[robocup]: https://github.com/rcsoccersim/rcssserver [dm_env]: https://github.com/deepmind/dm_env +[pymarl]: https://github.com/oxwhirl/pymarl From 4284061930811d60138f18a91644983215fe6b20 Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Wed, 2 Jun 2021 11:08:46 +0200 Subject: [PATCH 113/188] add simpe spread debug env image --- docs/images/simple_spread.png | Bin 0 -> 42349 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 docs/images/simple_spread.png diff --git a/docs/images/simple_spread.png b/docs/images/simple_spread.png new file mode 100644 index 0000000000000000000000000000000000000000..dd64c00e9c15a29bd578c390d3f436d3d81482dc GIT binary patch literal 42349 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research framework for multi-agent reinforcement learning +# Mava: a research framework for distributed multi-agent reinforcement learning **[Overview](#overview)** | **[Installation](#installation)** | **[Systems]** | **[Examples]** | @@ -51,9 +51,7 @@ We have tested `mava` on Python 3.6, 3.7 and 3.8. ```bash make run EXAMPLE=dir/to/example/example.py ``` - For example, `make run EXAMPLE=examples/petting_zoo/run_decentralised_feedforward_maddpg_continous.py`. - - Alternatively, run bash inside a docker container with mava installed, `make bash`, and from there examples can be run as follows: `python dir/to/example/example.py`. + For example, `make run EXAMPLE=examples/petting_zoo/run_decentralised_feedforward_maddpg_continous.py`. Alternatively, run bash inside a docker container with mava installed, `make bash`, and from there examples can be run as follows: `python dir/to/example/example.py`. To run an example with tensorboard viewing enabled, you can run ```bash @@ -70,7 +68,7 @@ We have tested `mava` on Python 3.6, 3.7 and 3.8. 1. If not using docker, we strongly recommend using a [Python virtual environment](https://docs.python.org/3/tutorial/venv.html) - to manage your dependencies in order to avoid version conflicts: + to manage your dependencies in order to avoid version conflicts. Please note that since Launchpad only supports Linux based OSes, using a python virtual environment will only work in these cases: ```bash python3 -m venv mava @@ -78,57 +76,25 @@ We have tested `mava` on Python 3.6, 3.7 and 3.8. pip install --upgrade pip setuptools ``` -2. To install the core libraries (including [Reverb], our storage backend): +2. To install the core libraries with dependencies from source (from root directory): ```bash - pip install id-mava + pip install -e ".[tf,envs,reverb,launchpad]" ``` - or install from source (from root directory): + Note that the dependencies may be installed selectively by adding and removing their identifiers. Additional optional dependencies include `record_episode` for installing packages required to make video recordings of evaluation runs and `testing_formatting` for running tests and code formatting checks. Extra information on optional installs are given below. + +3. **NB**: Flatland and SMAC installations have to be done separately. Flatland can be installed using: + ```bash - pip install . + pip install .[flatland] ``` - - -3. To install Acme dependencies for [TensorFlow]-based Acme agents: - - ``` - pip install id-mava[reverb] - pip install id-mava[tf] - ``` - - or from source: - - ```bash - pip install .[reverb] - pip install .[tf] - ``` - -4. To install a few example environments (including [pettingzoo], - [openspiel], and [flatland]): - - ```bash - pip install id-mava[envs] - ``` - - or from source: - ```bash - pip install .[envs] - ``` - - NB: For flatland installation, It has to be installed separately using: - ```bash - pip install id-mava[flatland] - ``` - or from source - ```bash - pip install .[flatland] - ``` - - NB: For StarCraft II installation, this must be installed separately according to your operating system. - To install the StarCraft II ML environment and associated packages, please follow the instructions on [PySC2](https://github.com/deepmind/pysc2) to install the StarCraft II game files. - Please ensure you have the required game maps (for both PySC2 and SMAC) extracted in the StarCraft II maps directory. - Once this is done you can install the packages for the single agent case (PySC2) and the multi-agent case (SMAC). + + For StarCraft II installation, this must be installed separately according to your operating system. + To install the StarCraft II ML environment and associated packages, please follow the instructions on [PySC2](https://github.com/deepmind/pysc2) to install the StarCraft II game files. + Please ensure you have the required game maps (for both PySC2 and SMAC) extracted in the StarCraft II maps directory. + Once this is done you can install the packages for the single agent case (PySC2) and the multi-agent case (SMAC). + ```bash pip install pysc2 pip install git+https://github.com/oxwhirl/smac.git @@ -139,6 +105,22 @@ We also have a list of [optional installs](OPTIONAL_INSTALL.md) for extra functi ## Debugging Simple spread debugging environment. + +

+ +

+ +## Roadmap + +[![](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Sequential%20environment%20support%20including%20OpenSpiel)](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Sequential%20environment%20support%20including%20OpenSpiel/vote) +[![](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Population%20based%20training)](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Population%20based%20training/vote) +[![](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Dynamic%20networked%20architectures)](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Dynamic%20networked%20architectures/vote) + + +* Improved testing and verification of system implementations. +* Sequential environment support including OpenSpiel. +* Population based training. +* Additional systems and modules. ## Contributing Please read our [contributing docs](./CONTRIBUTING.md) for details on how to submit pull requests, our Contributor License Agreement and community guidelines. @@ -147,6 +129,21 @@ Please read our [contributing docs](./CONTRIBUTING.md) for details on how to sub Please read our [troubleshooting and FAQs guide](./TROUBLESHOOTING.md). +## Citing Mava + +If you use Mava in your work, please cite the accompanying +[technical report][Paper]: + +```bibtex +@article{anon2021mava, + title={Mava: A Research Framework for Distributed Multi-Agent Reinforcement Learning}, + author={Anonymous authors}, + year={2021}, + journal={arXiv preprint}, + url={}, +} +``` + [Acme]: https://github.com/deepmind/acme [Systems]: mava/systems/ [Examples]: examples/ From 1c34217c7f3726156eea69c7539d65313c379432 Mon Sep 17 00:00:00 2001 From: Arnu Pretorius Date: Wed, 2 Jun 2021 11:54:18 +0200 Subject: [PATCH 115/188] Update README.md --- README.md | 20 +++++++++----------- 1 file changed, 9 insertions(+), 11 deletions(-) diff --git a/README.md b/README.md index 26f4bbd6b..1c931a032 100644 --- a/README.md +++ b/README.md @@ -16,9 +16,9 @@ baselines, while still providing enough flexibility to do novel research. At the core of the Mava framework is the concept of a `system`. A system refers to a full multi-agent reinforcement learning algorithm consisting of the following specific components: an `Executor`, a `Trainer` and a `Dataset`.

- +          - +

The `Executor` is the part of the system that interacts with the environment, takes actions for each agent and observes the next state as a collection of observations, one for each agent in the system. Essentially, executors are the multi-agent version of the Actor class in Acme and are themselves constructed through feeding to the executor a dictionary of policy networks. The `Trainer` is responsible for sampling data from the Dataset originally collected from the executor and updating the parameters for every agent in the system. Trainers are therefore the multi-agent version of the Learner class in Acme. The `Dataset` stores all of the information collected by the executors in the form of a collection of dictionaries for the actions, observations and rewards with keys corresponding to the individual agent ids. @@ -32,7 +32,7 @@ Mava shares much of the design philosophy of Acme for the same reason: to allow A given multi-agent system interacts with its environment via an `EnvironmentLoop`. This loop takes as input a `system` instance and a multi-agent `environment` instance which implements the [DeepMind Environment API][dm_env]. Mava currently supports multi-agent environment loops and environment wrappers for the following environments and environment suites: [PettingZoo][pettingzoo], [SMAC][smac], [Flatland][flatland] and [2D RoboCup][robocup]. -### Examples +## Examples For a deeper dive, take a look at the detailed working code examples found in our [examples] subdirectory which show how to instantiate a few MARL systems and environments. @@ -107,20 +107,15 @@ We also have a list of [optional installs](OPTIONAL_INSTALL.md) for extra functi Simple spread debugging environment.

- +

## Roadmap -[![](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Sequential%20environment%20support%20including%20OpenSpiel)](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Sequential%20environment%20support%20including%20OpenSpiel/vote) -[![](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Population%20based%20training)](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Population%20based%20training/vote) +[![](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Sequential%20environment%20support%20including%20OpenSpiel)](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Sequential%20environment%20support%20including%20OpenSpiel/vote)
+[![](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Population%20based%20training)](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Population%20based%20training/vote)
[![](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Dynamic%20networked%20architectures)](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Dynamic%20networked%20architectures/vote) - -* Improved testing and verification of system implementations. -* Sequential environment support including OpenSpiel. -* Population based training. -* Additional systems and modules. ## Contributing Please read our [contributing docs](./CONTRIBUTING.md) for details on how to submit pull requests, our Contributor License Agreement and community guidelines. @@ -129,6 +124,9 @@ Please read our [contributing docs](./CONTRIBUTING.md) for details on how to sub Please read our [troubleshooting and FAQs guide](./TROUBLESHOOTING.md). +### Purple GitLab Tanuki +{: #tanuki-purple} + ## Citing Mava If you use Mava in your work, please cite the accompanying From 10683da225e762bf87c7f7918d3260d9f3082c20 Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Wed, 2 Jun 2021 11:55:54 +0200 Subject: [PATCH 116/188] added progress images --- README.md | 4 ++-- docs/images/green.png | Bin 0 -> 3467 bytes docs/images/orange.png | Bin 0 -> 3286 bytes docs/images/red.png | Bin 0 -> 3818 bytes 4 files changed, 2 insertions(+), 2 deletions(-) create mode 100644 docs/images/green.png create mode 100644 docs/images/orange.png create mode 100644 docs/images/red.png diff --git a/README.md b/README.md index 26f4bbd6b..003399db7 100644 --- a/README.md +++ b/README.md @@ -112,8 +112,8 @@ Simple spread debugging environment. ## Roadmap -[![](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Sequential%20environment%20support%20including%20OpenSpiel)](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Sequential%20environment%20support%20including%20OpenSpiel/vote) -[![](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Population%20based%20training)](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Population%20based%20training/vote) +[![](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Sequential%20environment%20support%20including%20OpenSpiel)](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Sequential%20environment%20support%20including%20OpenSpiel/vote)
+[![](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Population%20based%20training)](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Population%20based%20training/vote)
[![](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Dynamic%20networked%20architectures)](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Dynamic%20networked%20architectures/vote) diff --git a/docs/images/green.png b/docs/images/green.png new file mode 100644 index 0000000000000000000000000000000000000000..5aea31c051ab961a6f8bd0637de1ab0df087b045 GIT binary patch literal 3467 zcmV;64RrE}P)qA%-$%)*q%3%F8Ji<5!$A0W<7E+{Ht zU|`GuvX#qA3P9{0AX_9U8_Zq+Viy4&2xM;ovC|>!6JRwE_8qXAkRWFu`vZ{8mjq@r zf$ZW-hp+`eY^c2;An2HqpOmTq^1X(krKN=?iGCmmN>VFIK(upyL1j^9dPa$YBQT^= z6+H8j^>h@B42%pJ7(n3;QT;g(9G;9~XAoitfDoG_!NB0Zl!1Ze4?@hOmw{n{4g&-K zRD_rs&~yQP28Mlel1q!qpvEvV88a{t;sXFhw@PR+CXJ>5001~;SV?A0O#mtY000O8 z0f%V-1ONa40RR918UO$Q0007@0ssU600031002Xv0{{d700031002J#0000@flLqp z01MkmL_t(|0qt99a8uV6{$y>IEK9O2?--lK7;IW_AS{It3d54640{vWfu?O5l9^6t zI%(6EcBa$zNAsiUG->-MooSj(+B6y3gd~#$=!7p>_=5`J@m8` zvXvN=UTZtd!=3nI^;*X#rzkxC>3!--SM}l-BYBS`IU_WBX`AZuXu>AuY-K 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zaFE@8^0ilyLbo<2(Iz|Y7KsVAwm5auL|IvU|D?qk?3@$hJTcCY>Z8=K^W;7@la9~1 zGjbv%5+kwJImnqXK*4=4nW%MS&gRIZnMR{GXU0PVaQ0viCCvAHeH12&v2%W$iIVH& z^k4iX;)1S;raEh4=j+~|mfPI#N7F&-l7r*FynDS>aG>9`Pw&Y&0= z2juxB#F9WCqBCi?3##%2smwO|UM;h{5=C@z&He zd1ZX&fWn7#WX16d3DC!NK93tgj%8`og7w5-6|%FRzc!3#cg!3omj9QP@mMBwx!T06 gvR#d$TC)YybcN07*qoM6N<$f|G_D{r~^~ literal 0 HcmV?d00001 From 7371e60b3393b3fce68b53c22b0baee7aadaa9d1 Mon Sep 17 00:00:00 2001 From: Arnu Pretorius Date: Wed, 2 Jun 2021 17:53:09 +0200 Subject: [PATCH 117/188] Update README.md --- README.md | 43 +++++++++++++++++++++++++++++++++++++++---- 1 file changed, 39 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 1c931a032..a9a1fc079 100644 --- a/README.md +++ b/README.md @@ -31,9 +31,47 @@ Mava shares much of the design philosophy of Acme for the same reason: to allow ### Supported environments and the system-environment loop A given multi-agent system interacts with its environment via an `EnvironmentLoop`. This loop takes as input a `system` instance and a multi-agent `environment` -instance which implements the [DeepMind Environment API][dm_env]. Mava currently supports multi-agent environment loops and environment wrappers for the following environments and environment suites: [PettingZoo][pettingzoo], [SMAC][smac], [Flatland][flatland] and [2D RoboCup][robocup]. +instance which implements the [DeepMind Environment API][dm_env]. Mava currently supports multi-agent environment loops and environment wrappers for the following environments and environment suites: +* [PettingZoo][pettingzoo] +* [SMAC][smac] +* [Flatland][flatland] +* [2D RoboCup][robocup] + +## Implementations + +* 🟩 - Multi-Agent Deep Q-Networks (MADQN). +* 🟩 - Multi-Agent Deep Deterministic Policy Gradient (MADDPG). +* 🟩 - Multi-Agent Distributed Distributional Deep Deterministic Policy Gradient (MAD4PG). +* 🟨 - Multi-Agent Proximal Policy Optimisation (MAPPO). +* 🟥 - Value Decomposition Networks (VDN). +* 🟥 - Monotonic value function factorisation (QMIX). + ## Examples +```python +# Mava imports +from mava.systems.tf import madqn +from mava.components.tf.architectures import DecentralisedPolicyActor +from mava.systems.tf.system.helpers import environment_factory, network_factory + +# Launchpad imports +import launchpad + +# Distributed program +program = madqn.MADQN( + environment_factory=environment_factory, + network_factory=network_factory, + architecture=DecentralisedPolicyActor, + num_executors=2, + ).build() + +# Launch +launchpad.launch( + program, + launchpad.LaunchType.LOCAL_MULTI_PROCESSING, + ) +``` + For a deeper dive, take a look at the detailed working code examples found in our [examples] subdirectory which show how to instantiate a few MARL systems and environments. ## Installation @@ -124,9 +162,6 @@ Please read our [contributing docs](./CONTRIBUTING.md) for details on how to sub Please read our [troubleshooting and FAQs guide](./TROUBLESHOOTING.md). -### Purple GitLab Tanuki -{: #tanuki-purple} - ## Citing Mava If you use Mava in your work, please cite the accompanying From 514aa9e9ccf936eb19f740ba56e956d0d450d56e Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Wed, 2 Jun 2021 17:53:35 +0200 Subject: [PATCH 118/188] minor edits to readme --- README.md | 4 ---- 1 file changed, 4 deletions(-) diff --git a/README.md b/README.md index 1c931a032..c7f1829c6 100644 --- a/README.md +++ b/README.md @@ -123,10 +123,6 @@ Please read our [contributing docs](./CONTRIBUTING.md) for details on how to sub ## Troubleshooting and FAQs Please read our [troubleshooting and FAQs guide](./TROUBLESHOOTING.md). - -### Purple GitLab Tanuki -{: #tanuki-purple} - ## Citing Mava If you use Mava in your work, please cite the accompanying From 211a65473ff876c9e7d1698f782afbb8a2d68507 Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Wed, 2 Jun 2021 17:55:19 +0200 Subject: [PATCH 119/188] add debugging env results for discrete and cont. using ff nets. --- docs/images/continuous.png | Bin 0 -> 78877 bytes docs/images/discrete.png | Bin 0 -> 63365 bytes docs/images/green.png | Bin 3467 -> 0 bytes docs/images/orange.png | Bin 3286 -> 0 bytes docs/images/red.png | Bin 3818 -> 0 bytes 5 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 docs/images/continuous.png create mode 100644 docs/images/discrete.png delete mode 100644 docs/images/green.png delete mode 100644 docs/images/orange.png delete mode 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insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 7e4181476..82596cb16 100644 --- a/README.md +++ b/README.md @@ -32,13 +32,22 @@ Mava shares much of the design philosophy of Acme for the same reason: to allow A given multi-agent system interacts with its environment via an `EnvironmentLoop`. This loop takes as input a `system` instance and a multi-agent `environment` instance which implements the [DeepMind Environment API][dm_env]. Mava currently supports multi-agent environment loops and environment wrappers for the following environments and environment suites: + +

    + +
    + * [PettingZoo][pettingzoo] * [SMAC][smac] * [Flatland][flatland] * [2D RoboCup][robocup] +The animation on the right shows MAD4PG solving the Multi-Walker environment from PettingZoo. + ## Implementations +Mava includes several system implementations. Below we list these together with an indication of the maturity of the system using the following keys: 🟩 -- Tested and working well, 🟨 -- Running and training on simple environments, but not extensively tested and 🟥 -- Implemented but untested and yet to show clear signs of stable training. + * 🟩 - Multi-Agent Deep Q-Networks (MADQN). * 🟩 - Multi-Agent Deep Deterministic Policy Gradient (MADDPG). * 🟩 - Multi-Agent Distributed Distributional Deep Deterministic Policy Gradient (MAD4PG). @@ -46,21 +55,25 @@ instance which implements the [DeepMind Environment API][dm_env]. Mava currently * 🟥 - Value Decomposition Networks (VDN). * 🟥 - Monotonic value function factorisation (QMIX). +As we develop Mava further, we aim to have all systems well tested on a wide variety of environments. + ## Examples +To get a sense of how Mava systems are used we provide the following simplified example of launching a distributed MADQN system. + ```python # Mava imports from mava.systems.tf import madqn from mava.components.tf.architectures import DecentralisedPolicyActor -from mava.systems.tf.system.helpers import environment_factory, network_factory +from . import helpers # Launchpad imports import launchpad # Distributed program program = madqn.MADQN( - environment_factory=environment_factory, - network_factory=network_factory, + environment_factory=helpers.environment_factory, + network_factory=helpers.network_factory, architecture=DecentralisedPolicyActor, num_executors=2, ).build() @@ -72,8 +85,33 @@ launchpad.launch( ) ``` +The first two arguments to the program are environment and network factory functions. +These helper functions are responsible for creating the networks for the system, initialising their parameters on the different compute nodes and providing a copy of the environment for each executor. The next argument `num_executors` sets the number of executor processes to be run. +After building the program we feed it to Launchpad's `launch` function and specify the launch type to perform local multi-processing, i.e. running the distributed program on a single machine. Scaling up or down is simply a matter of adjusting the number of executor processes. + For a deeper dive, take a look at the detailed working code examples found in our [examples] subdirectory which show how to instantiate a few MARL systems and environments. + +### Components + +Mava provides several components to support the design of MARL systems such as different system `architectures` and `modules`. You can change the architecture to support a different form of information sharing between agents, or add a module to enhance system capabilities. For example, you can update the above system code in MADQN to use a communication module by wrapping the architecture fed to the system as shown below. + +```python +from mava.components.tf.modules import communication + +... + +# Wrap architecture in communication module +communication.BroadcastedCommunication( + architecture=architecture, + shared=True, + channel_size=1, + channel_noise=0, +) +``` + +All modules in Mava work in this way. + ## Installation We have tested `mava` on Python 3.6, 3.7 and 3.8. @@ -142,12 +180,22 @@ We also have a list of [optional installs](OPTIONAL_INSTALL.md) for extra functi ## Debugging -Simple spread debugging environment. +
    + +
    + +To test and debug new system implementations, we use a simplified version of the spread environment from the [MPE][mpe] suite. +Debuggin in MARL can be very difficult and time consuming, therefore it is important to use a small environment for debugging that is simple and fast but at the same time still able to clearly show whether a system is able to learn. An illustration of the debugging environment is shown on the right. Agents start at random locations and are assigned specific landmarks which they should attempt to reach in as few steps as possible. Rewards are given to each agent independently as a function of the distance that agent is from their landmark. To test both discrete and continous control systems we feature two version of the environment. In the discrete version the action space for each agent consists of the following five actions: `left`, `right`, `up`, `down`, `stand-still`. In the continuous case, the action space consists of real-values bounded between -1 and 1 for the acceleration of the agent in the x and y direction. + +### Feedforward systems

    - + +

    +### Recurrent systems + ## Roadmap [![](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Sequential%20environment%20support%20including%20OpenSpiel)](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Sequential%20environment%20support%20including%20OpenSpiel/vote)
    @@ -190,3 +238,4 @@ If you use Mava in your work, please cite the accompanying [robocup]: https://github.com/rcsoccersim/rcssserver [dm_env]: https://github.com/deepmind/dm_env [pymarl]: https://github.com/oxwhirl/pymarl +[mpe]: https://github.com/openai/multiagent-particle-envs From cc5b0e1108d0142740f19c7cc501a3e130a8609e Mon Sep 17 00:00:00 2001 From: Arnu Pretorius Date: Thu, 3 Jun 2021 10:21:31 +0200 Subject: [PATCH 122/188] Add table of contents to readme --- README.md | 17 ++++++++++++++--- 1 file changed, 14 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 82596cb16..8fbf6f517 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,18 @@ # Mava: a research framework for distributed multi-agent reinforcement learning -**[Overview](#overview)** | **[Installation](#installation)** | **[Systems]** | **[Examples]** | +# Table of Contents +1. [Overview](#overview) +2. [Support Environments](#supported-environments) +3. [System implementations](#system-implementations) +4. [Examples](#examples) +5. [Installation](#installation) +6. [Debugging](#debugging) +7. [Roadmap](#roadmap) +8. [Contributing](#contributing) +9. [Troubleshooting and FAQ](#troubleshooting-and-faqs) + + @@ -28,7 +39,7 @@ Several examples of system implementations can be viewed [here][Systems]. Mava shares much of the design philosophy of Acme for the same reason: to allow a high level of composability for novel research (i.e. building new systems) as well as making it possible to scale systems in a simple way, using the same underlying multi-agent RL system code. In the latter case, the system executor (which is responsible for data collection) is distributed across multiple processes each with a copy of the environment. Each process collects and stores data which the Trainer uses to update the parameters of the actor networks used within each executor. -### Supported environments and the system-environment loop +## Supported environments A given multi-agent system interacts with its environment via an `EnvironmentLoop`. This loop takes as input a `system` instance and a multi-agent `environment` instance which implements the [DeepMind Environment API][dm_env]. Mava currently supports multi-agent environment loops and environment wrappers for the following environments and environment suites: @@ -44,7 +55,7 @@ instance which implements the [DeepMind Environment API][dm_env]. Mava currently The animation on the right shows MAD4PG solving the Multi-Walker environment from PettingZoo. -## Implementations +## System implementations Mava includes several system implementations. Below we list these together with an indication of the maturity of the system using the following keys: 🟩 -- Tested and working well, 🟨 -- Running and training on simple environments, but not extensively tested and 🟥 -- Implemented but untested and yet to show clear signs of stable training. From a59e4bebc9d18d3956c24ea59b51c44c5580140c Mon Sep 17 00:00:00 2001 From: Arnu Pretorius Date: Thu, 3 Jun 2021 10:33:05 +0200 Subject: [PATCH 123/188] Add link to launchpad and minor edits --- README.md | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 8fbf6f517..a9d8210d8 100644 --- a/README.md +++ b/README.md @@ -2,9 +2,11 @@ # Mava: a research framework for distributed multi-agent reinforcement learning +![pytest](https://github.com/arnupretorius/mava/workflows/format_and_test/badge.svg) + # Table of Contents 1. [Overview](#overview) -2. [Support Environments](#supported-environments) +2. [Supported Environments](#supported-environments) 3. [System implementations](#system-implementations) 4. [Examples](#examples) 5. [Installation](#installation) @@ -17,7 +19,7 @@ -![pytest](https://github.com/arnupretorius/mava/workflows/format_and_test/badge.svg) + Mava is a library for building multi-agent reinforcement learning (MARL) systems. Mava builds off of [Acme][Acme] and in a similar way strives to expose simple, efficient, and readable components, as well as examples that serve both as reference implementations of popular algorithms and as strong baselines, while still providing enough flexibility to do novel research. @@ -32,12 +34,12 @@ At the core of the Mava framework is the concept of a `system`. A system refers

    -The `Executor` is the part of the system that interacts with the environment, takes actions for each agent and observes the next state as a collection of observations, one for each agent in the system. Essentially, executors are the multi-agent version of the Actor class in Acme and are themselves constructed through feeding to the executor a dictionary of policy networks. The `Trainer` is responsible for sampling data from the Dataset originally collected from the executor and updating the parameters for every agent in the system. Trainers are therefore the multi-agent version of the Learner class in Acme. The `Dataset` stores all of the information collected by the executors in the form of a collection of dictionaries for the actions, observations and rewards with keys corresponding to the individual agent ids. +The `Executor` is the part of the system that interacts with the environment, takes actions for each agent and observes the next state as a collection of observations, one for each agent in the system. Essentially, executors are the multi-agent version of the Actor class in Acme and are themselves constructed through feeding to the executor a dictionary of policy networks. The `Trainer` is responsible for sampling data from the Dataset originally collected from the executor and updating the parameters for every agent in the system. Trainers are therefore the multi-agent version of the Learner class in Acme. The `Dataset` stores all of the information collected by the executors in the form of a collection of dictionaries for the actions, observations and rewards with keys corresponding to the individual agent ids. The basic system design is shown on the left in the above figure. Several examples of system implementations can be viewed [here][Systems]. ### Distributed system training -Mava shares much of the design philosophy of Acme for the same reason: to allow a high level of composability for novel research (i.e. building new systems) as well as making it possible to scale systems in a simple way, using the same underlying multi-agent RL system code. In the latter case, the system executor (which is responsible for data collection) is distributed across multiple processes each with a copy of the environment. Each process collects and stores data which the Trainer uses to update the parameters of the actor networks used within each executor. +Mava shares much of the design philosophy of Acme for the same reason: to allow a high level of composability for novel research (i.e. building new systems) as well as making it possible to scale systems in a simple way, using the same underlying multi-agent RL system code. Mava uses [Launchpad](launchpad) for creating distributed programs. In Mava, the system executor (which is responsible for data collection) is distributed across multiple processes each with a copy of the environment. Each process collects and stores data which the Trainer uses to update the parameters of all the actor networks used within each executor. This approach to distributed system training is illustrated on the right in the figure above. ## Supported environments @@ -250,3 +252,4 @@ If you use Mava in your work, please cite the accompanying [dm_env]: https://github.com/deepmind/dm_env [pymarl]: https://github.com/oxwhirl/pymarl [mpe]: https://github.com/openai/multiagent-particle-envs +[launchpad]: https://github.com/deepmind/launchpad From eb0997f52618ad07e13601e5d0ac0b53fe6b024d Mon Sep 17 00:00:00 2001 From: Arnu Pretorius Date: Thu, 3 Jun 2021 10:52:23 +0200 Subject: [PATCH 124/188] debugging section and resize mava logo --- README.md | 8 ++------ 1 file changed, 2 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index a9d8210d8..906b38ebc 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ - + # Mava: a research framework for distributed multi-agent reinforcement learning @@ -198,17 +198,13 @@ We also have a list of [optional installs](OPTIONAL_INSTALL.md) for extra functi To test and debug new system implementations, we use a simplified version of the spread environment from the [MPE][mpe] suite. -Debuggin in MARL can be very difficult and time consuming, therefore it is important to use a small environment for debugging that is simple and fast but at the same time still able to clearly show whether a system is able to learn. An illustration of the debugging environment is shown on the right. Agents start at random locations and are assigned specific landmarks which they should attempt to reach in as few steps as possible. Rewards are given to each agent independently as a function of the distance that agent is from their landmark. To test both discrete and continous control systems we feature two version of the environment. In the discrete version the action space for each agent consists of the following five actions: `left`, `right`, `up`, `down`, `stand-still`. In the continuous case, the action space consists of real-values bounded between -1 and 1 for the acceleration of the agent in the x and y direction. - -### Feedforward systems +Debugging in MARL can be very difficult and time consuming, therefore it is important to use a small environment for debugging that is simple and fast but at the same time still able to clearly show whether a system is able to learn. An illustration of the debugging environment is shown on the right. Agents start at random locations and are assigned specific landmarks which they attempt to reach in as few steps as possible. Rewards are given to each agent independently as a function of their distance to the landmark. The reward is normalised to be between 0 and 1, where 1 is given when the agent is directly on top of the landmark. The further an agent is away from its landmark the more the reward value converges to 0. Collisions between agents result in a reward of -1 received by the colliding agents. To test both discrete and continous control systems we feature two versions of the environment. In the discrete version the action space for each agent consists of the following five actions: `left`, `right`, `up`, `down`, `stand-still`. In the continuous case, the action space consists of real-values bounded between -1 and 1 for the `acceleration` of the agent in the `x` and `y` direction. Below we show the results from some of our systems trained on the debugging environment.

    -### Recurrent systems - ## Roadmap [![](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Sequential%20environment%20support%20including%20OpenSpiel)](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Sequential%20environment%20support%20including%20OpenSpiel/vote)
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b/README.md @@ -1,4 +1,4 @@ - + # Mava: a research framework for distributed multi-agent reinforcement learning From e94d5f1f75d168f31bbfa51ee90e93838a6fd2df Mon Sep 17 00:00:00 2001 From: Arnu Pretorius Date: Thu, 3 Jun 2021 11:00:55 +0200 Subject: [PATCH 127/188] Add link to debug examples --- README.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index a7468ab02..c1ec10918 100644 --- a/README.md +++ b/README.md @@ -198,7 +198,7 @@ We also have a list of [optional installs](OPTIONAL_INSTALL.md) for extra functi To test and debug new system implementations, we use a simplified version of the spread environment from the [MPE][mpe] suite. -Debugging in MARL can be very difficult and time consuming, therefore it is important to use a small environment for debugging that is simple and fast but at the same time still able to clearly show whether a system is able to learn. An illustration of the debugging environment is shown on the right. Agents start at random locations and are assigned specific landmarks which they attempt to reach in as few steps as possible. Rewards are given to each agent independently as a function of their distance to the landmark. The reward is normalised to be between 0 and 1, where 1 is given when the agent is directly on top of the landmark. The further an agent is away from its landmark the more the reward value converges to 0. Collisions between agents result in a reward of -1 received by the colliding agents. To test both discrete and continous control systems we feature two versions of the environment. In the discrete version the action space for each agent consists of the following five actions: `left`, `right`, `up`, `down`, `stand-still`. In the continuous case, the action space consists of real-values bounded between -1 and 1 for the `acceleration` of the agent in the `x` and `y` direction. Below we show the results from some of our systems trained on the debugging environment. +Debugging in MARL can be very difficult and time consuming, therefore it is important to use a small environment for debugging that is simple and fast but at the same time still able to clearly show whether a system is able to learn. An illustration of the debugging environment is shown on the right. Agents start at random locations and are assigned specific landmarks which they attempt to reach in as few steps as possible. Rewards are given to each agent independently as a function of their distance to the landmark. The reward is normalised to be between 0 and 1, where 1 is given when the agent is directly on top of the landmark. The further an agent is away from its landmark the more the reward value converges to 0. Collisions between agents result in a reward of -1 received by the colliding agents. To test both discrete and continous control systems we feature two versions of the environment. In the discrete version the action space for each agent consists of the following five actions: `left`, `right`, `up`, `down`, `stand-still`. In the continuous case, the action space consists of real-values bounded between -1 and 1 for the `acceleration` of the agent in the `x` and `y` direction. Several examples of running systems on the debugging environment can be found [here](debug). Below we show the results from some of our systems trained on the debugging environment.

    @@ -235,7 +235,8 @@ If you use Mava in your work, please cite the accompanying [Acme]: https://github.com/deepmind/acme [Systems]: mava/systems/ -[Examples]: examples/ +[Examples]: examples +[debug]: examples/debugging_envs/ [Tutorial]: https://arxiv.org [Quickstart]: examples/quickstart.ipynb [Documentation]: www.mava.rl From 9487c079d5a0375b5bbf8a6f2b107472d277a208 Mon Sep 17 00:00:00 2001 From: Arnu Pretorius Date: Thu, 3 Jun 2021 11:25:38 +0200 Subject: [PATCH 128/188] Add roadmap section with poll --- README.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/README.md b/README.md index c1ec10918..cea5cf74a 100644 --- a/README.md +++ b/README.md @@ -207,10 +207,14 @@ Debugging in MARL can be very difficult and time consuming, therefore it is impo ## Roadmap +We have big ambitions for Mava! 🚀 But there is still much work that needs to be done. We have a clear roadmap for expanding our system implementations and associated modules, improving testing and robustness and providing support for across-machine training. Furthermore, in the near future, we also plan to implement the following features (feel free to vote for any one of them by clicking on the name!). + [![](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Sequential%20environment%20support%20including%20OpenSpiel)](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Sequential%20environment%20support%20including%20OpenSpiel/vote)
    [![](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Population%20based%20training)](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Population%20based%20training/vote)
    [![](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Dynamic%20networked%20architectures)](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Dynamic%20networked%20architectures/vote) +In the slightly more longer term, the Mava team plans to release benchmarking results for several different systems and environments and contribute a MARL specific behavioural environment suite specifically engineered to study aspects of MARL such as cooperation and coordination. + ## Contributing Please read our [contributing docs](./CONTRIBUTING.md) for details on how to submit pull requests, our Contributor License Agreement and community guidelines. 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Paper | Code -------------------------------------------------------------------- | :----------------------: | :--: -Multi-Agent Deep Deterministic Policy Gradient (MA-DDPG) | [Lowe et al., 2017] | [![TF](../../docs/logos/tf-small.png)][MADDPG_TF2] -Multi-Agent Distributed Distributional DDPG (MA-D4PG) | [Barth-Maron et al., 2018] | [![TF](../../docs/logos/tf-small.png)][MAD4PG_TF2] +Multi-Agent Deep Deterministic Policy Gradient (MA-DDPG) | [Lowe et al., 2017] | [![TF](../../docs/images/tf-small.png)][MADDPG_TF2] +Multi-Agent Distributed Distributional DDPG (MA-D4PG) | [Barth-Maron et al., 2018] | [![TF](../../docs/images/tf-small.png)][MAD4PG_TF2] ### Discrete control We also include a number of systems built with discrete action-spaces in mind listed below: Agent | Paper | Code -------------------------------------------------------- | :----------------------: | :--: -Deep Q-Networks (DQN) | [Horgan et al., 2018] | [![TF](../../docs/logos/tf-small.png)][DQN_TF2] -Differentiable Inter-Agent Learning (DIAL) | [Foerster et al., 2016] | [![TF](../../docs/logos/tf-small.png)][DIAL_TF2] -QMIX | [Rashid et al., 2018] | [![TF](../../docs/logos/tf-small.png)][QMIX_TF2] +Deep Q-Networks (DQN) | [Horgan et al., 2018] | [![TF](../../docs/images/tf-small.png)][DQN_TF2] +Differentiable Inter-Agent Learning (DIAL) | [Foerster et al., 2016] | [![TF](../../docs/images/tf-small.png)][DIAL_TF2] +QMIX | [Rashid et al., 2018] | [![TF](../../docs/images/tf-small.png)][QMIX_TF2] ### Mixed We also have a system that works with either discrete or continuous action-spaces: Agent | Paper | Code -------------------------------------------------------- | :----------------------: | :--: -Multi-Agent Proximal Policy Optimization (MA-PPO) | [Yu et al., 2021], [Schroeder et al., 2020] | [![TF](../../docs/logos/tf-small.png)][MAPPO_TF2] +Multi-Agent Proximal Policy Optimization (MA-PPO) | [Yu et al., 2021], [Schroeder et al., 2020] | [![TF](../../docs/images/tf-small.png)][MAPPO_TF2] From 00e48fead4d344c5ff551ffade3396ebf3fb685b Mon Sep 17 00:00:00 2001 From: Arnu Pretorius Date: Thu, 3 Jun 2021 11:52:00 +0200 Subject: [PATCH 131/188] Minor edits to system readme --- mava/systems/README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/mava/systems/README.md b/mava/systems/README.md index fbaa19d6f..c42964f48 100644 --- a/mava/systems/README.md +++ b/mava/systems/README.md @@ -1,6 +1,6 @@ # Systems -As with Acme, Mava includes a number of pre-built agents as listed below. All the systems are +Mava includes a number of pre-built systems as listed below. All the systems are implemented using [Launchpad](https://github.com/deepmind/launchpad), which is used for distributed training. This allows for easy scaling of computational resources by changing only one variable. @@ -11,7 +11,7 @@ Below we list the different systems in Mava based on the action spaces they use. The following systems focus on this setting: -Agent | Paper | Code +System | Paper | Code -------------------------------------------------------------------- | :----------------------: | :--: Multi-Agent Deep Deterministic Policy Gradient (MA-DDPG) | [Lowe et al., 2017] | [![TF](../../docs/images/tf-small.png)][MADDPG_TF2] Multi-Agent Distributed Distributional DDPG (MA-D4PG) | [Barth-Maron et al., 2018] | [![TF](../../docs/images/tf-small.png)][MAD4PG_TF2] @@ -19,7 +19,7 @@ Multi-Agent Distributed Distributional DDPG (MA-D4PG) | [Barth-Maron et al., ### Discrete control We also include a number of systems built with discrete action-spaces in mind listed below: -Agent | Paper | Code +System | Paper | Code -------------------------------------------------------- | :----------------------: | :--: Deep Q-Networks (DQN) | [Horgan et al., 2018] | [![TF](../../docs/images/tf-small.png)][DQN_TF2] Differentiable Inter-Agent Learning (DIAL) | [Foerster et al., 2016] | [![TF](../../docs/images/tf-small.png)][DIAL_TF2] @@ -28,7 +28,7 @@ QMIX | [Rashid et al., 2018] ### Mixed We also have a system that works with either discrete or continuous action-spaces: -Agent | Paper | Code +System | Paper | Code -------------------------------------------------------- | :----------------------: | :--: Multi-Agent Proximal Policy Optimization (MA-PPO) | [Yu et al., 2021], [Schroeder et al., 2020] | [![TF](../../docs/images/tf-small.png)][MAPPO_TF2] From a3bdc92b9a99a1d5e3e00846932e864a2fd1f7c4 Mon Sep 17 00:00:00 2001 From: Arnu Pretorius Date: Thu, 3 Jun 2021 11:53:38 +0200 Subject: [PATCH 132/188] update robocup link --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index cea5cf74a..d158f69d2 100644 --- a/README.md +++ b/README.md @@ -249,7 +249,7 @@ If you use Mava in your work, please cite the accompanying [smac]: https://github.com/oxwhirl/smac [openspiel]: https://github.com/deepmind/open_spiel [flatland]: https://gitlab.aicrowd.com/flatland/flatland -[robocup]: https://github.com/rcsoccersim/rcssserver +[robocup]: https://github.com/rcsoccersim [dm_env]: https://github.com/deepmind/dm_env [pymarl]: https://github.com/oxwhirl/pymarl [mpe]: https://github.com/openai/multiagent-particle-envs From 89d3b67742194af392c13f84ced97d6fad055932 Mon Sep 17 00:00:00 2001 From: Arnu Pretorius Date: Thu, 3 Jun 2021 12:02:45 +0200 Subject: [PATCH 133/188] fix broken debug env link --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index d158f69d2..6410d9225 100644 --- a/README.md +++ b/README.md @@ -198,7 +198,7 @@ We also have a list of [optional installs](OPTIONAL_INSTALL.md) for extra functi To test and debug new system implementations, we use a simplified version of the spread environment from the [MPE][mpe] suite. -Debugging in MARL can be very difficult and time consuming, therefore it is important to use a small environment for debugging that is simple and fast but at the same time still able to clearly show whether a system is able to learn. An illustration of the debugging environment is shown on the right. Agents start at random locations and are assigned specific landmarks which they attempt to reach in as few steps as possible. Rewards are given to each agent independently as a function of their distance to the landmark. The reward is normalised to be between 0 and 1, where 1 is given when the agent is directly on top of the landmark. The further an agent is away from its landmark the more the reward value converges to 0. Collisions between agents result in a reward of -1 received by the colliding agents. To test both discrete and continous control systems we feature two versions of the environment. In the discrete version the action space for each agent consists of the following five actions: `left`, `right`, `up`, `down`, `stand-still`. In the continuous case, the action space consists of real-values bounded between -1 and 1 for the `acceleration` of the agent in the `x` and `y` direction. Several examples of running systems on the debugging environment can be found [here](debug). Below we show the results from some of our systems trained on the debugging environment. +Debugging in MARL can be very difficult and time consuming, therefore it is important to use a small environment for debugging that is simple and fast but at the same time still able to clearly show whether a system is able to learn. An illustration of the debugging environment is shown on the right. Agents start at random locations and are assigned specific landmarks which they attempt to reach in as few steps as possible. Rewards are given to each agent independently as a function of their distance to the landmark. The reward is normalised to be between 0 and 1, where 1 is given when the agent is directly on top of the landmark. The further an agent is away from its landmark the more the reward value converges to 0. Collisions between agents result in a reward of -1 received by the colliding agents. To test both discrete and continous control systems we feature two versions of the environment. In the discrete version the action space for each agent consists of the following five actions: `left`, `right`, `up`, `down`, `stand-still`. In the continuous case, the action space consists of real-values bounded between -1 and 1 for the `acceleration` of the agent in the `x` and `y` direction. Several examples of running systems on the debugging environment can be found [here][debug]. Below we show the results from some of our systems trained on the debugging environment.

    From a5ba9b50d29d284232d4d4a726670b7994d73635 Mon Sep 17 00:00:00 2001 From: Arnu Pretorius Date: Thu, 3 Jun 2021 12:20:27 +0200 Subject: [PATCH 134/188] Fix broken link in examples readme --- examples/README.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/examples/README.md b/examples/README.md index 909c8fb6b..2eca91531 100644 --- a/examples/README.md +++ b/examples/README.md @@ -4,7 +4,7 @@ This directory includes working examples of Mava systems. ## Environments In Mava we use a variety of different environments which include the -[petting_zoo] environment set as well as a few custom [environments](utils/debugging) inside Mava. +[PettingZoo][pettingzoo] and [Flatland][flatland] environment set as well as a few custom [environments][debug] inside Mava. ## Continuous control We include a number of systems running on continuous control tasks. @@ -78,3 +78,7 @@ We also include a number of systems running on discrete action space environment an MA-DQN system running on the StarCraft 3m environment. - [MA-DQN (recurrent)](smac/run_recurrent_madqn.py): an MA-DQN system running on the StarCraft 3m environment. + +[debug]: ../mava/utils/debugging +[pettingzoo]: https://github.com/PettingZoo-Team/PettingZoo +[flatland]: https://gitlab.aicrowd.com/flatland/flatland From b1c27a8071735201f0e646a7db0fb3d8f752fe54 Mon Sep 17 00:00:00 2001 From: Arnu Pretorius Date: Thu, 3 Jun 2021 12:29:01 +0200 Subject: [PATCH 135/188] fix spelling mistakes and remove unused links --- README.md | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 6410d9225..1dbd236da 100644 --- a/README.md +++ b/README.md @@ -140,7 +140,7 @@ We have tested `mava` on Python 3.6, 3.7 and 3.8. ```bash make run EXAMPLE=dir/to/example/example.py ``` - For example, `make run EXAMPLE=examples/petting_zoo/run_decentralised_feedforward_maddpg_continous.py`. Alternatively, run bash inside a docker container with mava installed, `make bash`, and from there examples can be run as follows: `python dir/to/example/example.py`. + For example, `make run EXAMPLE=examples/petting_zoo/run_decentralised_feedforward_maddpg_continuous.py`. Alternatively, run bash inside a docker container with mava installed, `make bash`, and from there examples can be run as follows: `python dir/to/example/example.py`. To run an example with tensorboard viewing enabled, you can run ```bash @@ -198,7 +198,7 @@ We also have a list of [optional installs](OPTIONAL_INSTALL.md) for extra functi To test and debug new system implementations, we use a simplified version of the spread environment from the [MPE][mpe] suite. -Debugging in MARL can be very difficult and time consuming, therefore it is important to use a small environment for debugging that is simple and fast but at the same time still able to clearly show whether a system is able to learn. An illustration of the debugging environment is shown on the right. Agents start at random locations and are assigned specific landmarks which they attempt to reach in as few steps as possible. Rewards are given to each agent independently as a function of their distance to the landmark. The reward is normalised to be between 0 and 1, where 1 is given when the agent is directly on top of the landmark. The further an agent is away from its landmark the more the reward value converges to 0. Collisions between agents result in a reward of -1 received by the colliding agents. To test both discrete and continous control systems we feature two versions of the environment. In the discrete version the action space for each agent consists of the following five actions: `left`, `right`, `up`, `down`, `stand-still`. In the continuous case, the action space consists of real-values bounded between -1 and 1 for the `acceleration` of the agent in the `x` and `y` direction. Several examples of running systems on the debugging environment can be found [here][debug]. Below we show the results from some of our systems trained on the debugging environment. +Debugging in MARL can be very difficult and time consuming, therefore it is important to use a small environment for debugging that is simple and fast but at the same time still able to clearly show whether a system is able to learn. An illustration of the debugging environment is shown on the right. Agents start at random locations and are assigned specific landmarks which they attempt to reach in as few steps as possible. Rewards are given to each agent independently as a function of their distance to the landmark. The reward is normalised to be between 0 and 1, where 1 is given when the agent is directly on top of the landmark. The further an agent is away from its landmark the more the reward value converges to 0. Collisions between agents result in a reward of -1 received by the colliding agents. To test both discrete and continuous control systems we feature two versions of the environment. In the discrete version the action space for each agent consists of the following five actions: `left`, `right`, `up`, `down`, `stand-still`. In the continuous case, the action space consists of real values bounded between -1 and 1 for the `acceleration` of the agent in the `x` and `y` direction. Several examples of running systems on the debugging environment can be found [here][debug]. Below we show the results from some of our systems trained on the debugging environment.

    @@ -241,9 +241,6 @@ If you use Mava in your work, please cite the accompanying [Systems]: mava/systems/ [Examples]: examples [debug]: examples/debugging_envs/ -[Tutorial]: https://arxiv.org -[Quickstart]: examples/quickstart.ipynb -[Documentation]: www.mava.rl [Paper]: https://arxiv.org [pettingzoo]: https://github.com/PettingZoo-Team/PettingZoo [smac]: https://github.com/oxwhirl/smac From 87dd47deae9be1808d7e5cfdad626464968b30db Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Thu, 3 Jun 2021 12:48:14 +0200 Subject: [PATCH 136/188] update discrete results on readme --- docs/images/discrete.png | Bin 63365 -> 55476 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/docs/images/discrete.png b/docs/images/discrete.png index b8970d747a4f9244b5424c18e39a4d96c77ef596..25aa1d66bea029fde493b55ad706bf275f13dd09 100644 GIT binary patch literal 55476 zcmdqIRajh26D>>v2@o8Dd$8c{7Th7YLvVNZKnO0u-Q5}72_D?tT?dE3`8V(PKj;43 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Date: Thu, 3 Jun 2021 13:28:41 +0200 Subject: [PATCH 137/188] Update license to apache to match code stubs --- LICENSE | 222 ++++++++++++++++++++++++++++++++++++++++++++++++++------ 1 file changed, 201 insertions(+), 21 deletions(-) diff --git a/LICENSE b/LICENSE index 11b8c5047..261eeb9e9 100644 --- a/LICENSE +++ b/LICENSE @@ -1,21 +1,201 @@ -MIT License - -Copyright (c) 2021 [...placeholder...] - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED 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From d9488c4a04f43d5d35242ef7fa12041f3692538b Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Thu, 3 Jun 2021 14:44:43 +0200 Subject: [PATCH 138/188] removed qmix v2 --- .../debugging_envs/run_feedforward_qmix_v2.py | 175 ------- .../debugging_envs/run_feedforward_vdn.py | 4 +- mava/components/tf/modules/mixing/__init__.py | 2 - .../tf/modules/mixing/transformed.py | 75 --- mava/components/tf/modules/mixing/weighted.py | 44 -- mava/components/tf/networks/additive.py | 3 +- mava/components/tf/networks/mixers.py | 108 ----- mava/systems/tf/qmixV2/README.md | 0 mava/systems/tf/qmixV2/__init__.py | 19 - mava/systems/tf/qmixV2/builder.py | 327 ------------- mava/systems/tf/qmixV2/execution.py | 283 ------------ mava/systems/tf/qmixV2/system.py | 436 ------------------ mava/systems/tf/qmixV2/training.py | 366 --------------- 13 files changed, 4 insertions(+), 1838 deletions(-) delete mode 100644 examples/debugging_envs/run_feedforward_qmix_v2.py delete mode 100644 mava/components/tf/modules/mixing/transformed.py delete mode 100644 mava/components/tf/modules/mixing/weighted.py delete mode 100644 mava/components/tf/networks/mixers.py delete mode 100644 mava/systems/tf/qmixV2/README.md delete mode 100644 mava/systems/tf/qmixV2/__init__.py delete mode 100644 mava/systems/tf/qmixV2/builder.py delete mode 100644 mava/systems/tf/qmixV2/execution.py delete mode 100644 mava/systems/tf/qmixV2/system.py delete mode 100644 mava/systems/tf/qmixV2/training.py diff --git a/examples/debugging_envs/run_feedforward_qmix_v2.py b/examples/debugging_envs/run_feedforward_qmix_v2.py deleted file mode 100644 index 2dcc6dc88..000000000 --- a/examples/debugging_envs/run_feedforward_qmix_v2.py +++ /dev/null @@ -1,175 +0,0 @@ -# python3 -# Copyright 2021 [...placeholder...]. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import functools -from datetime import datetime -from typing import Any, Dict, Mapping, Optional, Sequence, Union - -import launchpad as lp -import sonnet as snt -import tensorflow as tf -from absl import app, flags -from acme import types -from acme.tf import networks -from launchpad.nodes.python.local_multi_processing import PythonProcess - -from mava import specs as mava_specs -from mava.components.tf.modules.exploration import LinearExplorationScheduler -from mava.components.tf.networks import epsilon_greedy_action_selector -from mava.components.tf.networks.mixers import QMixer -from mava.systems.tf import qmixV2 -from mava.utils import lp_utils -from mava.utils.environments import debugging_utils -from mava.utils.loggers import logger_utils - -FLAGS = flags.FLAGS -flags.DEFINE_string( - "env_name", - "simple_spread", - "Debugging environment name (str).", -) -flags.DEFINE_string( - "action_space", - "discrete", - "Environment action space type (str).", -) - -flags.DEFINE_string( - "mava_id", - str(datetime.now()), - "Experiment identifier that can be used to continue experiments.", -) -flags.DEFINE_string("base_dir", "./logs/", "Base dir to store experiments.") - - -def make_networks( - environment_spec: mava_specs.MAEnvironmentSpec, - q_networks_layer_sizes: Union[Dict[str, Sequence], Sequence] = ( - 512, - 512, - 256, - ), - shared_weights: bool = True, -) -> Mapping[str, types.TensorTransformation]: - """Creates networks used by the agents.""" - - specs = environment_spec.get_agent_specs() - - # Create agent_type specs - if shared_weights: - type_specs = {key.split("_")[0]: specs[key] for key in specs.keys()} - specs = type_specs - - if isinstance(q_networks_layer_sizes, Sequence): - q_networks_layer_sizes = {key: q_networks_layer_sizes for key in specs.keys()} - - def action_selector_fn( - q_values: types.NestedTensor, - legal_actions: types.NestedTensor, - epsilon: Optional[tf.Variable] = None, - ) -> types.NestedTensor: - return epsilon_greedy_action_selector( - action_values=q_values, legal_actions_mask=legal_actions, epsilon=epsilon - ) - - q_networks = {} - action_selectors = {} - for key in specs.keys(): - - # Get total number of action dimensions from action spec. - num_dimensions = specs[key].actions.num_values - - # Create the policy network. - q_network = snt.Sequential( - [ - networks.LayerNormMLP( - q_networks_layer_sizes[key], activate_final=False - ), - networks.NearZeroInitializedLinear(num_dimensions), - ] - ) - - # epsilon greedy action selector - action_selector = action_selector_fn - - q_networks[key] = q_network - action_selectors[key] = action_selector - - return { - "q_networks": q_networks, - "action_selectors": action_selectors, - } - - -def main(_: Any) -> None: - - # environment - environment_factory = functools.partial( - debugging_utils.make_environment, - env_name=FLAGS.env_name, - action_space=FLAGS.action_space, - num_agents=3, - return_state_info=True, - ) - - # networks - network_factory = lp_utils.partial_kwargs(make_networks) - - # Checkpointer appends "Checkpoints" to checkpoint_dir - checkpoint_dir = f"{FLAGS.base_dir}/{FLAGS.mava_id}" - - # loggers - log_every = 10 - logger_factory = functools.partial( - logger_utils.make_logger, - directory=FLAGS.base_dir, - to_terminal=True, - to_tensorboard=True, - time_stamp=FLAGS.mava_id, - time_delta=log_every, - ) - - # distributed program - program = qmixV2.QMix( - environment_factory=environment_factory, - network_factory=network_factory, - logger_factory=logger_factory, - num_executors=2, - exploration_scheduler_fn=LinearExplorationScheduler, - epsilon_min=0.05, - epsilon_decay=3e-4, - mixer_fn=QMixer, - optimizer=snt.optimizers.RMSProp(learning_rate=1e-4), - checkpoint_subpath=checkpoint_dir, - ).build() - - # launch - gpu_id = -1 - env_vars = {"CUDA_VISIBLE_DEVICES": str(gpu_id)} - local_resources = { - "trainer": [], - "evaluator": PythonProcess(env=env_vars), - "executor": PythonProcess(env=env_vars), - } - lp.launch( - program, - lp.LaunchType.LOCAL_MULTI_PROCESSING, - terminal="current_terminal", - local_resources=local_resources, - ) - - -if __name__ == "__main__": - app.run(main) diff --git a/examples/debugging_envs/run_feedforward_vdn.py b/examples/debugging_envs/run_feedforward_vdn.py index fae4ea2a4..a420de188 100644 --- a/examples/debugging_envs/run_feedforward_vdn.py +++ b/examples/debugging_envs/run_feedforward_vdn.py @@ -149,9 +149,9 @@ def main(_: Any) -> None: num_executors=2, exploration_scheduler_fn=LinearExplorationScheduler, epsilon_min=0.05, - epsilon_decay=3e-4, + epsilon_decay=5e-4, + optimizer=snt.optimizers.Adam(learning_rate=1e-4), max_replay_size=1000000, - optimizer=snt.optimizers.RMSProp(learning_rate=1e-4), checkpoint_subpath=checkpoint_dir, ).build() diff --git a/mava/components/tf/modules/mixing/__init__.py b/mava/components/tf/modules/mixing/__init__.py index a6bab6bdf..deb69a39b 100644 --- a/mava/components/tf/modules/mixing/__init__.py +++ b/mava/components/tf/modules/mixing/__init__.py @@ -18,5 +18,3 @@ from mava.components.tf.modules.mixing.additive import AdditiveMixing from mava.components.tf.modules.mixing.base import BaseMixingModule from mava.components.tf.modules.mixing.monotonic import MonotonicMixing -from mava.components.tf.modules.mixing.transformed import QTranAlt, QTranBase -from mava.components.tf.modules.mixing.weighted import WeightedMixing diff --git a/mava/components/tf/modules/mixing/transformed.py b/mava/components/tf/modules/mixing/transformed.py deleted file mode 100644 index 23f1aa7c6..000000000 --- a/mava/components/tf/modules/mixing/transformed.py +++ /dev/null @@ -1,75 +0,0 @@ -# python3 -# Copyright 2021 [...placeholder...]. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# TODO (StJohn): complete class for transformed mixing - -"""Mixing for multi-agent RL systems""" - -from typing import Dict - -import sonnet as snt - -from mava.components.tf.architectures import BaseArchitecture -from mava.components.tf.modules.mixing import BaseMixingModule - - -class QTranBase(BaseMixingModule): - """Multi-agent mixing architecture.""" - - def __init__( - self, - architecture: BaseArchitecture, - ) -> None: - """Initializes the mixer. - Args: - architecture: the BaseArchitecture used. - """ - super(QTranBase, self).__init__() - - self._architecture = architecture - - def create_mixing_layer(self) -> Dict[str, Dict[str, snt.Module]]: - # Implement method from base class - # TODO Call mixing network from networks folder. - return {} - - def create_system(self) -> Dict[str, Dict[str, snt.Module]]: - # Implement method from base class - return {} - - -class QTranAlt(BaseMixingModule): - """Multi-agent mixing architecture.""" - - def __init__( - self, - architecture: BaseArchitecture, - ) -> None: - """Initializes the mixer. - Args: - architecture: the BaseArchitecture used. - """ - super(QTranAlt, self).__init__() - - self._architecture = architecture - - def create_mixing_layer(self) -> Dict[str, Dict[str, snt.Module]]: - # Implement method from base class - # TODO Call mixing network from networks folder. - return {} - - def create_system(self) -> Dict[str, Dict[str, snt.Module]]: - # Implement method from base class - return {} diff --git a/mava/components/tf/modules/mixing/weighted.py b/mava/components/tf/modules/mixing/weighted.py deleted file mode 100644 index fbb625546..000000000 --- a/mava/components/tf/modules/mixing/weighted.py +++ /dev/null @@ -1,44 +0,0 @@ -# python3 -# Copyright 2021 [...placeholder...]. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# TODO (StJohn): complete class for weighted mixing -# https://github.com/oxwhirl/wqmix/tree/master/src/modules/mixers -"""Mixing for multi-agent RL systems""" - -from typing import Dict - -import sonnet as snt - -from mava.components.tf.architectures import BaseArchitecture -from mava.components.tf.modules.mixing import BaseMixingModule - - -class WeightedMixing(BaseMixingModule): - """Multi-agent mixing architecture.""" - - def __init__( - self, - architecture: BaseArchitecture, - ) -> None: - self._architecture = architecture - - def create_mixing_layer(self) -> Dict[str, Dict[str, snt.Module]]: - # Implement method from base class - # TODO Call mixing network from networks folder. - return {} - - def create_system(self) -> Dict[str, Dict[str, snt.Module]]: - # Implement method from base class - return {} diff --git a/mava/components/tf/networks/additive.py b/mava/components/tf/networks/additive.py index db827f0a5..c7398afe8 100644 --- a/mava/components/tf/networks/additive.py +++ b/mava/components/tf/networks/additive.py @@ -26,4 +26,5 @@ def __init__(self) -> None: def __call__(self, q_values: tf.Tensor) -> tf.Tensor: """Monotonic mixing logic.""" - return tf.math.reduce_sum(q_values, axis=1) + # return tf.math.reduce_sum(q_values, axis=1) + return tf.math.reduce_sum(q_values, keepdims=True) diff --git a/mava/components/tf/networks/mixers.py b/mava/components/tf/networks/mixers.py deleted file mode 100644 index cc1884abf..000000000 --- a/mava/components/tf/networks/mixers.py +++ /dev/null @@ -1,108 +0,0 @@ -from typing import Any - -import numpy as np -import sonnet as snt -import tensorflow as tf - - -class AdditiveMixingNetwork(snt.Module): - """Multi-agent monotonic mixing architecture.""" - - def __init__(self) -> None: - """Initializes the mixer.""" - super(AdditiveMixingNetwork, self).__init__() - - def __call__(self, q_values: tf.Tensor) -> tf.Tensor: - """Monotonic mixing logic.""" - return tf.math.reduce_sum(q_values, keepdims=True) - - -class QMixer(snt.Module): - def __init__(self, n_agents: Any, state_shape: Any, mixing_embed_dim: Any) -> None: - super().__init__() - self.n_agents = n_agents - self.embed_dim = mixing_embed_dim - self.state_dim = int(np.prod(state_shape)) - - self.hyper_w_1 = snt.Linear(self.embed_dim * self.n_agents) - self.hyper_w_final = snt.Linear(self.embed_dim) - - # State dependent bias for hidden layer - self.hyper_b_1 = snt.Linear(self.embed_dim) - - # V(s) instead of a bias for the last layers - self.V = snt.Sequential( - [snt.Linear(self.embed_dim), tf.keras.layers.ReLU(), snt.Linear(1)] - ) - - # self.linear_layer = snt.Sequential( - # [snt.Linear(self.n_agents)] # , tf.keras.layers.ReLU()] - # ) - - def __call__(self, agent_qs: Any, states: Any) -> tf.Tensor: - """Forward pass for the mixer. - Args: - agent_qs: Tensor of shape [B, n_agents] - states: Tensor of shape [B, state_dim] - """ - bs = agent_qs.shape[0] - states = tf.reshape(states, (-1, self.state_dim)) - agent_qs = tf.reshape(agent_qs, (-1, 1, self.n_agents)) - # First layer - w1 = tf.math.abs(self.hyper_w_1(states)) - b1 = self.hyper_b_1(states) - w1 = tf.reshape(w1, (-1, self.n_agents, self.embed_dim)) - b1 = tf.reshape(b1, (-1, 1, self.embed_dim)) - hidden = tf.keras.activations.elu(tf.linalg.matmul(agent_qs, w1) + b1) - # Second layer - w_final = tf.math.abs(self.hyper_w_final(states)) - w_final = tf.reshape(w_final, (-1, self.embed_dim, 1)) - # State-dependent bias - v = tf.reshape(self.V(states), (-1, 1, 1)) - # Compute final output - # print("hidden: ", hidden.shape) - # print("w_final: ", w_final.shape) - # print("v: ", v.shape) - y = tf.linalg.matmul(hidden, w_final) + v - # print("y: ", y.shape) - # Reshape and return - q_tot = tf.reshape(y, (bs, -1, 1)) - # print("q_tot: ", q_tot.shape) - return q_tot - - # hidden: (256, 1, 32) - # w_final: (256, 32, 1) - # tf.linalg.matmul(hidden, w_final): (256, 1, 1) - - # v: (256, 1, 1) - - # y: (256, 1, 1) - # q_tot: (256, 1, 1) - - # def __call__(self, agent_qs: Any, states: Any) -> tf.Tensor: - # """Forward pass for the mixer. - # Args: - # agent_qs: Tensor of shape [B, n_agents] - # states: Tensor of shape [B, state_dim] - # """ - # bs = agent_qs.shape[0] - # states = tf.reshape(states, (-1, self.state_dim)) # (1, num_states) - # print("states: ", states.shape) - # agent_qs = tf.reshape(agent_qs, (-1, 1, self.n_agents)) - # print("agent_qs: ", agent_qs.shape) - # # First layer - # w1 = tf.math.abs(self.hyper_w_1(states)) # bs, n_agents - # b1 = self.hyper_b_1(states) - # print("w1 before: ", w1.shape) - # w1 = tf.reshape(w1, (-1, self.n_agents, 1)) - # print("w1: ", w1.shape) - # b1 = tf.reshape(b1, (-1, 1, 1)) - # print("b1: ", b1.shape) - # # Compute final output - # print("agent_qs: ", agent_qs.shape) - # print("tf.transpose(w1): ", w1.shape) - # y = tf.linalg.matmul(agent_qs, w1) + b1 - # print("y: ", y.shape) - # q_tot = tf.reshape(y, (bs, -1, 1)) - # print("q_tot: ", q_tot.shape) - # return q_tot diff --git a/mava/systems/tf/qmixV2/README.md b/mava/systems/tf/qmixV2/README.md deleted file mode 100644 index e69de29bb..000000000 diff --git a/mava/systems/tf/qmixV2/__init__.py b/mava/systems/tf/qmixV2/__init__.py deleted file mode 100644 index a74c659df..000000000 --- a/mava/systems/tf/qmixV2/__init__.py +++ /dev/null @@ -1,19 +0,0 @@ -# python3 -# Copyright 2021 [...placeholder...]. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Implementations of a IDQN agent.""" - -from mava.systems.tf.qmixV2.system import QMix -from mava.systems.tf.qmixV2.training import QMixTrainer diff --git a/mava/systems/tf/qmixV2/builder.py b/mava/systems/tf/qmixV2/builder.py deleted file mode 100644 index 398b0f1b8..000000000 --- a/mava/systems/tf/qmixV2/builder.py +++ /dev/null @@ -1,327 +0,0 @@ -# python3 -# Copyright 2021 [...placeholder...]. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import dataclasses -from typing import Any, Dict, Iterator, List, Optional, Type - -import numpy as np -import reverb -import sonnet as snt -import tensorflow as tf -from acme import datasets -from acme.tf import utils as tf_utils -from acme.tf import variable_utils -from acme.utils import counting - -from mava import adders, core, specs, types -from mava.adders import reverb as reverb_adders -from mava.components.tf.modules.exploration.exploration_scheduling import ( - LinearExplorationScheduler, -) -from mava.components.tf.modules.stabilising import FingerPrintStabalisation -from mava.systems.tf import executors -from mava.systems.tf.qmixV2 import execution, training -from mava.wrappers import DetailedTrainerStatisticsWithEpsilon - - -@dataclasses.dataclass -class QMixConfig: - """Configuration options for the MADDPG system. - Args: - environment_spec: description of the actions, observations, etc. - discount: discount to use for TD updates. - batch_size: batch size for updates. - prefetch_size: size to prefetch from replay. - target_update_period: number of learner steps to perform before updating - the target networks. - min_replay_size: minimum replay size before updating. - max_replay_size: maximum replay size. - samples_per_insert: number of samples to take from replay for every insert - that is made. - n_step: number of steps to squash into a single transition. - sigma: standard deviation of zero-mean, Gaussian exploration noise. - clipping: whether to clip gradients by global norm. - replay_table_name: string indicating what name to give the replay table.""" - - environment_spec: specs.MAEnvironmentSpec - epsilon_min: float - epsilon_decay: float - shared_weights: bool - target_update_period: int - executor_variable_update_period: int - clipping: bool - min_replay_size: int - max_replay_size: int - samples_per_insert: Optional[float] - prefetch_size: int - batch_size: int - n_step: int - sequence_length: int - period: int - discount: float - checkpoint: bool - optimizer: snt.Optimizer - replay_table_name: str = reverb_adders.DEFAULT_PRIORITY_TABLE - checkpoint_subpath: str = "~/mava/" - - -class QMixBuilder: - """Builder for MADQN which constructs individual components of the system.""" - - """Defines an interface for defining the components of an RL system. - Implementations of this interface contain a complete specification of a - concrete RL system. An instance of this class can be used to build an - RL system which interacts with the environment either locally or in a - distributed setup. - """ - - def __init__( - self, - config: QMixConfig, - trainer_fn: Type[training.QMixTrainer] = training.QMixTrainer, - executor_fn: Type[core.Executor] = execution.QMixFeedForwardExecutor, - extra_specs: Dict[str, Any] = {}, - exploration_scheduler_fn: Type[ - LinearExplorationScheduler - ] = LinearExplorationScheduler, - replay_stabilisation_fn: Optional[Type[FingerPrintStabalisation]] = None, - mixer_fn: Optional[Type[snt.Module]] = None, - ): - """Args: - _config: Configuration options for the MADQN system. - _trainer_fn: Trainer module to use.""" - self._config = config - self._extra_specs = extra_specs - - self._agents = self._config.environment_spec.get_agent_ids() - self._agent_types = self._config.environment_spec.get_agent_types() - self._trainer_fn = trainer_fn - self._executor_fn = executor_fn - self._exploration_scheduler_fn = exploration_scheduler_fn - self._replay_stabiliser_fn = replay_stabilisation_fn - self._mixer_fn = mixer_fn - - def make_replay_tables( - self, - environment_spec: specs.MAEnvironmentSpec, - ) -> List[reverb.Table]: - """Create tables to insert data into.""" - - # Select adder - if issubclass(self._executor_fn, executors.FeedForwardExecutor): - # Check if we should use fingerprints - if self._replay_stabiliser_fn is not None: - self._extra_specs.update({"fingerprint": np.array([1.0, 1.0])}) - adder_sig = reverb_adders.ParallelNStepTransitionAdder.signature( - environment_spec, self._extra_specs - ) - elif issubclass(self._executor_fn, executors.RecurrentExecutor): - adder_sig = reverb_adders.ParallelSequenceAdder.signature( - environment_spec, self._extra_specs - ) - else: - raise NotImplementedError("Unknown executor type: ", self._executor_fn) - - if self._config.samples_per_insert is None: - # We will take a samples_per_insert ratio of None to mean that there is - # no limit, i.e. this only implies a min size limit. - limiter = reverb.rate_limiters.MinSize(self._config.min_replay_size) - - else: - # Create enough of an error buffer to give a 10% tolerance in rate. - samples_per_insert_tolerance = 0.1 * self._config.samples_per_insert - error_buffer = self._config.min_replay_size * samples_per_insert_tolerance - limiter = reverb.rate_limiters.SampleToInsertRatio( - min_size_to_sample=self._config.min_replay_size, - samples_per_insert=self._config.samples_per_insert, - error_buffer=error_buffer, - ) - - replay_table = reverb.Table( - name=self._config.replay_table_name, - sampler=reverb.selectors.Uniform(), - remover=reverb.selectors.Fifo(), - max_size=self._config.max_replay_size, - rate_limiter=limiter, - signature=adder_sig, - ) - - return [replay_table] - - def make_dataset_iterator( - self, replay_client: reverb.Client - ) -> Iterator[reverb.ReplaySample]: - """Create a dataset iterator to use for learning/updating the system. - Args: - replay_client: Reverb Client which points to the replay server.""" - - sequence_length = ( - self._config.sequence_length - if issubclass(self._executor_fn, executors.RecurrentExecutor) - else None - ) - - dataset = datasets.make_reverb_dataset( - table=self._config.replay_table_name, - server_address=replay_client.server_address, - batch_size=self._config.batch_size, - prefetch_size=self._config.prefetch_size, - sequence_length=sequence_length, - ) - return iter(dataset) - - def make_adder( - self, replay_client: reverb.Client - ) -> Optional[adders.ParallelAdder]: - """Create an adder which records data generated by the executor/environment. - Args: - replay_client: Reverb Client which points to the replay server.""" - - # Select adder - if issubclass(self._executor_fn, executors.FeedForwardExecutor): - adder = reverb_adders.ParallelNStepTransitionAdder( - priority_fns=None, - client=replay_client, - n_step=self._config.n_step, - discount=self._config.discount, - ) - elif issubclass(self._executor_fn, executors.RecurrentExecutor): - adder = reverb_adders.ParallelSequenceAdder( - priority_fns=None, - client=replay_client, - sequence_length=self._config.sequence_length, - period=self._config.period, - ) - else: - raise NotImplementedError("Unknown executor type: ", self._executor_fn) - - return adder - - def make_executor( - self, - q_networks: Dict[str, snt.Module], - action_selectors: Dict[str, Any], - adder: Optional[adders.ParallelAdder] = None, - variable_source: Optional[core.VariableSource] = None, - trainer: Optional[training.QMixTrainer] = None, - evaluator: bool = False, - ) -> core.Executor: - """Create an executor instance. - Args: - q_networks: A struct of instance of all - the different system q networks, - this should be a callable which takes as input observations - and returns actions. - adder: How data is recorded (e.g. added to replay). - variable_source: collection of (nested) numpy arrays. Contains - source variables as defined in mava.core. - """ - - shared_weights = self._config.shared_weights - - variable_client = None - if variable_source: - agent_keys = self._agent_types if shared_weights else self._agents - - # Create policy variables - variables = {agent: q_networks[agent].variables for agent in agent_keys} - # Get new policy variables - variable_client = variable_utils.VariableClient( - client=variable_source, - variables={"q_network": variables}, - update_period=self._config.executor_variable_update_period, - ) - - # Make sure not to use a random policy after checkpoint restoration by - # assigning variables before running the environment loop. - variable_client.update_and_wait() - - # Check if we should use fingerprints - fingerprint = True if self._replay_stabiliser_fn is not None else False - - # Create the executor which coordinates the actors. - return self._executor_fn( - q_networks=q_networks, - action_selectors=action_selectors, - shared_weights=shared_weights, - variable_client=variable_client, - adder=adder, - trainer=trainer, - evaluator=evaluator, - fingerprint=fingerprint, - ) - - def make_trainer( - self, - networks: Dict[str, Dict[str, snt.Module]], - dataset: Iterator[reverb.ReplaySample], - counter: Optional[counting.Counter] = None, - logger: Optional[types.NestedLogger] = None, - ) -> core.Trainer: - """Creates an instance of the trainer. - Args: - networks: struct describing the networks needed by the trainer; this can - be specific to the trainer in question. - dataset: iterator over samples from replay. - counter: a Counter which allows for recording of counts (trainer steps, - executor steps, etc.) distributed throughout the system. - logger: Logger object for logging metadata. - checkpoint: bool controlling whether the trainer checkpoints itself. - """ - q_networks = networks["values"] - target_q_networks = networks["target_values"] - - agents = self._config.environment_spec.get_agent_ids() - agent_types = self._config.environment_spec.get_agent_types() - - # Make epsilon scheduler - exploration_scheduler = self._exploration_scheduler_fn( - epsilon_min=self._config.epsilon_min, - epsilon_decay=self._config.epsilon_decay, - ) - - # Check if we should use fingerprints - fingerprint = True if self._replay_stabiliser_fn is not None else False - - # Mixer - if self._mixer_fn: - state_spec = self._config.environment_spec.get_extra_specs()["s_t"] - mixer = self._mixer_fn(len(agents), state_spec.shape, 32) - tf_utils.create_variables(mixer, [tf.ones(len(agents)), state_spec]) - - # The learner updates the parameters (and initializes them). - trainer = self._trainer_fn( - agents=agents, - agent_types=agent_types, - discount=self._config.discount, - q_networks=q_networks, - target_q_networks=target_q_networks, - shared_weights=self._config.shared_weights, - optimizer=self._config.optimizer, - target_update_period=self._config.target_update_period, - clipping=self._config.clipping, - exploration_scheduler=exploration_scheduler, - dataset=dataset, - counter=counter, - fingerprint=fingerprint, - logger=logger, - mixer=mixer, - checkpoint=self._config.checkpoint, - checkpoint_subpath=self._config.checkpoint_subpath, - ) - - trainer = DetailedTrainerStatisticsWithEpsilon(trainer) # type:ignore - - return trainer diff --git a/mava/systems/tf/qmixV2/execution.py b/mava/systems/tf/qmixV2/execution.py deleted file mode 100644 index fd4041306..000000000 --- a/mava/systems/tf/qmixV2/execution.py +++ /dev/null @@ -1,283 +0,0 @@ -# python3 -# Copyright 2021 [...placeholder...]. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -"""Executor implementations for MADQN.""" - -from typing import Any, Dict, Optional - -import dm_env -import numpy as np -import sonnet as snt -import tensorflow as tf -from acme import types - -# Internal imports. -from acme.tf import utils as tf2_utils -from acme.tf import variable_utils as tf2_variable_utils - -from mava import adders -from mava.systems.tf.executors import FeedForwardExecutor, RecurrentExecutor -from mava.systems.tf.qmixV2.training import QMixTrainer -from mava.types import OLT - - -class QMixFeedForwardExecutor(FeedForwardExecutor): - """A feed-forward executor. - An executor based on a feed-forward policy for each agent in the system - which takes non-batched observations and outputs non-batched actions. - It also allows adding experiences to replay and updating the weights - from the policy on the learner. - """ - - def __init__( - self, - q_networks: Dict[str, snt.Module], - action_selectors: Dict[str, snt.Module], - trainer: QMixTrainer, - shared_weights: bool = True, - adder: Optional[adders.ParallelAdder] = None, - variable_client: Optional[tf2_variable_utils.VariableClient] = None, - fingerprint: bool = False, - evaluator: bool = False, - ): - """Initializes the executor. - Args: - policy_network: the policy to run for each agent in the system. - shared_weights: specify if weights are shared between agent networks. - adder: the adder object to which allows to add experiences to a - dataset/replay buffer. - variable_client: object which allows to copy weights from the trainer copy - of the policies to the executor copy (in case they are separate). - """ - - # Store these for later use. - self._adder = adder - self._variable_client = variable_client - self._q_networks = q_networks - self._action_selectors = action_selectors - self._trainer = trainer - self._shared_weights = shared_weights - self._fingerprint = fingerprint - self._evaluator = evaluator - - @tf.function - def _policy( - self, - agent: str, - observation: types.NestedTensor, - legal_actions: types.NestedTensor, - epsilon: tf.Tensor, - fingerprint: Optional[tf.Tensor] = None, - ) -> types.NestedTensor: - - # Add a dummy batch dimension and as a side effect convert numpy to TF. - batched_observation = tf2_utils.add_batch_dim(observation) - batched_legals = tf2_utils.add_batch_dim(legal_actions) - - # index network either on agent type or on agent id - agent_key = agent.split("_")[0] if self._shared_weights else agent - - # Compute the policy, conditioned on the observation and - # possibly the fingerprint. - if fingerprint is not None: - q_values = self._q_networks[agent_key](batched_observation, fingerprint) - else: - q_values = self._q_networks[agent_key](batched_observation) - - # select legal action - action = self._action_selectors[agent_key]( - q_values, batched_legals, epsilon=epsilon - ) - - return action - - def select_action( - self, agent: str, observation: types.NestedArray - ) -> types.NestedArray: - raise NotImplementedError - - def observe_first( - self, - timestep: dm_env.TimeStep, - extras: Dict[str, types.NestedArray] = {}, - ) -> None: - - if self._fingerprint and self._trainer is not None: - epsilon = self._trainer.get_epsilon() - trainer_step = self._trainer.get_trainer_steps() - fingerprint = np.array([epsilon, trainer_step]) - extras.update({"fingerprint": fingerprint}) - - if self._adder: - self._adder.add_first(timestep, extras) - - def observe( - self, - actions: Dict[str, types.NestedArray], - next_timestep: dm_env.TimeStep, - next_extras: Dict[str, types.NestedArray] = {}, - ) -> None: - if self._fingerprint and self._trainer is not None: - trainer_step = self._trainer.get_trainer_steps() - epsilon = self._trainer.get_epsilon() - fingerprint = np.array([epsilon, trainer_step]) - next_extras.update({"fingerprint": fingerprint}) - - if self._adder: - self._adder.add(actions, next_timestep, next_extras) - - def select_actions( - self, observations: Dict[str, OLT] - ) -> Dict[str, types.NestedArray]: - actions = {} - for agent, observation in observations.items(): - # Pass the observation through the policy network. - if not self._evaluator: - epsilon = self._trainer.get_epsilon() - else: - epsilon = 0.0 - - epsilon = tf.convert_to_tensor(epsilon) - - if self._fingerprint: - trainer_step = self._trainer.get_trainer_steps() - fingerprint = tf.concat([epsilon, trainer_step], axis=0) - fingerprint = tf.expand_dims(fingerprint, axis=0) - fingerprint = tf.cast(fingerprint, "float32") - else: - fingerprint = None - - action = self._policy( - agent, - observation.observation, - observation.legal_actions, - epsilon, - fingerprint, - ) - - actions[agent] = tf2_utils.to_numpy_squeeze(action) - - # Return a numpy array with squeezed out batch dimension. - return actions - - def update(self, wait: bool = False) -> None: - if self._variable_client: - self._variable_client.update(wait) - - -class MADQNRecurrentExecutor(RecurrentExecutor): - """A feed-forward executor. - An executor based on a feed-forward policy for each agent in the system - which takes non-batched observations and outputs non-batched actions. - It also allows adding experiences to replay and updating the weights - from the policy on the learner. - """ - - def __init__( - self, - q_networks: Dict[str, snt.Module], - action_selectors: Dict[str, snt.Module], - shared_weights: bool = True, - adder: Optional[adders.ParallelAdder] = None, - variable_client: Optional[tf2_variable_utils.VariableClient] = None, - store_recurrent_state: bool = True, - trainer: QMixTrainer = None, - evaluator: bool = False, - fingerprint: bool = False, - ): - """Initializes the executor. - Args: - policy_network: the policy to run for each agent in the system. - shared_weights: specify if weights are shared between agent networks. - adder: the adder object to which allows to add experiences to a - dataset/replay buffer. - variable_client: object which allows to copy weights from the trainer copy - of the policies to the executor copy (in case they are separate). - """ - - # Store these for later use. - self._adder = adder - self._variable_client = variable_client - self._q_networks = q_networks - self._policy_networks = q_networks - self._action_selectors = action_selectors - self._store_recurrent_state = store_recurrent_state - self._trainer = trainer - self._shared_weights = shared_weights - - self._states: Dict[str, Any] = {} - - @tf.function - def _policy( - self, - agent: str, - observation: types.NestedTensor, - state: types.NestedTensor, - legal_actions: types.NestedTensor, - epsilon: tf.Tensor, - ) -> types.NestedTensor: - - # Add a dummy batch dimension and as a side effect convert numpy to TF. - batched_observation = tf2_utils.add_batch_dim(observation) - batched_legals = tf2_utils.add_batch_dim(legal_actions) - - # index network either on agent type or on agent id - agent_key = agent.split("_")[0] if self._shared_weights else agent - - # Compute the policy, conditioned on the observation. - q_values, new_state = self._q_networks[agent_key](batched_observation, state) - - # select legal action - action = self._action_selectors[agent_key]( - q_values, batched_legals, epsilon=epsilon - ) - - return action, new_state - - def select_action( - self, agent: str, observation: types.NestedArray - ) -> types.NestedArray: - raise NotImplementedError - - def select_actions( - self, observations: Dict[str, OLT] - ) -> Dict[str, types.NestedArray]: - actions = {} - - for agent, observation in observations.items(): - - # Pass the observation through the policy network. - if self._trainer is not None: - epsilon = self._trainer.get_epsilon() - else: - epsilon = 0.0 - - epsilon = tf.convert_to_tensor(epsilon) - - policy_output, new_state = self._policy( - agent, - observation.observation, - self._states[agent], - observation.legal_actions, - epsilon, - ) - - self._states[agent] = new_state - - actions[agent] = tf2_utils.to_numpy_squeeze(policy_output) - - # Return a numpy array with squeezed out batch dimension. - return actions diff --git a/mava/systems/tf/qmixV2/system.py b/mava/systems/tf/qmixV2/system.py deleted file mode 100644 index 85de21e64..000000000 --- a/mava/systems/tf/qmixV2/system.py +++ /dev/null @@ -1,436 +0,0 @@ -# python3 -# Copyright 2021 [...placeholder...]. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Defines the MADQN system class.""" -import functools -from typing import Any, Callable, Dict, Optional, Type - -import acme -import dm_env -import launchpad as lp -import reverb -import sonnet as snt -from acme import specs as acme_specs -from acme.tf import utils as tf2_utils -from acme.utils import counting - -import mava -from mava import core -from mava import specs as mava_specs -from mava.components.tf.architectures import DecentralisedValueActor -from mava.components.tf.modules.exploration import LinearExplorationScheduler -from mava.components.tf.modules.stabilising import FingerPrintStabalisation -from mava.environment_loop import ParallelEnvironmentLoop -from mava.systems.tf import executors -from mava.systems.tf import savers as tf2_savers -from mava.systems.tf.qmixV2 import builder, execution, training -from mava.utils import lp_utils -from mava.utils.loggers import MavaLogger, logger_utils -from mava.wrappers import DetailedPerAgentStatistics - - -class QMix: - """MADQN system. - This implements a single-process QMIX system. - Args: - environment_factory: Callable to instantiate an environment on a compute node. - network_factory: Callable to instantiate system networks on a compute node. - logger_factory: Callable to instantiate a system logger on a compute node. - architecture: system architecture, e.g. decentralised or centralised. - trainer_fn: training type associated with executor and architecture, - e.g. centralised training. - executor_fn: executor type for example feedforward or recurrent. - num_executors: number of executor processes to run in parallel. - num_caches: number of trainer node caches. - environment_spec: description of the actions, observations, etc. - q_networks: the online Q network (the one being optimized) - epsilon: probability of taking a random action; ignored if a policy - network is given. - trainer_fn: the class used for training the agent and mixing networks. - shared_weights: boolean determining whether shared weights is used. - target_update_period: number of learner steps to perform before updating - the target networks. - clipping: whether to clip gradients by global norm. - replay_table_name: string indicating what name to give the replay table. - max_replay_size: maximum replay size. - samples_per_insert: number of samples to take from replay for every insert - that is made. - prefetch_size: size to prefetch from replay. - batch_size: batch size for updates. - n_step: number of steps to squash into a single transition. - discount: discount to use for TD updates. - counter: counter object used to keep track of steps. - checkpoint: boolean indicating whether to checkpoint the learner. - """ - - def __init__( - self, - environment_factory: Callable[[bool], dm_env.Environment], - network_factory: Callable[[acme_specs.BoundedArray], Dict[str, snt.Module]], - logger_factory: Callable[[str], MavaLogger] = None, - architecture: Type[DecentralisedValueActor] = DecentralisedValueActor, - trainer_fn: Type[training.QMixTrainer] = training.QMixTrainer, - executor_fn: Type[core.Executor] = execution.QMixFeedForwardExecutor, - exploration_scheduler_fn: Type[ - LinearExplorationScheduler - ] = LinearExplorationScheduler, - replay_stabilisation_fn: Optional[Type[FingerPrintStabalisation]] = None, - mixer_fn: Optional[Type[snt.Module]] = None, - epsilon_min: float = 0.05, - epsilon_decay: float = 1e-4, - num_executors: int = 1, - num_caches: int = 0, - environment_spec: mava_specs.MAEnvironmentSpec = None, - shared_weights: bool = True, - batch_size: int = 256, - prefetch_size: int = 4, - min_replay_size: int = 1000, - max_replay_size: int = 1000000, - samples_per_insert: Optional[float] = 32.0, - n_step: int = 5, - sequence_length: int = 20, - period: int = 20, - clipping: bool = True, - discount: float = 0.99, - optimizer: snt.Optimizer = snt.optimizers.Adam(learning_rate=1e-4), - target_update_period: int = 100, - executor_variable_update_period: int = 1000, - max_executor_steps: int = None, - checkpoint: bool = True, - checkpoint_subpath: str = "~/mava/", - logger_config: Dict = {}, - train_loop_fn: Callable = ParallelEnvironmentLoop, - eval_loop_fn: Callable = ParallelEnvironmentLoop, - train_loop_fn_kwargs: Dict = {}, - eval_loop_fn_kwargs: Dict = {}, - ): - if not environment_spec: - environment_spec = mava_specs.MAEnvironmentSpec( - environment_factory(evaluation=False) # type:ignore - ) - - # set default logger if no logger provided - if not logger_factory: - logger_factory = functools.partial( - logger_utils.make_logger, - directory="~/mava", - to_terminal=True, - time_delta=10, - ) - - self._architecture = architecture - self._environment_factory = environment_factory - self._network_factory = network_factory - self._logger_factory = logger_factory - self._environment_spec = environment_spec - self._shared_weights = shared_weights - self._num_exectors = num_executors - self._num_caches = num_caches - self._max_executor_steps = max_executor_steps - self._checkpoint_subpath = checkpoint_subpath - self._checkpoint = checkpoint - self._logger_config = logger_config - self._train_loop_fn = train_loop_fn - self._train_loop_fn_kwargs = train_loop_fn_kwargs - self._eval_loop_fn = eval_loop_fn - self._eval_loop_fn_kwargs = eval_loop_fn_kwargs - - if issubclass(executor_fn, executors.RecurrentExecutor): - extra_specs = self._get_extra_specs() - else: - extra_specs = {} - - self._builder = builder.QMixBuilder( - builder.QMixConfig( - environment_spec=environment_spec, - epsilon_min=epsilon_min, - epsilon_decay=epsilon_decay, - shared_weights=shared_weights, - discount=discount, - batch_size=batch_size, - prefetch_size=prefetch_size, - target_update_period=target_update_period, - executor_variable_update_period=executor_variable_update_period, - min_replay_size=min_replay_size, - max_replay_size=max_replay_size, - samples_per_insert=samples_per_insert, - n_step=n_step, - sequence_length=sequence_length, - period=period, - clipping=clipping, - checkpoint=checkpoint, - optimizer=optimizer, - checkpoint_subpath=checkpoint_subpath, - ), - trainer_fn=trainer_fn, - executor_fn=executor_fn, - extra_specs=extra_specs, - exploration_scheduler_fn=exploration_scheduler_fn, - replay_stabilisation_fn=replay_stabilisation_fn, - mixer_fn=mixer_fn, - ) - - def _get_extra_specs(self) -> Any: - agents = self._environment_spec.get_agent_ids() - core_state_specs = {} - networks = self._network_factory( # type: ignore - environment_spec=self._environment_spec - ) - for agent in agents: - agent_type = agent.split("_")[0] - core_state_specs[agent] = ( - tf2_utils.squeeze_batch_dim( - networks["q_networks"][agent_type].initial_state(1) - ), - ) - return {"core_states": core_state_specs} - - def replay(self) -> Any: - """The replay storage.""" - return self._builder.make_replay_tables(self._environment_spec) - - def counter(self) -> Any: - return tf2_savers.CheckpointingRunner( - counting.Counter(), - time_delta_minutes=15, - directory=self._checkpoint_subpath, - subdirectory="counter", - ) - - def coordinator(self, counter: counting.Counter) -> Any: - return lp_utils.StepsLimiter(counter, self._max_executor_steps) # type: ignore - - def trainer( - self, - replay: reverb.Client, - counter: counting.Counter, - ) -> mava.core.Trainer: - """The Trainer part of the system.""" - - # Create the networks to optimize (online) - networks = self._network_factory( # type: ignore - environment_spec=self._environment_spec, shared_weights=self._shared_weights - ) - - # Create system architecture with target networks. - architecture = self._architecture( - environment_spec=self._environment_spec, - value_networks=networks["q_networks"], - shared_weights=self._shared_weights, - ) - - if self._builder._replay_stabiliser_fn is not None: - architecture = self._builder._replay_stabiliser_fn( # type: ignore - architecture - ) - - system_networks = architecture.create_system() - - # create logger - trainer_logger_config = {} - if self._logger_config and "trainer" in self._logger_config: - trainer_logger_config = self._logger_config["trainer"] - trainer_logger = self._logger_factory( # type: ignore - "trainer", **trainer_logger_config - ) - - dataset = self._builder.make_dataset_iterator(replay) - counter = counting.Counter(counter, "trainer") - - return self._builder.make_trainer( - networks=system_networks, - dataset=dataset, - counter=counter, - logger=trainer_logger, - ) - - def executor( - self, - executor_id: str, - replay: reverb.Client, - variable_source: acme.VariableSource, - counter: counting.Counter, - trainer: Optional[training.QMixTrainer] = None, - ) -> mava.ParallelEnvironmentLoop: - """The executor process.""" - - # Create the behavior policy. - networks = self._network_factory( # type: ignore - environment_spec=self._environment_spec, shared_weights=self._shared_weights - ) - - # Create system architecture with target networks. - architecture = self._architecture( - environment_spec=self._environment_spec, - value_networks=networks["q_networks"], - shared_weights=self._shared_weights, - ) - - if self._builder._replay_stabiliser_fn is not None: - architecture = self._builder._replay_stabiliser_fn( # type: ignore - architecture - ) - - system_networks = architecture.create_system() - - # Create the executor. - executor = self._builder.make_executor( - q_networks=system_networks["values"], - action_selectors=networks["action_selectors"], - adder=self._builder.make_adder(replay), - variable_source=variable_source, - trainer=trainer, - ) - - # TODO (Arnu): figure out why factory function are giving type errors - # Create the environment. - environment = self._environment_factory(evaluation=False) # type: ignore - - # Create logger and counter; actors will not spam bigtable. - counter = counting.Counter(counter, "executor") - - # Create executor logger - executor_logger_config = {} - if self._logger_config and "executor" in self._logger_config: - executor_logger_config = self._logger_config["executor"] - exec_logger = self._logger_factory( # type: ignore - f"executor_{executor_id}", **executor_logger_config - ) - - # Create the loop to connect environment and executor. - train_loop = self._train_loop_fn( - environment, - executor, - counter=counter, - logger=exec_logger, - **self._train_loop_fn_kwargs, - ) - - train_loop = DetailedPerAgentStatistics(train_loop) - - return train_loop - - def evaluator( - self, - variable_source: acme.VariableSource, - counter: counting.Counter, - trainer: training.QMixTrainer, - ) -> Any: - """The evaluation process.""" - - # Create the behavior policy. - networks = self._network_factory( # type: ignore - environment_spec=self._environment_spec, shared_weights=self._shared_weights - ) - - # Create system architecture with target networks. - architecture = self._architecture( - environment_spec=self._environment_spec, - value_networks=networks["q_networks"], - shared_weights=self._shared_weights, - ) - - if self._builder._replay_stabiliser_fn is not None: - architecture = self._builder._replay_stabiliser_fn( # type: ignore - architecture - ) - - system_networks = architecture.create_system() - - # Create the agent. - executor = self._builder.make_executor( - q_networks=system_networks["values"], - action_selectors=networks["action_selectors"], - variable_source=variable_source, - trainer=trainer, - evaluator=True, - ) - - # Make the environment. - environment = self._environment_factory(evaluation=True) # type: ignore - - # Create logger and counter. - counter = counting.Counter(counter, "evaluator") - evaluator_logger_config = {} - if self._logger_config and "evaluator" in self._logger_config: - evaluator_logger_config = self._logger_config["evaluator"] - eval_logger = self._logger_factory( # type: ignore - "evaluator", **evaluator_logger_config - ) - - # Create the run loop and return it. - # Create the loop to connect environment and executor. - eval_loop = self._eval_loop_fn( - environment, - executor, - counter=counter, - logger=eval_logger, - **self._eval_loop_fn_kwargs, - ) - - eval_loop = DetailedPerAgentStatistics(eval_loop) - return eval_loop - - def build(self, name: str = "madqn") -> Any: - """Build the distributed system topology.""" - program = lp.Program(name=name) - - with program.group("replay"): - replay = program.add_node(lp.ReverbNode(self.replay)) - - with program.group("counter"): - counter = program.add_node(lp.CourierNode(self.counter)) - - if self._max_executor_steps: - with program.group("coordinator"): - _ = program.add_node(lp.CourierNode(self.coordinator, counter)) - - with program.group("trainer"): - trainer = program.add_node(lp.CourierNode(self.trainer, replay, counter)) - - with program.group("evaluator"): - program.add_node(lp.CourierNode(self.evaluator, trainer, counter, trainer)) - - if not self._num_caches: - # Use the trainer as a single variable source. - sources = [trainer] - else: - with program.group("cacher"): - # Create a set of trainer caches. - sources = [] - for _ in range(self._num_caches): - cacher = program.add_node( - lp.CacherNode( - trainer, refresh_interval_ms=2000, stale_after_ms=4000 - ) - ) - sources.append(cacher) - - with program.group("executor"): - # Add executors which pull round-robin from our variable sources. - for executor_id in range(self._num_exectors): - source = sources[executor_id % len(sources)] - program.add_node( - lp.CourierNode( - self.executor, - executor_id, - replay, - source, - counter, - trainer, - ) - ) - - return program diff --git a/mava/systems/tf/qmixV2/training.py b/mava/systems/tf/qmixV2/training.py deleted file mode 100644 index 7616b80d2..000000000 --- a/mava/systems/tf/qmixV2/training.py +++ /dev/null @@ -1,366 +0,0 @@ -# python3 -# Copyright 2021 [...placeholder...]. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import copy -import time -from typing import Any, Dict, List, Optional, Sequence, Tuple - -import numpy as np -import sonnet as snt -import tensorflow as tf -import trfl -from acme.tf import utils as tf2_utils -from acme.types import NestedArray -from acme.utils import counting, loggers -from trfl.indexing_ops import batched_index - -import mava -from mava.components.tf.modules.exploration.exploration_scheduling import ( - LinearExplorationScheduler, -) -from mava.systems.tf import savers as tf2_savers -from mava.utils import training_utils as train_utils - - -class QMixTrainer(mava.Trainer): - """MADQN trainer. - This is the trainer component of a MADQN system. IE it takes a dataset as input - and implements update functionality to learn from this dataset. - """ - - def __init__( - self, - agents: List[str], - agent_types: List[str], - q_networks: Dict[str, snt.Module], - target_q_networks: Dict[str, snt.Module], - target_update_period: int, - dataset: tf.data.Dataset, - optimizer: snt.Optimizer, - discount: float, - shared_weights: bool, - exploration_scheduler: LinearExplorationScheduler, - clipping: bool = True, - fingerprint: bool = False, - mixer: Optional[snt.Module] = None, - counter: counting.Counter = None, - logger: loggers.Logger = None, - checkpoint: bool = True, - checkpoint_subpath: str = "~/mava/", - ): - - self._agents = agents - self._agent_types = agent_types - self._shared_weights = shared_weights - self._optimizer = optimizer - self._checkpoint = checkpoint - - # Store online and target q-networks. - self._q_networks = q_networks - self._target_q_networks = target_q_networks - - # General learner book-keeping and loggers. - self._counter = counter or counting.Counter() - self._logger = logger - - # Other learner parameters. - self._discount = discount - self._clipping = clipping - self._fingerprint = fingerprint - - # Necessary to track when to update target networks. - self._num_steps = tf.Variable(0, dtype=tf.int32) - self._target_update_period = target_update_period - - # Create an iterator to go through the dataset. - self._iterator = dataset - - # Store the exploration scheduler - self._exploration_scheduler = exploration_scheduler - - # mixer - self._mixer = mixer - self._target_mixer = copy.deepcopy(mixer) - - # Dictionary with network keys for each agent. - self.agent_net_keys = {agent: agent for agent in self._agents} - if self._shared_weights: - self.agent_net_keys = {agent: agent.split("_")[0] for agent in self._agents} - - self.unique_net_keys = self._agent_types if shared_weights else self._agents - - # Expose the variables. - q_networks_to_expose = {} - self._system_network_variables: Dict[str, Dict[str, snt.Module]] = { - "q_network": {}, - } - for agent_key in self.unique_net_keys: - q_network_to_expose = self._target_q_networks[agent_key] - - q_networks_to_expose[agent_key] = q_network_to_expose - - self._system_network_variables["q_network"][ - agent_key - ] = q_network_to_expose.variables - - # Checkpointer - self._system_checkpointer = {} - if checkpoint: - for agent_key in self.unique_net_keys: - - checkpointer = tf2_savers.Checkpointer( - directory=checkpoint_subpath, - time_delta_minutes=15, - objects_to_save={ - "counter": self._counter, - "q_network": self._q_networks[agent_key], - "target_q_network": self._target_q_networks[agent_key], - "optimizer": self._optimizer, - "num_steps": self._num_steps, - }, - enable_checkpointing=checkpoint, - ) - - self._system_checkpointer[agent_key] = checkpointer - - # Do not record timestamps until after the first learning step is done. - # This is to avoid including the time it takes for actors to come online and - # fill the replay buffer. - - self._timestamp: Optional[float] = None - - def get_epsilon(self) -> float: - return self._exploration_scheduler.get_epsilon() - - def get_trainer_steps(self) -> float: - return self._num_steps.numpy() - - def _decrement_epsilon(self) -> None: - self._exploration_scheduler.decrement_epsilon() - - def _update_target_networks(self) -> None: - for key in self.unique_net_keys: - # Update target network. - online_variables = [ - *self._q_networks[key].variables, - ] - - target_variables = [ - *self._target_q_networks[key].variables, - ] - - if self._mixer and self._target_mixer: - online_variables += self._mixer.variables - target_variables += self._target_mixer.variables - - # Make online -> target network update ops. - if tf.math.mod(self._num_steps, self._target_update_period) == 0: - for src, dest in zip(online_variables, target_variables): - dest.assign(src) - self._num_steps.assign_add(1) - - def _get_feed( - self, - o_tm1_trans: Dict[str, np.ndarray], - o_t_trans: Dict[str, np.ndarray], - a_tm1: Dict[str, np.ndarray], - agent: str, - ) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor]: - - # Decentralised - o_tm1_feed = o_tm1_trans[agent].observation - o_t_feed = o_t_trans[agent].observation - a_tm1_feed = a_tm1[agent] - - return o_tm1_feed, o_t_feed, a_tm1_feed - - def step(self) -> None: - # Run the learning step. - fetches = self._step() - - # Compute elapsed time. - timestamp = time.time() - if self._timestamp: - elapsed_time = timestamp - self._timestamp - else: - elapsed_time = 0 - self._timestamp = timestamp # type: ignore - - # Update our counts and record it. - counts = self._counter.increment(steps=1, walltime=elapsed_time) - fetches.update(counts) - - # Checkpoint and attempt to write the logs. - if self._checkpoint: - train_utils.checkpoint_networks(self._system_checkpointer) - - # Log and decrement epsilon - epsilon = self.get_epsilon() - fetches["epsilon"] = epsilon - self._decrement_epsilon() - - if self._logger: - self._logger.write(fetches) - - @tf.function - def _step( - self, - ) -> Dict[str, Dict[str, Any]]: - - # Update the target networks - self._update_target_networks() - - # Get data from replay (dropping extras if any). Note there is no - # extra data here because we do not insert any into Reverb. - inputs = next(self._iterator) - - self._forward(inputs) - - self._backward() - - # Log losses per agent - return {agent: {"q_value_loss": self.loss} for agent in self._agents} - - def _forward(self, inputs: Any) -> None: - # Unpack input data as follows: - # o_tm1 = dictionary of observations one for each agent - # a_tm1 = dictionary of actions taken from obs in o_tm1 - # r_t = dictionary of rewards or rewards sequences - # (if using N step transitions) ensuing from actions a_tm1 - # d_t = environment discount ensuing from actions a_tm1. - # This discount is applied to future rewards after r_t. - # o_t = dictionary of next observations or next observation sequences - # e_t [Optional] = extra data that the agents persist in replay. - o_tm1, a_tm1, e_tm1, r_t, d_t, o_t, e_t = inputs.data - s_tm1 = e_tm1["s_t"] - s_t = e_t["s_t"] - with tf.GradientTape(persistent=True) as tape: - q_network_losses: Dict[str, NestedArray] = {} - q_acts = [] - q_targets = [] - for agent in self._agents: - agent_key = self.agent_net_keys[agent] - - # Cast the additional discount to match the environment discount dtype. - discount = tf.cast(self._discount, dtype=d_t[agent].dtype) - - # Maybe transform the observation before feeding into policy and critic. - # Transforming the observations this way at the start of the learning - # step effectively means that the policy and critic share observation - # network weights. - - o_tm1_feed, o_t_feed, a_tm1_feed = self._get_feed( - o_tm1, o_t, a_tm1, agent - ) - q_tm1 = self._q_networks[agent_key](o_tm1_feed) - q_t_value = self._target_q_networks[agent_key](o_t_feed) - q_t_selector = self._q_networks[agent_key](o_t_feed) - best_action = tf.argmax(q_t_selector, axis=1, output_type=tf.int32) - - # TODO Make use of q_t_selector for fingerprinting. Speak to Claude. - q_act = batched_index(q_tm1, a_tm1_feed, keepdims=True) # [B, 1] - q_target = batched_index( - q_t_value, best_action, keepdims=True - ) # [B, 1] - - q_acts.append(q_act) - q_targets.append(q_target) - - if self._mixer and self._target_mixer: - # NOTE we expect a cooperative environment where agents share - # the same scalar reward and discount. - rewards = tf.concat( - [tf.reshape(val, (-1, 1)) for val in list(r_t.values())], axis=1 - ) - rewards = tf.reduce_mean(rewards, axis=1) # [B] - - pcont = tf.concat( - [tf.reshape(val, (-1, 1)) for val in list(d_t.values())], axis=1 - ) - pcont = tf.reduce_mean(pcont, axis=1) - discount = tf.cast(self._discount, list(d_t.values())[0].dtype) - pcont = discount * pcont # [B] - - q_acts = tf.concat(q_acts, axis=1) # [B, num_agents] - q_targets = tf.concat(q_targets, axis=1) # [B, num_agents] - - q_tot_mixed = self._mixer(q_acts, s_tm1) # [B, 1, 1] - q_tot_target_mixed = self._target_mixer(q_targets, s_t) # [B, 1, 1] - - # q_tot_mixed = tf.reduce_sum(q_acts, axis=1) # [B, 1, 1] - # q_tot_target_mixed = tf.reduce_sum(q_targets, axis=1) # [B, 1, 1] - - q_tot_mixed = tf.reshape(q_tot_mixed, (-1,)) - q_tot_target_mixed = tf.reshape(q_tot_target_mixed, (-1,)) - - # Calculate Q loss. - targets = rewards + pcont * q_tot_target_mixed - targets = tf.stop_gradient(targets) - td_error = targets - q_tot_mixed - - # Loss is MSE scaled by 0.5, so the gradient is equal to the TD error. - self.loss = 0.5 * tf.reduce_mean(tf.square(td_error)) - self.tape = tape - else: - # Q-network learning - loss, _ = trfl.double_qlearning( - q_tm1, - a_tm1_feed, - r_t[agent], - discount * d_t[agent], - q_t_value, - q_t_selector, - ) - - loss = tf.reduce_mean(loss) - - q_network_losses[agent] = {"q_value_loss": loss} - self._q_network_losses = q_network_losses - self.tape = tape - - def _backward(self) -> None: - loss = self.loss - tape = self.tape - variables = [] - for agent in self._agents: - agent_key = self.agent_net_keys[agent] - - # Get trainable variables - variables = self._q_networks[agent_key].trainable_variables - if self._mixer: - variables += self._mixer.trainable_variables - - # Compute gradients - gradients = tape.gradient(loss, variables) - - # Maybe clip gradients. - if self._clipping: - gradients = tf.clip_by_global_norm(gradients, 40.0)[0] - - # Apply gradients. - self._optimizer.apply(gradients, variables) - - train_utils.safe_del(self, "tape") - - def get_variables(self, names: Sequence[str]) -> Dict[str, Dict[str, np.ndarray]]: - variables: Dict[str, Dict[str, np.ndarray]] = {} - for network_type in names: - variables[network_type] = { - agent: tf2_utils.to_numpy( - self._system_network_variables[network_type][agent] - ) - for agent in self.unique_net_keys - } - return variables From 1d78da65b71a7e231245b04f394736bff82098e0 Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Thu, 3 Jun 2021 14:45:45 +0200 Subject: [PATCH 139/188] change additive network back to how it was --- mava/components/tf/networks/additive.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mava/components/tf/networks/additive.py b/mava/components/tf/networks/additive.py index c7398afe8..cf5549439 100644 --- a/mava/components/tf/networks/additive.py +++ b/mava/components/tf/networks/additive.py @@ -27,4 +27,4 @@ def __init__(self) -> None: def __call__(self, q_values: tf.Tensor) -> tf.Tensor: """Monotonic mixing logic.""" # return tf.math.reduce_sum(q_values, axis=1) - return tf.math.reduce_sum(q_values, keepdims=True) + return tf.math.reduce_sum(q_values, axis=1) From 3253bad6ceb8201049144e4cdf26493789d30c95 Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Thu, 3 Jun 2021 15:05:59 +0200 Subject: [PATCH 140/188] update system and building to use mixing module instead of network --- mava/systems/tf/qmix/builder.py | 10 ++++------ mava/systems/tf/qmix/system.py | 15 ++++++++------- 2 files changed, 12 insertions(+), 13 deletions(-) diff --git a/mava/systems/tf/qmix/builder.py b/mava/systems/tf/qmix/builder.py index 528cb4f65..0f6cbe20c 100644 --- a/mava/systems/tf/qmix/builder.py +++ b/mava/systems/tf/qmix/builder.py @@ -29,7 +29,7 @@ LinearExplorationScheduler, ) from mava.components.tf.modules.stabilising import FingerPrintStabalisation -from mava.components.tf.networks import MonotonicMixingNetwork +from mava.components.tf.modules.mixing import MonotonicMixing from mava.systems.tf.madqn.builder import MADQNBuilder, MADQNConfig from mava.systems.tf.qmix import execution, training from mava.wrappers import DetailedTrainerStatisticsWithEpsilon @@ -73,7 +73,7 @@ def __init__( config: QMIXConfig, trainer_fn: Type[training.QMIXTrainer] = training.QMIXTrainer, executor_fn: Type[core.Executor] = execution.QMIXFeedForwardExecutor, - mixer: Type[MonotonicMixingNetwork] = MonotonicMixingNetwork, + mixer: Type[MonotonicMixing] = MonotonicMixing, extra_specs: Dict[str, Any] = {}, exploration_scheduler_fn: Type[ LinearExplorationScheduler @@ -117,10 +117,8 @@ def make_trainer( q_networks = networks["values"] target_q_networks = networks["target_values"] - mixing_network = self._mixer(len(agents)) - state_spec = self._config.environment_spec.get_extra_specs()["s_t"] - tf_utils.create_variables(mixing_network, [tf.ones(len(agents)), state_spec]) - target_mixing_network = copy.deepcopy(mixing_network) + mixing_network = networks["mixing"] + target_mixing_network = networks["target_mixing"] # Make epsilon scheduler exploration_scheduler = self._exploration_scheduler_fn( diff --git a/mava/systems/tf/qmix/system.py b/mava/systems/tf/qmix/system.py index 3a98cd1bc..80bbf29ef 100644 --- a/mava/systems/tf/qmix/system.py +++ b/mava/systems/tf/qmix/system.py @@ -30,7 +30,7 @@ from mava.components.tf.modules.communication import BaseCommunicationModule from mava.components.tf.modules.exploration import LinearExplorationScheduler from mava.components.tf.modules.stabilising import FingerPrintStabalisation -from mava.components.tf.networks import MonotonicMixingNetwork +from mava.components.tf.modules.mixing import MonotonicMixing from mava.environment_loop import ParallelEnvironmentLoop from mava.systems.tf import executors from mava.systems.tf.madqn.system import MADQN @@ -82,7 +82,7 @@ def __init__( architecture: Type[DecentralisedValueActor] = DecentralisedValueActor, trainer_fn: Type[training.QMIXTrainer] = training.QMIXTrainer, executor_fn: Type[core.Executor] = execution.QMIXFeedForwardExecutor, - mixer: Type[MonotonicMixingNetwork] = MonotonicMixingNetwork, + mixer: Type[MonotonicMixing] = MonotonicMixing, communication_module: Type[BaseCommunicationModule] = None, exploration_scheduler_fn: Type[ LinearExplorationScheduler @@ -242,11 +242,12 @@ def trainer( system_networks = architecture.create_system() # # Augment network architecture by adding mixing layer network. - # system_networks = self._mixer( - # environment_spec=self._environment_spec, - # agent_networks=system_networks, - # num_hypernet_layers=1, - # ).create_system() + system_networks = self._mixer( + architecture=architecture, + environment_spec=self._environment_spec, + agent_networks=system_networks, + num_hypernet_layers=1, + ).create_system() # create logger trainer_logger_config = {} From a9d3e0c6c098c0fc4aa9b0b2429a501a4262dc29 Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Thu, 3 Jun 2021 15:16:09 +0200 Subject: [PATCH 141/188] fix minor bug in qmix trainer --- .../debugging_envs/run_feedforward_vdn.py | 4 ++-- mava/systems/tf/qmix/training.py | 22 +------------------ 2 files changed, 3 insertions(+), 23 deletions(-) diff --git a/examples/debugging_envs/run_feedforward_vdn.py b/examples/debugging_envs/run_feedforward_vdn.py index a420de188..fae4ea2a4 100644 --- a/examples/debugging_envs/run_feedforward_vdn.py +++ b/examples/debugging_envs/run_feedforward_vdn.py @@ -149,9 +149,9 @@ def main(_: Any) -> None: num_executors=2, exploration_scheduler_fn=LinearExplorationScheduler, epsilon_min=0.05, - epsilon_decay=5e-4, - optimizer=snt.optimizers.Adam(learning_rate=1e-4), + epsilon_decay=3e-4, max_replay_size=1000000, + optimizer=snt.optimizers.RMSProp(learning_rate=1e-4), checkpoint_subpath=checkpoint_dir, ).build() diff --git a/mava/systems/tf/qmix/training.py b/mava/systems/tf/qmix/training.py index 13b10e573..3d80c5ce6 100644 --- a/mava/systems/tf/qmix/training.py +++ b/mava/systems/tf/qmix/training.py @@ -91,26 +91,7 @@ def __init__( ) # Checkpoint the mixing networks - # if checkpoint: - # TODO Checkpointing of mixing networks not playing nicely... - # self._system_checkpointer["mixing_network"] = tf2_savers.Checkpointer( - # directory=checkpoint_subpath, - # time_delta_minutes=15, - # objects_to_save={ - # "mixing_network": self._mixing_network, - # }, - # enable_checkpointing=checkpoint, - # ) - # self._system_checkpointer[ - # "target_mixing_network" - # ] = tf2_savers.Checkpointer( - # directory=checkpoint_subpath, - # time_delta_minutes=15, - # objects_to_save={ - # "target_mixing_network": self._target_mixing_network, - # }, - # enable_checkpointing=checkpoint, - # ) + # TODO add checkpointing for mixing networks def _update_target_networks(self) -> None: # Update target networks (incl. mixing networks). @@ -275,7 +256,6 @@ def _backward(self) -> None: gradients = self.tape.gradient(self.loss, variables) gradients = tf.clip_by_global_norm(gradients, self._max_gradient_norm)[0] self._optimizer.apply(gradients, variables) - variables += self._mixing_network.trainable_variable train_utils.safe_del(self, "tape") From ee5236cb63d1ace6d00de5c2c05a02dd875421c7 Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Thu, 3 Jun 2021 15:18:25 +0200 Subject: [PATCH 142/188] fix mypy issues. --- mava/systems/tf/qmix/builder.py | 5 +---- mava/systems/tf/qmix/system.py | 2 +- 2 files changed, 2 insertions(+), 5 deletions(-) diff --git a/mava/systems/tf/qmix/builder.py b/mava/systems/tf/qmix/builder.py index 0f6cbe20c..9104df2d0 100644 --- a/mava/systems/tf/qmix/builder.py +++ b/mava/systems/tf/qmix/builder.py @@ -13,14 +13,11 @@ # See the License for the specific language governing permissions and # limitations under the License. -import copy import dataclasses from typing import Any, Dict, Iterator, Optional, Type import reverb import sonnet as snt -import tensorflow as tf -from acme.tf import utils as tf_utils from acme.utils import counting from mava import core, types @@ -28,8 +25,8 @@ from mava.components.tf.modules.exploration.exploration_scheduling import ( LinearExplorationScheduler, ) -from mava.components.tf.modules.stabilising import FingerPrintStabalisation from mava.components.tf.modules.mixing import MonotonicMixing +from mava.components.tf.modules.stabilising import FingerPrintStabalisation from mava.systems.tf.madqn.builder import MADQNBuilder, MADQNConfig from mava.systems.tf.qmix import execution, training from mava.wrappers import DetailedTrainerStatisticsWithEpsilon diff --git a/mava/systems/tf/qmix/system.py b/mava/systems/tf/qmix/system.py index 80bbf29ef..0027a9472 100644 --- a/mava/systems/tf/qmix/system.py +++ b/mava/systems/tf/qmix/system.py @@ -29,8 +29,8 @@ from mava.components.tf.architectures import DecentralisedValueActor from mava.components.tf.modules.communication import BaseCommunicationModule from mava.components.tf.modules.exploration import LinearExplorationScheduler -from mava.components.tf.modules.stabilising import FingerPrintStabalisation from mava.components.tf.modules.mixing import MonotonicMixing +from mava.components.tf.modules.stabilising import FingerPrintStabalisation from mava.environment_loop import ParallelEnvironmentLoop from mava.systems.tf import executors from mava.systems.tf.madqn.system import MADQN From a3fec9ff86726528c6e65effe615eef872102641 Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Thu, 3 Jun 2021 15:40:01 +0200 Subject: [PATCH 143/188] add vdn to discrete 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z7-YJv)aLKgPuyg+i#(IWUWe}GKK%jdx1AtXcR2x?h?Dqm`X6i8AX<3VCy_7gBh6Zi zh(yC4R7;=Kfm2C8EeY=c8P^C;7?+`9#jcRC4aLRU>%>PRts&AD68~d&g}^$tBp~G( zqa$Q2wE#I5vb@5n<7v4sUXQJE8mYac3l*aBH^BHD6IA41=zJD+OKg*9fI@j$0AyOS z{#F|6fQM-uvaqOsV82big_%uv|FQ0{izGgLUQrSCL|$s)X5ci4&OW%6&iA!>2s60~umqR&EP`(cHR<0vSL@daudo6=+1)yZUhl zInKJc`~dZc>-&c7p;Uv{c$kQArn$o|~_UlU)Wh&Ze%d0VEb zc+w;+ncRGJJL`}QqoF0&G+0?Tc7})}FVMic=~-o^%tWPOh$CcWoUk~(u=+q8E#PHQ z!&ZHl?}uc{U|K%;U2Uz+vAZsB-dFi*>rJAzsNkT9AflZ6(7KU}lw;!L=p2SS@1Vpj z4e4Np9PX+;fpbA|a{au^oqF4>HHd?)Omvqr&Dvs&t88eLXwRW_ygH689yKCRd)pXH z+P$a+$s9Fm`3jM-!PyLqyW#a_;}*^C-!BY8O~rTUjzGrMDSac_lKQvy?r+1m9ANl+mjeXwHteM38?cHGmx^ra4@IK+PI7+vbySt|oE=?y5M zn)#d(nb@mC+a;NYSvW}NQ_)lI*5PuW_dk0`X5{zMNzespK)Dr(`Qyug_K=;~kj zea@uM1hU&$*T*lqK4WQ$%nz3Sd|?dq`LN@0WxFNK{lwbuKz=AX_V4n-xQ} z49B6B_vNiPH3k=)+}gQ?=(WQ2r07d6P`o3UP$pq6&2j7sFha+TYdSfBeLmXTD u3%kCp!T?X9+b;i(fB&bP_WxkeO==m(Yu!^EGUhAzGj{X@n*?jO4gUpfLxJxA From e4b4ff0a1fa77ba5e5b44147ff248706784526f6 Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Thu, 3 Jun 2021 15:41:07 +0200 Subject: [PATCH 144/188] Update VDN status to yellow --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 1dbd236da..e152deac6 100644 --- a/README.md +++ b/README.md @@ -65,7 +65,7 @@ Mava includes several system implementations. Below we list these together with * 🟩 - Multi-Agent Deep Deterministic Policy Gradient (MADDPG). * 🟩 - Multi-Agent Distributed Distributional Deep Deterministic Policy Gradient (MAD4PG). * 🟨 - Multi-Agent Proximal Policy Optimisation (MAPPO). -* 🟥 - Value Decomposition Networks (VDN). +* 🟨 - Value Decomposition Networks (VDN). * 🟥 - Monotonic value function factorisation (QMIX). As we develop Mava further, we aim to have all systems well tested on a wide variety of environments. From 6820f473749e89d006fe609537a7d9ee67f500a5 Mon Sep 17 00:00:00 2001 From: Arnu Pretorius Date: Fri, 4 Jun 2021 09:35:33 +0200 Subject: [PATCH 145/188] Update year and add placeholder --- LICENSE | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/LICENSE b/LICENSE index 261eeb9e9..2c758549a 100644 --- a/LICENSE +++ b/LICENSE @@ -186,7 +186,7 @@ same "printed page" as the copyright notice for easier identification within third-party archives. - Copyright [yyyy] [name of copyright owner] + Copyright 2021 [...placeholder...] Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. From 7ab51d0b79a4bfd4f8f2a0d1ae06083cfe56acd5 Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Fri, 4 Jun 2021 10:44:39 +0200 Subject: [PATCH 146/188] clean up make file --- Makefile | 41 ++--------------------------------------- 1 file changed, 2 insertions(+), 39 deletions(-) diff --git a/Makefile b/Makefile index af24c30c6..995f33261 100755 --- a/Makefile +++ b/Makefile @@ -16,50 +16,13 @@ DOCKER_RUN=docker run $(RUN_FLAGS) $(IMAGE) DOCKER_RUN_TENSORBOARD=docker run $(RUN_FLAGS_TENSORBOARD) $(IMAGE) # Set example to run when using `make run` -MADDPG=examples/debugging_envs/run_decentralised_feedforward_maddpg_continous.py -MAPPO=examples/debugging_envs/run_decentralised_feedforward_mappo.py -MADQN=examples/debugging_envs/run_feedforward_madqn.py -QMIX=examples/debugging_envs/run_feedforward_qmix.py -QMIXv2=examples/debugging_envs/run_feedforward_qmix_V2.py -VDN=examples/debugging_envs/run_feedforward_vdn.py - -MADDPG-PZ=examples/petting_zoo/run_decentralised_feedforward_maddpg_continous.py -QMIX-PZ=examples/petting_zoo/run_feedforward_qmix.py -VDN-PZ==examples/petting_zoo/run_feedforward_vdn.py - -MADDPG_RECORD=examples/petting_zoo/run_feedforward_maddpg_record_video.py +# Default example +EXAMPLE=examples/debugging_envs/run_decentralised_feedforward_maddpg_continous.py # make file commands run: $(DOCKER_RUN) python $(EXAMPLE) --base_dir /home/app/mava/logs/ -run-maddpg: - $(DOCKER_RUN) python $(MADDPG) --base_dir /home/app/mava/logs/ - -run-madqn: - $(DOCKER_RUN) python $(MADQN) --base_dir /home/app/mava/logs/ - -run-mappo: - $(DOCKER_RUN) python $(MAPPO) --base_dir /home/app/mava/logs/ - -run-qmix: - $(DOCKER_RUN) python $(QMIX) --base_dir /home/app/mava/logs/ - -run-qmix-v2: - $(DOCKER_RUN) python $(QMIXv2) --base_dir /home/app/mava/logs/ - -run-vdn: - $(DOCKER_RUN) python $(VDN) --base_dir /home/app/mava/logs/ - -run-maddpg-pz: - $(DOCKER_RUN) python $(MADDPG-PZ) --base_dir /home/app/mava/logs/ - -run-qmix-pz: - $(DOCKER_RUN) python $(QMIX-PZ) --base_dir /home/app/mava/logs/ - -run-vdn-pz: - $(DOCKER_RUN) python $(VDN-PZ) --base_dir /home/app/mava/logs/ - bash: $(DOCKER_RUN) bash From a17c42d40c30983320a65644dc2873babb7f9776 Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Fri, 4 Jun 2021 10:56:27 +0200 Subject: [PATCH 147/188] Refactor qmix to use generic hypernetwork again --- README.md | 46 ++++++------- examples/README.md | 2 +- .../components/tf/modules/mixing/monotonic.py | 4 +- mava/components/tf/networks/monotonic.py | 66 +++++++------------ 4 files changed, 48 insertions(+), 70 deletions(-) diff --git a/README.md b/README.md index e152deac6..4a38d6e8d 100644 --- a/README.md +++ b/README.md @@ -26,7 +26,7 @@ baselines, while still providing enough flexibility to do novel research. ## Overview ### Systems and the Executor-Trainer paradigm -At the core of the Mava framework is the concept of a `system`. A system refers to a full multi-agent reinforcement learning algorithm consisting of the following specific components: an `Executor`, a `Trainer` and a `Dataset`. +At the core of the Mava framework is the concept of a `system`. A system refers to a full multi-agent reinforcement learning algorithm consisting of the following specific components: an `Executor`, a `Trainer` and a `Dataset`.

    @@ -44,7 +44,7 @@ Mava shares much of the design philosophy of Acme for the same reason: to allow ## Supported environments A given multi-agent system interacts with its environment via an `EnvironmentLoop`. This loop takes as input a `system` instance and a multi-agent `environment` -instance which implements the [DeepMind Environment API][dm_env]. Mava currently supports multi-agent environment loops and environment wrappers for the following environments and environment suites: +instance which implements the [DeepMind Environment API][dm_env]. Mava currently supports multi-agent environment loops and environment wrappers for the following environments and environment suites:

    @@ -53,13 +53,13 @@ instance which implements the [DeepMind Environment API][dm_env]. Mava currently * [PettingZoo][pettingzoo] * [SMAC][smac] * [Flatland][flatland] -* [2D RoboCup][robocup] +* [2D RoboCup][robocup] The animation on the right shows MAD4PG solving the Multi-Walker environment from PettingZoo. ## System implementations -Mava includes several system implementations. Below we list these together with an indication of the maturity of the system using the following keys: 🟩 -- Tested and working well, 🟨 -- Running and training on simple environments, but not extensively tested and 🟥 -- Implemented but untested and yet to show clear signs of stable training. +Mava includes several system implementations. Below we list these together with an indication of the maturity of the system using the following keys: 🟩 -- Tested and working well, 🟨 -- Running and training on simple environments, but not extensively tested and 🟥 -- Implemented but untested and yet to show clear signs of stable training. * 🟩 - Multi-Agent Deep Q-Networks (MADQN). * 🟩 - Multi-Agent Deep Deterministic Policy Gradient (MADDPG). @@ -85,22 +85,22 @@ import launchpad # Distributed program program = madqn.MADQN( - environment_factory=helpers.environment_factory, - network_factory=helpers.network_factory, - architecture=DecentralisedPolicyActor, - num_executors=2, - ).build() + environment_factory=helpers.environment_factory, + network_factory=helpers.network_factory, + architecture=DecentralisedPolicyActor, + num_executors=2, +).build() # Launch launchpad.launch( - program, - launchpad.LaunchType.LOCAL_MULTI_PROCESSING, - ) + program, + launchpad.LaunchType.LOCAL_MULTI_PROCESSING, +) ``` The first two arguments to the program are environment and network factory functions. -These helper functions are responsible for creating the networks for the system, initialising their parameters on the different compute nodes and providing a copy of the environment for each executor. The next argument `num_executors` sets the number of executor processes to be run. -After building the program we feed it to Launchpad's `launch` function and specify the launch type to perform local multi-processing, i.e. running the distributed program on a single machine. Scaling up or down is simply a matter of adjusting the number of executor processes. +These helper functions are responsible for creating the networks for the system, initialising their parameters on the different compute nodes and providing a copy of the environment for each executor. The next argument `num_executors` sets the number of executor processes to be run. +After building the program we feed it to Launchpad's `launch` function and specify the launch type to perform local multi-processing, i.e. running the distributed program on a single machine. Scaling up or down is simply a matter of adjusting the number of executor processes. For a deeper dive, take a look at the detailed working code examples found in our [examples] subdirectory which show how to instantiate a few MARL systems and environments. @@ -114,7 +114,7 @@ from mava.components.tf.modules import communication ... -# Wrap architecture in communication module +# Wrap architecture in communication module communication.BroadcastedCommunication( architecture=architecture, shared=True, @@ -165,7 +165,7 @@ We have tested `mava` on Python 3.6, 3.7 and 3.8. pip install --upgrade pip setuptools ``` -2. To install the core libraries with dependencies from source (from root directory): +2. To install the core libraries with dependencies from source (from root directory): ```bash pip install -e ".[tf,envs,reverb,launchpad]" @@ -174,16 +174,16 @@ We have tested `mava` on Python 3.6, 3.7 and 3.8. Note that the dependencies may be installed selectively by adding and removing their identifiers. Additional optional dependencies include `record_episode` for installing packages required to make video recordings of evaluation runs and `testing_formatting` for running tests and code formatting checks. Extra information on optional installs are given below. 3. **NB**: Flatland and SMAC installations have to be done separately. Flatland can be installed using: - + ```bash pip install .[flatland] ``` - + For StarCraft II installation, this must be installed separately according to your operating system. To install the StarCraft II ML environment and associated packages, please follow the instructions on [PySC2](https://github.com/deepmind/pysc2) to install the StarCraft II game files. Please ensure you have the required game maps (for both PySC2 and SMAC) extracted in the StarCraft II maps directory. Once this is done you can install the packages for the single agent case (PySC2) and the multi-agent case (SMAC). - + ```bash pip install pysc2 pip install git+https://github.com/oxwhirl/smac.git @@ -196,9 +196,9 @@ We also have a list of [optional installs](OPTIONAL_INSTALL.md) for extra functi
    - + To test and debug new system implementations, we use a simplified version of the spread environment from the [MPE][mpe] suite. -Debugging in MARL can be very difficult and time consuming, therefore it is important to use a small environment for debugging that is simple and fast but at the same time still able to clearly show whether a system is able to learn. An illustration of the debugging environment is shown on the right. Agents start at random locations and are assigned specific landmarks which they attempt to reach in as few steps as possible. Rewards are given to each agent independently as a function of their distance to the landmark. The reward is normalised to be between 0 and 1, where 1 is given when the agent is directly on top of the landmark. The further an agent is away from its landmark the more the reward value converges to 0. Collisions between agents result in a reward of -1 received by the colliding agents. To test both discrete and continuous control systems we feature two versions of the environment. In the discrete version the action space for each agent consists of the following five actions: `left`, `right`, `up`, `down`, `stand-still`. In the continuous case, the action space consists of real values bounded between -1 and 1 for the `acceleration` of the agent in the `x` and `y` direction. Several examples of running systems on the debugging environment can be found [here][debug]. Below we show the results from some of our systems trained on the debugging environment. +Debugging in MARL can be very difficult and time consuming, therefore it is important to use a small environment for debugging that is simple and fast but at the same time still able to clearly show whether a system is able to learn. An illustration of the debugging environment is shown on the right. Agents start at random locations and are assigned specific landmarks which they attempt to reach in as few steps as possible. Rewards are given to each agent independently as a function of their distance to the landmark. The reward is normalised to be between 0 and 1, where 1 is given when the agent is directly on top of the landmark. The further an agent is away from its landmark the more the reward value converges to 0. Collisions between agents result in a reward of -1 received by the colliding agents. To test both discrete and continuous control systems we feature two versions of the environment. In the discrete version the action space for each agent consists of the following five actions: `left`, `right`, `up`, `down`, `stand-still`. In the continuous case, the action space consists of real values bounded between -1 and 1 for the `acceleration` of the agent in the `x` and `y` direction. Several examples of running systems on the debugging environment can be found [here][debug]. Below we show the results from some of our systems trained on the debugging environment.

    @@ -209,8 +209,8 @@ Debugging in MARL can be very difficult and time consuming, therefore it is impo We have big ambitions for Mava! 🚀 But there is still much work that needs to be done. We have a clear roadmap for expanding our system implementations and associated modules, improving testing and robustness and providing support for across-machine training. Furthermore, in the near future, we also plan to implement the following features (feel free to vote for any one of them by clicking on the name!). -[![](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Sequential%20environment%20support%20including%20OpenSpiel)](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Sequential%20environment%20support%20including%20OpenSpiel/vote)
    -[![](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Population%20based%20training)](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Population%20based%20training/vote)
    +[![](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Sequential%20environment%20support%20including%20OpenSpiel)](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Sequential%20environment%20support%20including%20OpenSpiel/vote)
    +[![](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Population%20based%20training)](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Population%20based%20training/vote)
    [![](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Dynamic%20networked%20architectures)](https://api.gh-polls.com/poll/01F75ZJZXE8C5JM7MQWEX9PRXQ/Dynamic%20networked%20architectures/vote) In the slightly more longer term, the Mava team plans to release benchmarking results for several different systems and environments and contribute a MARL specific behavioural environment suite specifically engineered to study aspects of MARL such as cooperation and coordination. diff --git a/examples/README.md b/examples/README.md index 2eca91531..d675910cb 100644 --- a/examples/README.md +++ b/examples/README.md @@ -78,7 +78,7 @@ We also include a number of systems running on discrete action space environment an MA-DQN system running on the StarCraft 3m environment. - [MA-DQN (recurrent)](smac/run_recurrent_madqn.py): an MA-DQN system running on the StarCraft 3m environment. - + [debug]: ../mava/utils/debugging [pettingzoo]: https://github.com/PettingZoo-Team/PettingZoo [flatland]: https://gitlab.aicrowd.com/flatland/flatland diff --git a/mava/components/tf/modules/mixing/monotonic.py b/mava/components/tf/modules/mixing/monotonic.py index a31251c86..245bc4c49 100644 --- a/mava/components/tf/modules/mixing/monotonic.py +++ b/mava/components/tf/modules/mixing/monotonic.py @@ -60,8 +60,6 @@ def __init__( if agent_networks is None: agent_networks = self._architecture.create_actor_variables() self._agent_networks = agent_networks - # self._agent_networks["values"] = agent_networks["values"] - # self._agent_networks["target_values"] = agent_networks["target_values"] def _create_mixing_layer(self) -> snt.Module: """Modify and return system architecture given mixing structure.""" @@ -73,6 +71,8 @@ def _create_mixing_layer(self) -> snt.Module: # Implement method from base class self._mixed_network = MonotonicMixingNetwork( + self._architecture, + self._agent_networks, self._n_agents, self._qmix_hidden_dim, num_hypernet_layers=self._num_hypernet_layers, diff --git a/mava/components/tf/networks/monotonic.py b/mava/components/tf/networks/monotonic.py index 089eca67e..dff1010e6 100644 --- a/mava/components/tf/networks/monotonic.py +++ b/mava/components/tf/networks/monotonic.py @@ -18,12 +18,13 @@ """Mixing for multi-agent RL systems""" -# import launchpad as lp +from typing import Dict + import sonnet as snt import tensorflow as tf -# from mava.components.tf.architectures.base import BaseArchitecture -# from mava.components.tf.networks.hypernetwork import HyperNetwork +from mava.components.tf.architectures.base import BaseArchitecture +from mava.components.tf.networks.hypernetwork import HyperNetwork class MonotonicMixingNetwork(snt.Module): @@ -34,6 +35,8 @@ class MonotonicMixingNetwork(snt.Module): def __init__( self, + architecture: BaseArchitecture, + agent_networks: Dict[str, snt.Module], n_agents: int, qmix_hidden_dim: int = 64, num_hypernet_layers: int = 2, @@ -49,24 +52,20 @@ def __init__( layer. Relevant for num_hypernet_layers > 1. """ super(MonotonicMixingNetwork, self).__init__() + self._architecture = architecture + self._agent_networks = agent_networks self._n_agents = n_agents self._qmix_hidden_dim = qmix_hidden_dim self._num_hypernet_layers = num_hypernet_layers self._hypernet_hidden_dim = hypernet_hidden_dim # Create hypernetwork - # self._hypernetworks = HyperNetwork( - # self._agent_networks, - # self._qmix_hidden_dim, - # self._num_hypernet_layers, - # self._hypernet_hidden_dim, - # ) - self.hyper_w1 = snt.nets.MLP( - output_sizes=[self._qmix_hidden_dim * self._n_agents] + self._hypernetworks = HyperNetwork( + self._agent_networks, + self._qmix_hidden_dim, + self._num_hypernet_layers, + self._hypernet_hidden_dim, ) - self.hyper_w2 = snt.nets.MLP(output_sizes=[self._qmix_hidden_dim]) - self.hyper_b1 = snt.nets.MLP(output_sizes=[self._qmix_hidden_dim]) - self.hyper_b2 = snt.nets.MLP(output_sizes=[self._qmix_hidden_dim, 1]) def __call__( self, @@ -75,36 +74,15 @@ def __call__( ) -> tf.Tensor: """Monotonic mixing logic.""" - # Expand dimensions to [B, 1, n_agents] = [B,1,2] for matmul - q_values = tf.expand_dims(q_values, axis=1) - - # --- HYPERNET ---- - w1 = tf.abs( - self.hyper_w1(states) - ) # [B, qmix_hidden_dim] = [B, qmix_hidden_dim] - w1 = tf.reshape( - w1, - (-1, self._n_agents, self._qmix_hidden_dim), - ) # [B, n_agents, qmix_hidden_dim] - - b1 = self.hyper_b1(states) # [B, qmix_hidden_dim] = [B, qmix_hidden_dim] - b1 = tf.reshape(b1, [-1, 1, self._qmix_hidden_dim]) # [B, 1, qmix_hidden_dim] - - w2 = tf.abs(self.hyper_w2(states)) - w2 = tf.reshape( - w2, shape=(-1, self._qmix_hidden_dim, 1) - ) # [B, qmix_hidden_dim, 1] - - b2 = self.hyper_b2(states) # [B, 1] - b2 = tf.reshape(b2, shape=(-1, 1, 1)) # [B, 1, 1] - # ----------------- - # self._hyperparams = self._hypernetworks(states) - - # For convenience - # w1 = self._hyperparams["w1"] # [B, n_agents, qmix_hidden_dim] - # b1 = self._hyperparams["b1"] # [B, 1, qmix_hidden_dim] - # w2 = self._hyperparams["w2"] # [B, qmix_hidden_dim, 1] - # b2 = self._hyperparams["b2"] # [B, 1, 1] + # Create hypernetwork + self._hyperparams = self._hypernetworks(states) + + # Extract hypernetwork layers + # TODO: make more general -> this assumes two layer hypernetwork + w1 = self._hyperparams["w1"] # [B, n_agents, qmix_hidden_dim] + b1 = self._hyperparams["b1"] # [B, 1, qmix_hidden_dim] + w2 = self._hyperparams["w2"] # [B, qmix_hidden_dim, 1] + b2 = self._hyperparams["b2"] # [B, 1, 1] # ELU -> Exp. linear unit hidden = tf.nn.elu(tf.matmul(q_values, w1) + b1) # [B, 1, qmix_hidden_dim] From 1a63a8237d614049cc09d12d4cd32645ff394687 Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Fri, 4 Jun 2021 11:17:46 +0200 Subject: [PATCH 148/188] Remove communication module from qmix --- mava/systems/tf/qmix/system.py | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/mava/systems/tf/qmix/system.py b/mava/systems/tf/qmix/system.py index 0027a9472..d09feda73 100644 --- a/mava/systems/tf/qmix/system.py +++ b/mava/systems/tf/qmix/system.py @@ -224,28 +224,28 @@ def trainer( shared_weights=self._shared_weights, ) + # Fingerprint module if self._builder._replay_stabiliser_fn is not None: architecture = self._builder._replay_stabiliser_fn( # type: ignore architecture ) - communication_module = None + # Communication module + # NOTE: this is currently not expected to work with qmix + # since we do not have a recurrent version. if self._communication_module_fn is not None: - communication_module = self._communication_module_fn( - architecture=architecture, - shared=True, - channel_size=1, - channel_noise=0, + raise Exception( + "QMIX currently does not support recurrence and therefore cannot use a communication module." ) - system_networks = communication_module.create_system() - else: - system_networks = architecture.create_system() - # # Augment network architecture by adding mixing layer network. + # extract agent networks + agent_networks = architecture.create_actor_variables() + + # Mixing module system_networks = self._mixer( architecture=architecture, environment_spec=self._environment_spec, - agent_networks=system_networks, + agent_networks=agent_networks, num_hypernet_layers=1, ).create_system() @@ -264,7 +264,7 @@ def trainer( networks=system_networks, dataset=dataset, counter=counter, - communication_module=communication_module, + communication_module=None, logger=trainer_logger, ) From 3634524878bd1528b135dc7d402c79b4f3581348 Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Fri, 4 Jun 2021 11:20:04 +0200 Subject: [PATCH 149/188] minor mypy changes --- mava/systems/tf/qmix/system.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/mava/systems/tf/qmix/system.py b/mava/systems/tf/qmix/system.py index d09feda73..8ade6e0af 100644 --- a/mava/systems/tf/qmix/system.py +++ b/mava/systems/tf/qmix/system.py @@ -235,10 +235,11 @@ def trainer( # since we do not have a recurrent version. if self._communication_module_fn is not None: raise Exception( - "QMIX currently does not support recurrence and therefore cannot use a communication module." + "QMIX currently does not support recurrence and \ + therefore cannot use a communication module." ) - # extract agent networks + # Extract agent networks agent_networks = architecture.create_actor_variables() # Mixing module @@ -249,7 +250,7 @@ def trainer( num_hypernet_layers=1, ).create_system() - # create logger + # Create logger trainer_logger_config = {} if self._logger_config and "trainer" in self._logger_config: trainer_logger_config = self._logger_config["trainer"] From f4b6ceabdbc8c4992ffd666eb3dd7ab637c6a35c Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Fri, 4 Jun 2021 11:45:14 +0200 Subject: [PATCH 150/188] remove commented line in make file --- Makefile | 1 - 1 file changed, 1 deletion(-) diff --git a/Makefile b/Makefile index 42c8e6f0a..fea702376 100755 --- a/Makefile +++ b/Makefile @@ -37,7 +37,6 @@ build: build_robocup: docker build --tag $(IMAGE) -f ./Dockerfile.robocup . - # sudo rm -r ./*.rcl ./*.rcg ./rcssserver-16.0.0* ./rcssserver-rcssserver-16.0.0 ./rcssmonitor* push: docker login From 704ba3620e9cef8f6c0776044c9762e9548225ad Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Fri, 4 Jun 2021 11:45:36 +0200 Subject: [PATCH 151/188] rename example --- .../{run_robocup.py => run_state_based_recurrent_mad4pg.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename examples/robocup/{run_robocup.py => run_state_based_recurrent_mad4pg.py} (100%) diff --git a/examples/robocup/run_robocup.py b/examples/robocup/run_state_based_recurrent_mad4pg.py similarity index 100% rename from examples/robocup/run_robocup.py rename to examples/robocup/run_state_based_recurrent_mad4pg.py From 78fc3f4e78189845f28a0332b269755ec74fbf45 Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Fri, 4 Jun 2021 11:49:18 +0200 Subject: [PATCH 152/188] clean agents file --- .../RoboCup_env/robocup_utils/agent.py | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/agent.py b/mava/utils/environments/RoboCup_env/robocup_utils/agent.py index d7fc879fd..184b92cb5 100755 --- a/mava/utils/environments/RoboCup_env/robocup_utils/agent.py +++ b/mava/utils/environments/RoboCup_env/robocup_utils/agent.py @@ -1,5 +1,18 @@ -# type: ignore # !/usr/bin/env python3 +# Copyright 2021 [...placeholder...]. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import threading import time From d5183646e2f396d03abd9e83f36ea459cddcaa28 Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Fri, 4 Jun 2021 11:51:02 +0200 Subject: [PATCH 153/188] cleaned fixed_agent file --- .../RoboCup_env/robocup_agents/fixed_agent.py | 36 +++++++++---------- 1 file changed, 16 insertions(+), 20 deletions(-) diff --git a/mava/utils/environments/RoboCup_env/robocup_agents/fixed_agent.py b/mava/utils/environments/RoboCup_env/robocup_agents/fixed_agent.py index 8165718b3..989d9b382 100755 --- a/mava/utils/environments/RoboCup_env/robocup_agents/fixed_agent.py +++ b/mava/utils/environments/RoboCup_env/robocup_agents/fixed_agent.py @@ -1,4 +1,18 @@ -# type: ignore +# python3 +# Copyright 2021 [...placeholder...]. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import numpy as np from mava.utils.environments.RoboCup_env.robocup_utils.util_functions import ( @@ -16,10 +30,6 @@ def __init__(self, agent_type, num_players): self.num_players = num_players assert self.scaling == 200.0 self.kicked = False - # self.obs_norm = 200.0 - # self.action_scaling = 180 - # self.obs_size = 17 - # self._action_size = 7 def reset_brain(self): self.kicked = False @@ -80,7 +90,6 @@ def get_action(self, observation): action = np.zeros(22) if see_ball < 0.5: # Search for the ball - # print("Searching..") action[command["do_turn"]] = 1 x, y = deg_rot_to_xy(20) action[command["turn_dir_x"]] = x @@ -89,7 +98,7 @@ def get_action(self, observation): if not self.kicked: # Kick the ball self.kicked = True - # goal_locs = np.array([np.random.randn()*20, np.random.randn()*20]) + goal_locs = np.array([52.5, 0]) x_diff, y_diff = goal_locs - p_loc @@ -109,7 +118,6 @@ def get_action(self, observation): # print("Waiting..") elif np.abs(b_deg) > 10: # Turn towards the ball - # print("Turning..") action[command["do_turn"]] = 1 x, y = deg_rot_to_xy(b_deg / 3) action[command["turn_dir_x"]] = x @@ -117,7 +125,6 @@ def get_action(self, observation): else: # Dash towards the ball action[command["do_dash"]] = 1 - # print("Dashing..") speed = 100 if b_dist < 5: @@ -132,14 +139,3 @@ def get_action(self, observation): self.kicked = False return action - - -# class RotateBot(object): -# def __init__(self, num_actions=9): -# pass -# -# def reset_brain(self): -# pass -# -# def get_action(self, observations, last_action, reward=None): -# return "(turn 30)" From 5d700bf40389d2c236aec98b6c72add91e7d1e73 Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Fri, 4 Jun 2021 11:51:57 +0200 Subject: [PATCH 154/188] cleaned nn_agents file --- .../RoboCup_env/robocup_agents/nn_agent.py | 23 ++++++++++++------- 1 file changed, 15 insertions(+), 8 deletions(-) diff --git a/mava/utils/environments/RoboCup_env/robocup_agents/nn_agent.py b/mava/utils/environments/RoboCup_env/robocup_agents/nn_agent.py index 4a736b7df..b673174f3 100755 --- a/mava/utils/environments/RoboCup_env/robocup_agents/nn_agent.py +++ b/mava/utils/environments/RoboCup_env/robocup_agents/nn_agent.py @@ -1,4 +1,18 @@ -# type: ignore +# python3 +# Copyright 2021 [...placeholder...]. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import os from typing import Any, Dict, Mapping, MutableSequence, Sequence, Union @@ -19,7 +33,6 @@ from mava.utils.environments.RoboCup_env.robocup_utils.util_functions import SpecWrapper tfd = tfp.distributions -# log = logging.getLogger(__name__) def set_gpu_affinity(gpus: Any) -> Any: @@ -51,11 +64,6 @@ def set_gpu_affinity(gpus: Any) -> Any: tf.config.experimental.set_memory_growth(gpu, True) except RuntimeError as e: print(e) - - # log.info( - # f"Devices: {len(devices)} physical and " - # f"{len(tf.config.list_logical_devices('GPU'))} logical devices" - # ) return gpus @@ -243,7 +251,6 @@ def __init__( # Create the policy_networks policy_networks = architecture.create_actor_variables() before_sum = policy_networks["policies"][agent_type].variables[1].numpy().sum() - # print("Weights before: ", before_sum) objects_to_save = { "policy": policy_networks["policies"][agent_type], "observation": policy_networks["observations"][agent_type], From 67c55970656bcc4c8b01d69661c752fb3da12144 Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Fri, 4 Jun 2021 11:55:14 +0200 Subject: [PATCH 155/188] update custom executor --- .../robocup_agents/custom_executor.py | 16 +++++++++++++++- 1 file changed, 15 insertions(+), 1 deletion(-) diff --git a/mava/utils/environments/RoboCup_env/robocup_agents/custom_executor.py b/mava/utils/environments/RoboCup_env/robocup_agents/custom_executor.py index 912fbf774..b0cc5d6b4 100644 --- a/mava/utils/environments/RoboCup_env/robocup_agents/custom_executor.py +++ b/mava/utils/environments/RoboCup_env/robocup_agents/custom_executor.py @@ -1,4 +1,18 @@ -# type: ignore +# python3 +# Copyright 2021 [...placeholder...]. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + from typing import Dict, Optional import dm_env From 478f39def16c498630d43d11caae8d3af61cde88 Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Fri, 4 Jun 2021 11:58:28 +0200 Subject: [PATCH 156/188] update copyright stubs --- .../RoboCup_env/robocup_utils/__init__.py | 14 +++++ .../RoboCup_env/robocup_utils/agent.py | 52 ------------------- .../robocup_utils/extract_policy.py | 18 +++++-- .../RoboCup_env/robocup_utils/game_object.py | 14 ++++- 4 files changed, 41 insertions(+), 57 deletions(-) diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/__init__.py b/mava/utils/environments/RoboCup_env/robocup_utils/__init__.py index e69de29bb..06240e71c 100755 --- a/mava/utils/environments/RoboCup_env/robocup_utils/__init__.py +++ b/mava/utils/environments/RoboCup_env/robocup_utils/__init__.py @@ -0,0 +1,14 @@ +# python3 +# Copyright 2021 [...placeholder...]. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. \ No newline at end of file diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/agent.py b/mava/utils/environments/RoboCup_env/robocup_utils/agent.py index 184b92cb5..81e6aeef2 100755 --- a/mava/utils/environments/RoboCup_env/robocup_utils/agent.py +++ b/mava/utils/environments/RoboCup_env/robocup_utils/agent.py @@ -33,11 +33,6 @@ wait_for_next_observations, ) -# import copy -# import os -# os.environ['PYGAME_HIDE_SUPPORT_PROMPT'] = "hide" -# import pygame - # Define some colours green = (0, 255, 50) light_blue = (0, 255, 255) @@ -135,16 +130,12 @@ def start_comm_threads(self, host, port, teamname, version): self.__message_tread.start() # wait until the socket receives a response from the server and gets its # assigned port. - # print("Agent init address: ", init_address) while self.__sock.address == init_address: time.sleep(0.0001) - # print("Agent new address: ", self.__sock.address) - # exit() # create our thinking thread. this will perform the actions necessary # to play a game of robo-soccer. self.__thinking = True - # self.__think_thread.daemon = True # set connected state. done last to prevent state inconsistency if # something goes wrong beforehand. @@ -153,7 +144,6 @@ def start_comm_threads(self, host, port, teamname, version): self.last_action = None # Move to random starting position - x = np.random.randint(-52, 53) y = np.random.randint(-34, 35) @@ -167,45 +157,6 @@ def start_comm_threads(self, host, port, teamname, version): self.__think_thread.daemon = True self.__think_thread.start() - # def disconnect(self): - # """ - # Tell the loop threads to stop and signal the server that we're - # disconnecting, then join the loop threads and destroy all our inner - # methods. - # - # Since the message loop thread can conceiveably block indefinitely while - # waiting for the server to respond, we only allow it (and the think loop - # for good measure) a short time to finish before simply giving up. - # - # Once an agent has been disconnected, it is 'dead' and cannot be used - # again. All of its methods get replaced by a method that raises an - # exception every time it is called. - # """ - # - # # don't do anything if not connected - # if not self.__connected: - # return - # - # # tell the loops to terminate - # self.__parsing = False - # self.__thinking = False - # - # # tell the server that we're quitting - # self.__sock.send("(bye)") - # - # # tell our threads to join, but only wait breifly for them to do so. - # # don't join them if they haven't been started (this can happen if - # # disconnect is called very quickly after connect). - # if self.__msg_thread.is_alive(): - # self.__msg_thread.join(0.01) - # - # # if self.__think_thread.is_alive(): - # # self.__think_thread.join(0.01) - # - # # reset all standard variables in this object. self.__connected gets - # # reset here, along with all other non-user defined internal variables. - # Agent.__init__(self) - def __message_loop(self): """ Handles messages received from the server. @@ -221,9 +172,6 @@ def __message_loop(self): # itself for perusal at our leisure. raw_msg = self.__sock.recv() - # if b'goal' in raw_msg: - # print("Agent message: '" , raw_msg, "'") - msg_type = self.msg_handler.handle_message(raw_msg) if msg_type is not None: # we send commands all at once every cycle, ie. whenever a diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/extract_policy.py b/mava/utils/environments/RoboCup_env/robocup_utils/extract_policy.py index 8bd16d250..66cc359d7 100644 --- a/mava/utils/environments/RoboCup_env/robocup_utils/extract_policy.py +++ b/mava/utils/environments/RoboCup_env/robocup_utils/extract_policy.py @@ -1,4 +1,17 @@ -# type: ignore +# Copyright 2021 [...placeholder...]. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import os import pickle @@ -8,7 +21,6 @@ def save_policy_and_obs(architecture, agent_type, checkpoint_subpath, pickle_save_loc): policy_networks = architecture.create_actor_variables() before_sum = policy_networks["policies"][agent_type].variables[1].numpy().sum() - # print("Weights before: ", before_sum) objects_to_save = { "policy": policy_networks["policies"][agent_type], "observation": policy_networks["observations"][agent_type], @@ -27,8 +39,6 @@ def save_policy_and_obs(architecture, agent_type, checkpoint_subpath, pickle_sav assert before_sum != after_sum # Save policy variables - print(policy_networks["policies"][agent_type]) - exit() policy = policy_networks["policies"][agent_type].set.variables policy_file = open(os.path.join(pickle_save_loc, "policy.obj"), "wb") pickle.dump(policy, policy_file) diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/game_object.py b/mava/utils/environments/RoboCup_env/robocup_utils/game_object.py index 5b6700664..246c1ebf7 100755 --- a/mava/utils/environments/RoboCup_env/robocup_utils/game_object.py +++ b/mava/utils/environments/RoboCup_env/robocup_utils/game_object.py @@ -1,4 +1,16 @@ -# type: ignore +# Copyright 2021 [...placeholder...]. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. class GameObject: """ Root class for all percievable objects in the world model. From 8735ba5515fd55ef994a904caae061ddecff4cdb Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Fri, 4 Jun 2021 11:59:57 +0200 Subject: [PATCH 157/188] clean message parser --- .../RoboCup_env/robocup_utils/handler.py | 31 +++++++------- .../robocup_utils/message_parser.py | 42 +++++++------------ 2 files changed, 29 insertions(+), 44 deletions(-) diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/handler.py b/mava/utils/environments/RoboCup_env/robocup_utils/handler.py index 052f0793f..2530faf3c 100755 --- a/mava/utils/environments/RoboCup_env/robocup_utils/handler.py +++ b/mava/utils/environments/RoboCup_env/robocup_utils/handler.py @@ -1,4 +1,17 @@ -# type: ignore +# Copyright 2021 [...placeholder...]. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import collections import queue @@ -11,12 +24,6 @@ WorldModel, ) -# import time - - -# from multiprocessing import Queue as queue - - # should we print messages received from the server? PRINT_SERVER_MESSAGES = False @@ -258,10 +265,6 @@ def _handle_see_global(self, msg): the world model. """ - # the simulation cycle of the soccer server - # print("msg: ", msg) - # sim_time = msg[1] - # store new values before changing those in the world model. all new # values replace those in the world model at the end of parsing. ball = None @@ -339,10 +342,6 @@ def _handle_hear(self, msg): sender = msg[1] # name (or direction) of who sent the message message = msg[3] # message string - # if message.startswith("goal_"): - # print("time_recvd: ", time_recvd) - # print("sender: ", sender) - # print("message: ", message) # ignore messages sent by self (NOTE: would anybody really want these?) if sender == "self": @@ -600,8 +599,6 @@ def send_commands(self): print("sent:", primary_cmd.text, "\n") self.sock.send(primary_cmd.text) - # self.__sock.send("(turn 30)\x00", append_null_terminator=False) - # time.sleep(0.01) self.sock.send("(done)") def move(self, x, y): diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/message_parser.py b/mava/utils/environments/RoboCup_env/robocup_utils/message_parser.py index c8eb24e20..262e27b4c 100755 --- a/mava/utils/environments/RoboCup_env/robocup_utils/message_parser.py +++ b/mava/utils/environments/RoboCup_env/robocup_utils/message_parser.py @@ -1,4 +1,17 @@ -# type: ignore +# Copyright 2021 [...placeholder...]. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import re # used to convert server value strings into actual python values @@ -43,7 +56,6 @@ def parse(text): # noqa: C901 # the last character seen, None to begin with prev_c = None - # raise ValueError("Message text has unmatching parenthesis: ", text) for c in text: # prevent parsing parens when inside a string (also ignores escaped # '"'s as well). doesn't add the quotes so we don't have to recognize @@ -137,28 +149,4 @@ def parse(text): # noqa: C901 # append the first '('. return result[0] else: - # print("Ignored messages because of unequal parenthesis") - return None - - -# if __name__ == "__main__": -# import sys - -# # interactive mode if any args were specified -# if len(sys.argv) > 2: -# from pprint import pprint - -# with open(sys.argv[1], "r") as f: -# for line in f: -# print("raw message:\n", line.strip()) -# print() -# print("parsed message:") -# pprint(parse(line.strip())) -# print("----") -# raw_input() -# print() -# else: -# # just parse the message file -# with open(sys.argv[1], "r") as f: -# for line in f: -# parse(line.strip()) + return None \ No newline at end of file From c6402263122de82cb4b6f49722b3142bf731ba66 Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Fri, 4 Jun 2021 12:58:56 +0200 Subject: [PATCH 158/188] clean player world model --- .../robocup_utils/player_world_model.py | 189 ++---------------- 1 file changed, 14 insertions(+), 175 deletions(-) diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/player_world_model.py b/mava/utils/environments/RoboCup_env/robocup_utils/player_world_model.py index b6ac5942c..a6591dbd0 100755 --- a/mava/utils/environments/RoboCup_env/robocup_utils/player_world_model.py +++ b/mava/utils/environments/RoboCup_env/robocup_utils/player_world_model.py @@ -1,4 +1,17 @@ -# type: ignore +# Copyright 2021 [...placeholder...]. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import math import numpy as np @@ -18,8 +31,6 @@ def rotate(origin, point, angle): The angle should be given in radians. """ - # print("origin: ", origin) - ox, oy = origin px, py = point @@ -166,176 +177,6 @@ def __init__(self, action_handler): # create a new server parameter object for holding all server params self.server_parameters = ServerParameters() - # def triangulate_direction(self, flags, flag_dict): - # """ - # Determines absolute view angle for the player given a list of visible - # flags. We find the absolute angle to each flag, then return the average - # of those angles. Returns 'None' if no angle could be determined. - # """ - # - # # average all flag angles together and save that as absolute angle - # abs_angles = [] - # for f in self.flags: - # # if the flag has useful data, consider it - # if f.distance is not None and f.flag_id in flag_dict: - # flag_point = flag_dict[f.flag_id] - # abs_dir = self.angle_between_points(self.abs_coords, flag_point) - # abs_angles.append(abs_dir) - # - # # return the average if available - # if len(abs_angles) > 0: - # return sum(abs_angles) / len(abs_angles) - # - # return None - - # def triangulate_position(self, flags, flag_dict, angle_step=36): - # """ - # Returns a best-guess position based on the triangulation via distances - # to all flags in the flag list given. 'angle_step' specifies the - # increments between angles for projecting points onto the circle - # surrounding a flag. - # """ - # - # points = [] - # for f in flags: - # # skip flags without distance information or without a specific id - # if f.distance is None or f.flag_id not in flag_dict: - # continue - # - # # generate points every 'angle_step' degrees around each flag, - # # discarding those off-field. - # for i in range(0, 360, angle_step): - # dy = f.distance * math.sin(math.radians(i)) - # dx = f.distance * math.cos(math.radians(i)) - # - # fcoords = flag_dict[f.flag_id] - # new_point = (fcoords[0] + dx, fcoords[1] + dy) - # - # # skip points with a coordinate outside the play boundaries - # if (new_point[0] > 60 or new_point[0] < -60 or - # new_point[1] < -40 or new_point[1] > 40): - # continue - # - # # add point to list of all points - # points.append(new_point) - # - # # get the dict of clusters mapped to centers - # clusters = self.cluster_points(points) - # - # # return the center that has the most points as an approximation to our - # # absolute position. - # center_with_most_points = (0, 0) - # max_points = 0 - # for c in clusters: - # if len(clusters[c]) > max_points: - # center_with_most_points = c - # max_points = len(clusters[c]) - # - # return center_with_most_points - - # def cluster_points(self, points, num_cluster_iterations=15): - # """ - # Cluster a set of points into a dict of centers mapped to point lists. - # Uses the k-means clustering algorithm with random initial centers and a - # fixed number of iterations to find clusters. - # """ - # - # # generate initial random centers, ignoring identical ones - # centers = set([]) - # for i in range(int(math.sqrt(len(points) / 2))): - # # a random coordinate somewhere within the field boundaries - # rand_center = (random.randint(-55, 55), random.randint(-35, 35)) - # centers.add(rand_center) - # - # # cluster for some iterations before the latest result - # latest = {} - # cur = {} - # for i in range(num_cluster_iterations): - # # initialze cluster lists - # for c in centers: - # cur[c] = [] - # - # # put every point into the list of its nearest cluster center - # for p in points: - # # get a list of (distance to center, center coords) tuples - # c_dists = map(lambda c: (self.euclidean_distance(c, p), c), - # centers) - # - # # find the smallest tuple's c (second item) - # nearest_center = min(c_dists)[1] - # - # # add point to this center's cluster - # cur[nearest_center].append(p) - # - # # recompute centers - # new_centers = set([]) - # for cluster in cur.values(): - # tot_x = 0 - # tot_y = 0 - # - # # remove empty clusters - # if len(cluster) == 0: - # continue - # - # # generate average center of cluster - # for p in cluster: - # tot_x += p[0] - # tot_y += p[1] - # - # # get average center and add to new centers set - # ave_center = (tot_x / len(cluster), tot_y / len(cluster)) - # new_centers.add(ave_center) - # - # # move on to next iteration - # centers = new_centers - # latest = cur - # cur = {} - # - # # return latest cluster iteration - # return latest - - # def euclidean_distance(self, point1, point2): - # """ - # Returns the Euclidean distance between two points on a plane. - # """ - # - # try: - # x1 = point1[0] - # y1 = point1[1] - # x2 = point2[0] - # y2 = point2[1] - # - # return math.sqrt((x1 - x2) ** 2 + (y1 - y2) ** 2) - # except: - # return 200 - - # def angle_between_points(self, point1, point2): - # """ - # Returns the angle from the first point to the second, assuming that - # these points exist on a plane, and that the positive x-axis is 0 degrees - # and the positive y-axis is 90 degrees. All returned angles are positive - # and relative to the positive x-axis. - # """ - # - # try: - # x1 = point1[0] - # y1 = point1[1] - # x2 = point2[0] - # y2 = point2[1] - # - # # get components of vector between the points - # dx = x2 - x1 - # dy = y2 - y1 - # - # # return the angle in degrees - # a = math.degrees(math.atan2(dy, dx)) - # if a < 0: - # a = 360 + a - # - # return a - # except: - # return 0 - def __calculate_abs_info(self): if self.flags is not None: rel_coords = [] @@ -443,8 +284,6 @@ def is_kick_off_us(self): ko_left = WorldModel.PlayModes.KICK_OFF_L ko_right = WorldModel.PlayModes.KICK_OFF_R - # print self.play_mode - # return whether we're on the side that's kicking off return ( self.side == WorldModel.SIDE_L From a3de0dc6c4d442a955b8a6d1abf700077d1ea4b3 Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Fri, 4 Jun 2021 12:59:58 +0200 Subject: [PATCH 159/188] clean trainer world model --- .../RoboCup_env/robocup_utils/sock.py | 15 ++++++++++++++- .../RoboCup_env/robocup_utils/sp_exceptions.py | 15 +++++++++++++++ 2 files changed, 29 insertions(+), 1 deletion(-) diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/sock.py b/mava/utils/environments/RoboCup_env/robocup_utils/sock.py index 7a6f23c05..0e09b9a21 100755 --- a/mava/utils/environments/RoboCup_env/robocup_utils/sock.py +++ b/mava/utils/environments/RoboCup_env/robocup_utils/sock.py @@ -1,4 +1,17 @@ -# type: ignore +# Copyright 2021 [...placeholder...]. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import socket diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/sp_exceptions.py b/mava/utils/environments/RoboCup_env/robocup_utils/sp_exceptions.py index 5d01b8eeb..516395d0e 100755 --- a/mava/utils/environments/RoboCup_env/robocup_utils/sp_exceptions.py +++ b/mava/utils/environments/RoboCup_env/robocup_utils/sp_exceptions.py @@ -1,3 +1,18 @@ +# Copyright 2021 [...placeholder...]. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + class SoccerServerError(Exception): """ Represents an error message returned by the soccer server. From 2c6b506a2fb2a10a6b742be5ce6c1c538707fb87 Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Fri, 4 Jun 2021 13:00:03 +0200 Subject: [PATCH 160/188] clean trainer world model --- .../robocup_utils/trainer_world_model.py | 45 ++++++------------- 1 file changed, 13 insertions(+), 32 deletions(-) diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/trainer_world_model.py b/mava/utils/environments/RoboCup_env/robocup_utils/trainer_world_model.py index a4e3f6ad2..8b1e0e4c2 100755 --- a/mava/utils/environments/RoboCup_env/robocup_utils/trainer_world_model.py +++ b/mava/utils/environments/RoboCup_env/robocup_utils/trainer_world_model.py @@ -1,15 +1,16 @@ -# type: ignore -# import math - -# import random - -# from mava.utils.environments.RoboCup_env.robocup_utils import ( -# game_object, -# message_parser, -# sp_exceptions, -# ) - - +# Copyright 2021 [...placeholder...]. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. class WorldModel: """ Holds and updates the model of the world as known from current and past @@ -103,24 +104,6 @@ def __init__(self, action_handler): # create a new server parameter object for holding all server params self.server_parameters = ServerParameters() - # def euclidean_distance(self, point1, point2): - # """ - # Returns the Euclidean distance between two points on a plane. - # """ - - # if point1 is not None and point1[0] is not None and - # point1[1] is not None - # and point2 is not None and point2[0] is not None and - # point2[1] is not None: - # x1 = point1[0] - # y1 = point1[1] - # x2 = point2[0] - # y2 = point2[1] - - # return math.sqrt((x1 - x2) ** 2 + (y1 - y2) ** 2) - # except: - # return 200 - def process_new_info(self, ball, goals, players): """ Update any internal variables based on the currently available @@ -201,8 +184,6 @@ def is_kick_off_us(self): ko_left = WorldModel.PlayModes.KICK_OFF_L ko_right = WorldModel.PlayModes.KICK_OFF_R - # print self.play_mode - # return whether we're on the side that's kicking off return ( self.side == WorldModel.SIDE_L From 230ac7ea37893084199fb53e1a52336165900c75 Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Fri, 4 Jun 2021 13:06:29 +0200 Subject: [PATCH 161/188] clean robocup env --- .../environments/RoboCup_env/RoboCup2D_env.py | 59 ------------------- .../robocup_agents/custom_executor.py | 1 + .../RoboCup_env/robocup_agents/fixed_agent.py | 2 + .../environments/RoboCup_env/robocup_base.py | 15 +++++ .../RoboCup_env/robocup_utils/agent.py | 4 +- .../robocup_utils/extract_policy.py | 3 + .../RoboCup_env/robocup_utils/game_object.py | 3 + .../RoboCup_env/robocup_utils/handler.py | 3 + .../robocup_utils/message_parser.py | 3 + .../robocup_utils/player_world_model.py | 3 + .../RoboCup_env/robocup_utils/sock.py | 3 + .../robocup_utils/sp_exceptions.py | 3 + .../RoboCup_env/robocup_utils/trainer.py | 17 +++++- .../robocup_utils/trainer_world_model.py | 3 + .../robocup_utils/util_functions.py | 15 +++++ 15 files changed, 74 insertions(+), 63 deletions(-) diff --git a/mava/utils/environments/RoboCup_env/RoboCup2D_env.py b/mava/utils/environments/RoboCup_env/RoboCup2D_env.py index e0bb78594..ae96675c7 100755 --- a/mava/utils/environments/RoboCup_env/RoboCup2D_env.py +++ b/mava/utils/environments/RoboCup_env/RoboCup2D_env.py @@ -148,9 +148,6 @@ def __init__( self.beta = 0.01 - # max_dist = 200 - # self.alpha = 1/(max_dist*game_length*(1+beta)) - # Start the server start_server(game_setting, include_wait, port) @@ -206,9 +203,6 @@ def step(self, actions: Dict[str, str]): # start_step = time.time() self.game_step += 1 - # if self.game_step > 1: - # self.tot_out_step += start_step-self.end_step - for agent_key, agent in self.agents.items(): agent.do_action(actions[agent_key]) @@ -216,25 +210,12 @@ def step(self, actions: Dict[str, str]): if self.trainer: self.trainer.send_done() - # if self.game_step > 1: - # inside_step_done = time.time() - # self.tot_in_step_no_wait += inside_step_done-start_step - # Wait for the environment to step and provide the next observations wait_for_next_observations([self.trainer]) # Check if done with game done = self.game_step > self.game_length - # if done: - # print("Time outside step: ", self.tot_out_step) - # print("Time inside step without wait: ", self.tot_in_step_no_wait) - # print("Time waiting for the server: ", self.tot_server_wait) - # - # self.tot_out_step = 0.0 - # self.tot_in_step_no_wait = 0.0 - # self.tot_server_wait = 0.0 - # Calculate rewards rewards = {} score_add = [ @@ -286,15 +267,8 @@ def step(self, actions: Dict[str, str]): ) ball_towards_goal = ball_goal_dist - ball_goal_delta_dist - # player_ball_dist = wm.euclidean_distance( - # player["coords"], wm.ball["coords"] - # ) next_x = float(player["coords"][0]) + float(player["delta_coords"][0]) next_y = float(player["coords"][1]) + float(player["delta_coords"][1]) - # player_ball_delta_dist = wm.euclidean_distance( - # (next_x, next_y), wm.ball["coords"] - # ) - # player_towards_ball = player_ball_dist - player_ball_delta_dist scored = score_add[team_id] - score_add[opponent_id] @@ -323,22 +297,6 @@ def step(self, actions: Dict[str, str]): rewards[agent_key] = np.array(reward, np.float32) - # self.end_step = time.time() - # if self.game_step > 1: - # self.tot_server_wait += self.end_step - inside_step_done - - # Return latest observations and states - - # obs = self.__get_latest_obs() - # state = self.__get_state() - - # if "estimated_coords" in obs["player_0"]: - # print("Coords: ", (obs["player_0"]["estimated_coords"].tolist(), - # state["players"][0]["coords"]), "Body angle: ", - # (obs["player_0"]["ang_offs"], state["players"][0]["body_angle"])) - # else: - # print("No location info.") - return self.__get_latest_obs(), rewards, self.__get_state(), done def reset(self): @@ -425,21 +383,4 @@ def __get_latest_obs(self): obs[agent_key] = obs_dict - # TODO: Delete the if else below - # if len(obs) == 1: - # state = self.trainer.get_state_dict() - # - # if len(state["players"]) > 0: - # obs["player_0"]["estimated_coords"] = - # np.array(state["players"][0]["coords"]) - # obs["player_0"]["ang_offs"] = state["players"][0]["body_angle"] - # else: - # obs["player_0"]["estimated_coords"] = [0.0, 0.0] - # obs["player_0"]["ang_offs"] = 0.0 - # - # # print("Agent: ", obs["player_0"]) - # # print("Trainer: ", self.trainer.get_state_dict()) - # else: - # print("Remove this trainer overwrite this!!!") - # exit() return obs diff --git a/mava/utils/environments/RoboCup_env/robocup_agents/custom_executor.py b/mava/utils/environments/RoboCup_env/robocup_agents/custom_executor.py index b0cc5d6b4..0e260fd47 100644 --- a/mava/utils/environments/RoboCup_env/robocup_agents/custom_executor.py +++ b/mava/utils/environments/RoboCup_env/robocup_agents/custom_executor.py @@ -1,3 +1,4 @@ +# type: ignore # python3 # Copyright 2021 [...placeholder...]. All rights reserved. # diff --git a/mava/utils/environments/RoboCup_env/robocup_agents/fixed_agent.py b/mava/utils/environments/RoboCup_env/robocup_agents/fixed_agent.py index 989d9b382..0f11d629f 100755 --- a/mava/utils/environments/RoboCup_env/robocup_agents/fixed_agent.py +++ b/mava/utils/environments/RoboCup_env/robocup_agents/fixed_agent.py @@ -13,6 +13,8 @@ # See the License for the specific language governing permissions and # limitations under the License. +# type: ignore + import numpy as np from mava.utils.environments.RoboCup_env.robocup_utils.util_functions import ( diff --git a/mava/utils/environments/RoboCup_env/robocup_base.py b/mava/utils/environments/RoboCup_env/robocup_base.py index 86f35ea27..7608360d8 100644 --- a/mava/utils/environments/RoboCup_env/robocup_base.py +++ b/mava/utils/environments/RoboCup_env/robocup_base.py @@ -1,3 +1,18 @@ +# python3 +# Copyright 2018 DeepMind Technologies Limited. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + # type: ignore from typing import Tuple diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/agent.py b/mava/utils/environments/RoboCup_env/robocup_utils/agent.py index 81e6aeef2..5a2deca01 100755 --- a/mava/utils/environments/RoboCup_env/robocup_utils/agent.py +++ b/mava/utils/environments/RoboCup_env/robocup_utils/agent.py @@ -13,11 +13,11 @@ # See the License for the specific language governing permissions and # limitations under the License. +# type: ignore + import threading import time - -# import math import numpy as np from mava.utils.environments.RoboCup_env.robocup_utils import ( diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/extract_policy.py b/mava/utils/environments/RoboCup_env/robocup_utils/extract_policy.py index 66cc359d7..5900c9336 100644 --- a/mava/utils/environments/RoboCup_env/robocup_utils/extract_policy.py +++ b/mava/utils/environments/RoboCup_env/robocup_utils/extract_policy.py @@ -1,3 +1,4 @@ +# python3 # Copyright 2021 [...placeholder...]. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -12,6 +13,8 @@ # See the License for the specific language governing permissions and # limitations under the License. +# type: ignore + import os import pickle diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/game_object.py b/mava/utils/environments/RoboCup_env/robocup_utils/game_object.py index 246c1ebf7..a04584a58 100755 --- a/mava/utils/environments/RoboCup_env/robocup_utils/game_object.py +++ b/mava/utils/environments/RoboCup_env/robocup_utils/game_object.py @@ -1,3 +1,4 @@ +# python3 # Copyright 2021 [...placeholder...]. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -11,6 +12,8 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. + +# type: ignore class GameObject: """ Root class for all percievable objects in the world model. diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/handler.py b/mava/utils/environments/RoboCup_env/robocup_utils/handler.py index 2530faf3c..92a4811b7 100755 --- a/mava/utils/environments/RoboCup_env/robocup_utils/handler.py +++ b/mava/utils/environments/RoboCup_env/robocup_utils/handler.py @@ -1,3 +1,4 @@ +# python3 # Copyright 2021 [...placeholder...]. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -12,6 +13,8 @@ # See the License for the specific language governing permissions and # limitations under the License. +# type: ignore + import collections import queue diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/message_parser.py b/mava/utils/environments/RoboCup_env/robocup_utils/message_parser.py index 262e27b4c..ed454876a 100755 --- a/mava/utils/environments/RoboCup_env/robocup_utils/message_parser.py +++ b/mava/utils/environments/RoboCup_env/robocup_utils/message_parser.py @@ -1,3 +1,4 @@ +# python3 # Copyright 2021 [...placeholder...]. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -12,6 +13,8 @@ # See the License for the specific language governing permissions and # limitations under the License. +# type: ignore + import re # used to convert server value strings into actual python values diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/player_world_model.py b/mava/utils/environments/RoboCup_env/robocup_utils/player_world_model.py index a6591dbd0..4a4517fac 100755 --- a/mava/utils/environments/RoboCup_env/robocup_utils/player_world_model.py +++ b/mava/utils/environments/RoboCup_env/robocup_utils/player_world_model.py @@ -1,3 +1,4 @@ +# python3 # Copyright 2021 [...placeholder...]. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -12,6 +13,8 @@ # See the License for the specific language governing permissions and # limitations under the License. +# type: ignore + import math import numpy as np diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/sock.py b/mava/utils/environments/RoboCup_env/robocup_utils/sock.py index 0e09b9a21..02d1e9b4f 100755 --- a/mava/utils/environments/RoboCup_env/robocup_utils/sock.py +++ b/mava/utils/environments/RoboCup_env/robocup_utils/sock.py @@ -1,3 +1,4 @@ +# python3 # Copyright 2021 [...placeholder...]. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -12,6 +13,8 @@ # See the License for the specific language governing permissions and # limitations under the License. +# type: ignore + import socket diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/sp_exceptions.py b/mava/utils/environments/RoboCup_env/robocup_utils/sp_exceptions.py index 516395d0e..6b7368e3c 100755 --- a/mava/utils/environments/RoboCup_env/robocup_utils/sp_exceptions.py +++ b/mava/utils/environments/RoboCup_env/robocup_utils/sp_exceptions.py @@ -1,3 +1,4 @@ +# python3 # Copyright 2021 [...placeholder...]. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -12,6 +13,8 @@ # See the License for the specific language governing permissions and # limitations under the License. +# type: ignore + class SoccerServerError(Exception): """ diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/trainer.py b/mava/utils/environments/RoboCup_env/robocup_utils/trainer.py index c086114c6..31ea65c80 100755 --- a/mava/utils/environments/RoboCup_env/robocup_utils/trainer.py +++ b/mava/utils/environments/RoboCup_env/robocup_utils/trainer.py @@ -1,7 +1,20 @@ -# type: ignore # !/usr/bin/env python3 +# Copyright 2021 [...placeholder...]. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# type: ignore -# import os import random import threading import time diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/trainer_world_model.py b/mava/utils/environments/RoboCup_env/robocup_utils/trainer_world_model.py index 8b1e0e4c2..aab4210f5 100755 --- a/mava/utils/environments/RoboCup_env/robocup_utils/trainer_world_model.py +++ b/mava/utils/environments/RoboCup_env/robocup_utils/trainer_world_model.py @@ -1,3 +1,4 @@ +# python3 # Copyright 2021 [...placeholder...]. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -11,6 +12,8 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. + +# type: ignore class WorldModel: """ Holds and updates the model of the world as known from current and past diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/util_functions.py b/mava/utils/environments/RoboCup_env/robocup_utils/util_functions.py index ecbd26846..9405c6fc4 100644 --- a/mava/utils/environments/RoboCup_env/robocup_utils/util_functions.py +++ b/mava/utils/environments/RoboCup_env/robocup_utils/util_functions.py @@ -1,3 +1,18 @@ +# python3 +# Copyright 2021 [...placeholder...]. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + # type: ignore import time from typing import Dict, NamedTuple From 21db40db56174826918d8bc4d7c4dfd976224f97 Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Fri, 4 Jun 2021 13:08:25 +0200 Subject: [PATCH 162/188] add todo to move wrapper to wrapper folder --- mava/utils/environments/RoboCup_env/robocup_base.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/mava/utils/environments/RoboCup_env/robocup_base.py b/mava/utils/environments/RoboCup_env/robocup_base.py index 7608360d8..d943ebda0 100644 --- a/mava/utils/environments/RoboCup_env/robocup_base.py +++ b/mava/utils/environments/RoboCup_env/robocup_base.py @@ -24,6 +24,8 @@ from mava.utils.environments.RoboCup_env.RoboCup2D_env import RoboCup2D from mava.utils.environments.RoboCup_env.robocup_utils.util_functions import SpecWrapper +# TODO: move wrapper to mava/wrappers folder + class RoboCupWrapper(SpecWrapper): """Environment wrapper for 2D RoboCup environment.""" From 4890d444996f248388ff80143debbd6500def72b Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Fri, 4 Jun 2021 13:12:44 +0200 Subject: [PATCH 163/188] fix mypy issues - todo need to check typing --- .../environments/RoboCup_env/robocup_agents/custom_executor.py | 3 ++- mava/utils/environments/RoboCup_env/robocup_agents/nn_agent.py | 2 ++ mava/utils/environments/RoboCup_env/robocup_utils/__init__.py | 2 +- mava/utils/environments/RoboCup_env/robocup_utils/agent.py | 1 + .../environments/RoboCup_env/robocup_utils/message_parser.py | 2 +- 5 files changed, 7 insertions(+), 3 deletions(-) diff --git a/mava/utils/environments/RoboCup_env/robocup_agents/custom_executor.py b/mava/utils/environments/RoboCup_env/robocup_agents/custom_executor.py index 0e260fd47..b6bd70170 100644 --- a/mava/utils/environments/RoboCup_env/robocup_agents/custom_executor.py +++ b/mava/utils/environments/RoboCup_env/robocup_agents/custom_executor.py @@ -1,4 +1,3 @@ -# type: ignore # python3 # Copyright 2021 [...placeholder...]. All rights reserved. # @@ -14,6 +13,8 @@ # See the License for the specific language governing permissions and # limitations under the License. +# type: ignore + from typing import Dict, Optional import dm_env diff --git a/mava/utils/environments/RoboCup_env/robocup_agents/nn_agent.py b/mava/utils/environments/RoboCup_env/robocup_agents/nn_agent.py index b673174f3..2dab2c3f2 100755 --- a/mava/utils/environments/RoboCup_env/robocup_agents/nn_agent.py +++ b/mava/utils/environments/RoboCup_env/robocup_agents/nn_agent.py @@ -13,6 +13,8 @@ # See the License for the specific language governing permissions and # limitations under the License. +# type: ignore + import os from typing import Any, Dict, Mapping, MutableSequence, Sequence, Union diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/__init__.py b/mava/utils/environments/RoboCup_env/robocup_utils/__init__.py index 06240e71c..f6af6a34e 100755 --- a/mava/utils/environments/RoboCup_env/robocup_utils/__init__.py +++ b/mava/utils/environments/RoboCup_env/robocup_utils/__init__.py @@ -11,4 +11,4 @@ # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and -# limitations under the License. \ No newline at end of file +# limitations under the License. diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/agent.py b/mava/utils/environments/RoboCup_env/robocup_utils/agent.py index 5a2deca01..f03c0c4c0 100755 --- a/mava/utils/environments/RoboCup_env/robocup_utils/agent.py +++ b/mava/utils/environments/RoboCup_env/robocup_utils/agent.py @@ -18,6 +18,7 @@ import threading import time + import numpy as np from mava.utils.environments.RoboCup_env.robocup_utils import ( diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/message_parser.py b/mava/utils/environments/RoboCup_env/robocup_utils/message_parser.py index ed454876a..ca98b6ca2 100755 --- a/mava/utils/environments/RoboCup_env/robocup_utils/message_parser.py +++ b/mava/utils/environments/RoboCup_env/robocup_utils/message_parser.py @@ -152,4 +152,4 @@ def parse(text): # noqa: C901 # append the first '('. return result[0] else: - return None \ No newline at end of file + return None From 4e11825c6131085451dcbaa9206b677362095ac2 Mon Sep 17 00:00:00 2001 From: Dries Date: Fri, 4 Jun 2021 15:13:04 +0200 Subject: [PATCH 164/188] fix: Moved RoboCup files to their appropriate places in Mava. --- .../run_state_based_recurrent_mad4pg.py | 17 +----- mava/utils/environments/robocup_utils.py | 60 +++++++++++++++++++ mava/wrappers/__init__.py | 1 + .../robocup_base.py => wrappers/robocup.py} | 39 ------------ 4 files changed, 64 insertions(+), 53 deletions(-) create mode 100644 mava/utils/environments/robocup_utils.py rename mava/{utils/environments/RoboCup_env/robocup_base.py => wrappers/robocup.py} (71%) diff --git a/examples/robocup/run_state_based_recurrent_mad4pg.py b/examples/robocup/run_state_based_recurrent_mad4pg.py index 16c78324c..697446285 100644 --- a/examples/robocup/run_state_based_recurrent_mad4pg.py +++ b/examples/robocup/run_state_based_recurrent_mad4pg.py @@ -4,7 +4,6 @@ from datetime import datetime from typing import Any -import dm_env import launchpad as lp from absl import app, flags from launchpad.nodes.python.local_multi_processing import PythonProcess @@ -14,10 +13,10 @@ from mava.systems.tf.mad4pg.execution import MAD4PGRecurrentExecutor from mava.systems.tf.mad4pg.training import MAD4PGStateBasedRecurrentTrainer from mava.utils import lp_utils +from mava.utils.environments import robocup_utils from mava.utils.environments.RoboCup_env.robocup_agents.nn_agent import ( make_recurrent_networks as make_networks, ) -from mava.utils.environments.RoboCup_env.robocup_base import create_robocup_environment from mava.utils.loggers import logger_utils FLAGS = flags.FLAGS @@ -27,27 +26,17 @@ "Experiment identifier that can be used to continue experiments.", ) -flags.DEFINE_string("num_executors", "6", "The number of executors to run.") +flags.DEFINE_string("num_executors", "2", "The number of executors to run.") flags.DEFINE_string("base_dir", "~/mava/", "Base dir to store experiments.") -def make_environment(**kwargs) -> dm_env.Environment: - """Creates a MPE environment.""" - - def environment_fn(**kwargs): - return create_robocup_environment(game_name="domain_randomisation", **kwargs) - - return environment_fn - - def main(_: Any) -> None: # Environment # Create an environment, grab the spec, and use it to create networks. - environment_fn = make_environment() # Neural Networks # Create the networks - environment_factory = lp_utils.partial_kwargs(environment_fn) + environment_factory = lp_utils.partial_kwargs(robocup_utils.make_environment) network_factory = lp_utils.partial_kwargs(make_networks) # Checkpointer appends "Checkpoints" to checkpoint_dir diff --git a/mava/utils/environments/robocup_utils.py b/mava/utils/environments/robocup_utils.py new file mode 100644 index 000000000..fb5c85b60 --- /dev/null +++ b/mava/utils/environments/robocup_utils.py @@ -0,0 +1,60 @@ +# python3 +# Copyright 2021 [...placeholder...]. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Pettingzoo environment factory.""" + +import dm_env +import numpy as np + +from mava.utils.environments.RoboCup_env.RoboCup2D_env import RoboCup2D +from mava.wrappers.robocup import RoboCupWrapper + + +def make_environment( + game_name: str = "domain_randomisation", + evaluation: bool = False, +) -> dm_env.Environment: + """Wraps the Robocup environment with some basic preprocessing. + + Args: + game_name: str, the name of the Robocup game setting. + evaluation: bool, to change the behaviour during evaluation. + + Returns: + A Robocup environment with some standard preprocessing. + """ + + # Create environment + if game_name == "domain_randomisation": + players_per_team = [1, 0] + elif game_name == "reward_shaping": + players_per_team = [1, 0] + # elif game_name == "fixed_opponent": + # players_per_team = [2, 2] + else: + raise NotImplementedError("Game type not implemented: ", game_name) + + # TODO: Change this to better assign ports + rand_port = np.random.randint(6000, 60000) + robocup_env = RoboCup2D( + game_setting=game_name, + team_names=["Team_A", "Team_B"], + players_per_team=players_per_team, + render_game=False, + include_wait=False, + game_length=1000, + port=rand_port, + ) + return RoboCupWrapper(robocup_env) diff --git a/mava/wrappers/__init__.py b/mava/wrappers/__init__.py index 02a95a511..beee9cab4 100644 --- a/mava/wrappers/__init__.py +++ b/mava/wrappers/__init__.py @@ -24,6 +24,7 @@ PettingZooAECEnvWrapper, PettingZooParallelEnvWrapper, ) +from mava.wrappers.robocup import RoboCupWrapper # TODO only import smac if the user needs it. from mava.wrappers.smac import SMACEnvWrapper diff --git a/mava/utils/environments/RoboCup_env/robocup_base.py b/mava/wrappers/robocup.py similarity index 71% rename from mava/utils/environments/RoboCup_env/robocup_base.py rename to mava/wrappers/robocup.py index d943ebda0..c1694d3df 100644 --- a/mava/utils/environments/RoboCup_env/robocup_base.py +++ b/mava/wrappers/robocup.py @@ -24,8 +24,6 @@ from mava.utils.environments.RoboCup_env.RoboCup2D_env import RoboCup2D from mava.utils.environments.RoboCup_env.robocup_utils.util_functions import SpecWrapper -# TODO: move wrapper to mava/wrappers folder - class RoboCupWrapper(SpecWrapper): """Environment wrapper for 2D RoboCup environment.""" @@ -85,40 +83,3 @@ def environment(self) -> gym.Env: def close(self): self._environment.close() - - -def create_robocup_environment( - game_name: str, evaluation: bool = False -) -> dm_env.Environment: - """Wraps the Robocup environment with some basic preprocessing. - - Args: - game_name: str, the name of the Robocup game setting. - evaluation: bool, to change the behaviour during evaluation. - - Returns: - A Robocup environment with some standard preprocessing. - """ - - # Create environment - if game_name == "domain_randomisation": - players_per_team = [1, 0] - elif game_name == "reward_shaping": - players_per_team = [1, 0] - # elif game_name == "fixed_opponent": - # players_per_team = [2, 2] - else: - raise NotImplementedError("Game type not implemented: ", game_name) - - # TODO: Change this to better assign ports - rand_port = np.random.randint(6000, 60000) - robocup_env = RoboCup2D( - game_setting=game_name, - team_names=["Team_A", "Team_B"], - players_per_team=players_per_team, - render_game=False, - include_wait=False, - game_length=1000, - port=rand_port, - ) - return RoboCupWrapper(robocup_env) From 0dbf259e19cf41052add4ed9a59041653b3334ce Mon Sep 17 00:00:00 2001 From: Dries Date: Fri, 4 Jun 2021 15:44:08 +0200 Subject: [PATCH 165/188] feature: Update the main README.md with the RoboCup installation process. --- README.md | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 4a38d6e8d..1fa524834 100644 --- a/README.md +++ b/README.md @@ -153,6 +153,12 @@ We have tested `mava` on Python 3.6, 3.7 and 3.8. ```bash ./install_sc2.sh +3. Install 2D RoboCup environment [Optional]: + To install the environment, please run the robocup docker build command after running the Mava docker build command. + ```bash + make build + make build_robocup + ``` ### Python virtual environment 1. If not using docker, we strongly recommend using a @@ -188,7 +194,10 @@ We have tested `mava` on Python 3.6, 3.7 and 3.8. pip install pysc2 pip install git+https://github.com/oxwhirl/smac.git ``` - +3. Install 2D RoboCup environment: + A local install of the 2D RoboCup environment has only been tested using the Ubuntu 18.04 operating system. + The installation can be performed by running all the commands, inside the Dockerfile.robocup, manually while inside the Mava + python virtual environment. We also have a list of [optional installs](OPTIONAL_INSTALL.md) for extra functionality such as the use of Atari environments, environment wrappers, gpu support and agent episode recording. ## Debugging From b67749b6f9e0ad8b47c544b6e2ea5fd191310130 Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Fri, 4 Jun 2021 15:48:59 +0200 Subject: [PATCH 166/188] correct copyright --- mava/wrappers/robocup.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/mava/wrappers/robocup.py b/mava/wrappers/robocup.py index c1694d3df..9d873b907 100644 --- a/mava/wrappers/robocup.py +++ b/mava/wrappers/robocup.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2018 DeepMind Technologies Limited. All rights reserved. +# Copyright 2021 [...placeholder...]. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -13,7 +13,6 @@ # See the License for the specific language governing permissions and # limitations under the License. -# type: ignore from typing import Tuple import dm_env From a8d4219ac3fe45a5b76f0ebd79ac9876e31ce158 Mon Sep 17 00:00:00 2001 From: Dries Date: Fri, 4 Jun 2021 15:49:53 +0200 Subject: [PATCH 167/188] fix: Update the readme file. --- README.md | 7 ++++--- install_robocup.sh | 49 ++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 53 insertions(+), 3 deletions(-) create mode 100755 install_robocup.sh diff --git a/README.md b/README.md index 1fa524834..e2bb5c04b 100644 --- a/README.md +++ b/README.md @@ -194,10 +194,11 @@ We have tested `mava` on Python 3.6, 3.7 and 3.8. pip install pysc2 pip install git+https://github.com/oxwhirl/smac.git ``` -3. Install 2D RoboCup environment: - A local install of the 2D RoboCup environment has only been tested using the Ubuntu 18.04 operating system. - The installation can be performed by running all the commands, inside the Dockerfile.robocup, manually while inside the Mava +3. For the 2D RoboCup environment, a local install has only been tested using the Ubuntu 18.04 operating system. + The installation can be performed by running the robocup bash script while inside the Mava python virtual environment. + ```bash + ./install_sc2.sh We also have a list of [optional installs](OPTIONAL_INSTALL.md) for extra functionality such as the use of Atari environments, environment wrappers, gpu support and agent episode recording. ## Debugging diff --git a/install_robocup.sh b/install_robocup.sh new file mode 100755 index 000000000..1dca35651 --- /dev/null +++ b/install_robocup.sh @@ -0,0 +1,49 @@ +sudo apt-get update \ + && apt-get install -y --no-install-recommends \ + apt-utils \ + build-essential \ + curl \ + xvfb \ + ffmpeg \ + xorg-dev \ + libsdl2-dev \ + swig \ + cmake \ + python-opengl + +# fetch repo / ppa packages, etc +sudo apt-get -y update --fix-missing +# Install package, that hangs the operation, separately +sudo DEBIAN_FRONTEND=noninteractive apt install -y tzdata + +sudo apt update && \ + apt -y install autoconf bison clang flex libboost-dev libboost-all-dev libc6-dev make wget + +sudo apt -y install build-essential libboost-all-dev qt5-default libfontconfig1-dev libaudio-dev libxt-dev libglib2.0-dev libxi-dev libxrender-dev libboost-all-dev + +sudo wget https://github.com/rcsoccersim/rcssserver/archive/rcssserver-16.0.0.tar.gz && \ + tar xfz rcssserver-16.0.0.tar.gz && \ + cd rcssserver-rcssserver-16.0.0 && \ + ./bootstrap && \ + ./configure && \ + make && \ + make install && \ + ldconfig + +sudo wget https://github.com/rcsoccersim/rcssmonitor/archive/rcssmonitor-16.0.0.tar.gz && \ + tar xfz rcssmonitor-16.0.0.tar.gz && \ + cd rcssmonitor-rcssmonitor-16.0.0 && \ + ./bootstrap && \ + ./configure && \ + make && \ + make install && \ + ldconfig + +sudo ldconfig && \ + apt update && \ + apt install -y libboost-filesystem1.65.1 libboost-system1.65.1 libboost-program-options-dev tmux + +sudo apt-get install -y libqt5widgets5 +# RUN pip install hydra-core + +# ENV PYTHONPATH '/home' From be2c61ec3cd7bbe53e7d49192ecd2d5bc2470960 Mon Sep 17 00:00:00 2001 From: Dries Date: Fri, 4 Jun 2021 15:51:06 +0200 Subject: [PATCH 168/188] Small changes. --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index e2bb5c04b..e4fda76bb 100644 --- a/README.md +++ b/README.md @@ -195,10 +195,10 @@ We have tested `mava` on Python 3.6, 3.7 and 3.8. pip install git+https://github.com/oxwhirl/smac.git ``` 3. For the 2D RoboCup environment, a local install has only been tested using the Ubuntu 18.04 operating system. - The installation can be performed by running the robocup bash script while inside the Mava + The installation can be performed by running the RoboCup bash script while inside the Mava python virtual environment. ```bash - ./install_sc2.sh + ./install_robocup.sh We also have a list of [optional installs](OPTIONAL_INSTALL.md) for extra functionality such as the use of Atari environments, environment wrappers, gpu support and agent episode recording. ## Debugging From 0f2633cb1d39fe8c081f5796a2471a4eb3e4d082 Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Fri, 4 Jun 2021 15:57:09 +0200 Subject: [PATCH 169/188] add inits and update typing --- .../environments/RoboCup_env/__init__.py | 0 .../RoboCup_env/robocup_agents/__init__.py | 0 .../environments/RoboCup_env/robocup_base.py | 124 ++++++++++++++++++ mava/wrappers/robocup.py | 4 +- 4 files changed, 126 insertions(+), 2 deletions(-) create mode 100644 mava/utils/environments/RoboCup_env/__init__.py create mode 100644 mava/utils/environments/RoboCup_env/robocup_agents/__init__.py create mode 100644 mava/utils/environments/RoboCup_env/robocup_base.py diff --git a/mava/utils/environments/RoboCup_env/__init__.py b/mava/utils/environments/RoboCup_env/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/mava/utils/environments/RoboCup_env/robocup_agents/__init__.py b/mava/utils/environments/RoboCup_env/robocup_agents/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/mava/utils/environments/RoboCup_env/robocup_base.py b/mava/utils/environments/RoboCup_env/robocup_base.py new file mode 100644 index 000000000..628c33e4f --- /dev/null +++ b/mava/utils/environments/RoboCup_env/robocup_base.py @@ -0,0 +1,124 @@ +# python3 +# Copyright 2021 [...placeholder...]. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# type: ignore +from typing import Tuple + +import dm_env +import gym +import numpy as np +from acme import types + +from mava.utils.environments.RoboCup_env.RoboCup2D_env import RoboCup2D +from mava.utils.environments.RoboCup_env.robocup_utils.util_functions import SpecWrapper + +# TODO: move wrapper to mava/wrappers folder + + +class RoboCupWrapper(SpecWrapper): + """Environment wrapper for 2D RoboCup environment.""" + + # Note: we don't inherit from base.EnvironmentWrapper because that class + # assumes that the wrapped environment is a dm_env.Environment. + + def __init__(self, environment: RoboCup2D): + self._environment = environment + self._reset_next_step = True + assert environment.game_setting in ["reward_shaping", "domain_randomisation"] + + super().__init__(environment.num_players) + + def reset(self) -> Tuple[dm_env.TimeStep, np.array]: + """Resets the episode.""" + self._reset_next_step = False + raw_obs, _, state = self._environment.reset() + proc_obs = self._proc_robocup_obs(observations=raw_obs, done=False) + proccessed_state = self._proc_robocup_state(state, proc_obs) + timestep = dm_env.restart(proc_obs) + return timestep, {"env_state": proccessed_state} + + def step(self, nn_actions: types.NestedArray) -> Tuple[dm_env.TimeStep, np.array]: + """Steps the environment.""" + if self._reset_next_step: + return self.reset() + + actions = self._proc_robocup_actions(nn_actions) + raw_obs, rewards, state, done = self._environment.step(actions) + self._reset_next_step = done + + proc_obs = self._proc_robocup_obs( + observations=raw_obs, done=done, nn_actions=nn_actions + ) + proccessed_state = self._proc_robocup_state(state, proc_obs) + + if done: + self._step_type = dm_env.StepType.LAST + else: + self._step_type = dm_env.StepType.MID + + return ( + dm_env.TimeStep( + observation=proc_obs, + reward=rewards, + discount=self._discount, + step_type=self._step_type, + ), + {"env_state": proccessed_state}, + ) + + @property + def environment(self) -> gym.Env: + """Returns the wrapped environment.""" + return self._environment + + def close(self): + self._environment.close() + + +def create_robocup_environment( + game_name: str, evaluation: bool = False +) -> dm_env.Environment: + """Wraps the Robocup environment with some basic preprocessing. + + Args: + game_name: str, the name of the Robocup game setting. + evaluation: bool, to change the behaviour during evaluation. + + Returns: + A Robocup environment with some standard preprocessing. + """ + + # Create environment + if game_name == "domain_randomisation": + players_per_team = [1, 0] + elif game_name == "reward_shaping": + players_per_team = [1, 0] + # elif game_name == "fixed_opponent": + # players_per_team = [2, 2] + else: + raise NotImplementedError("Game type not implemented: ", game_name) + + # TODO: Change this to better assign ports + rand_port = np.random.randint(6000, 60000) + robocup_env = RoboCup2D( + game_setting=game_name, + team_names=["Team_A", "Team_B"], + players_per_team=players_per_team, + render_game=False, + include_wait=False, + game_length=1000, + port=rand_port, + ) + return RoboCupWrapper(robocup_env) diff --git a/mava/wrappers/robocup.py b/mava/wrappers/robocup.py index 9d873b907..44b7ea6ba 100644 --- a/mava/wrappers/robocup.py +++ b/mava/wrappers/robocup.py @@ -30,7 +30,7 @@ class RoboCupWrapper(SpecWrapper): # Note: we don't inherit from base.EnvironmentWrapper because that class # assumes that the wrapped environment is a dm_env.Environment. - def __init__(self, environment: RoboCup2D): + def __init__(self, environment: RoboCup2D) -> None: self._environment = environment self._reset_next_step = True assert environment.game_setting in ["reward_shaping", "domain_randomisation"] @@ -80,5 +80,5 @@ def environment(self) -> gym.Env: """Returns the wrapped environment.""" return self._environment - def close(self): + def close(self) -> None: self._environment.close() From 0e2b586528076a268fbb16e0c23b1d04f10a1bef Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Fri, 4 Jun 2021 15:58:54 +0200 Subject: [PATCH 170/188] copy stubs to inits --- mava/utils/environments/RoboCup_env/__init__.py | 14 ++++++++++++++ .../RoboCup_env/robocup_agents/__init__.py | 14 ++++++++++++++ 2 files changed, 28 insertions(+) diff --git a/mava/utils/environments/RoboCup_env/__init__.py b/mava/utils/environments/RoboCup_env/__init__.py index e69de29bb..06240e71c 100644 --- a/mava/utils/environments/RoboCup_env/__init__.py +++ b/mava/utils/environments/RoboCup_env/__init__.py @@ -0,0 +1,14 @@ +# python3 +# Copyright 2021 [...placeholder...]. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. \ No newline at end of file diff --git a/mava/utils/environments/RoboCup_env/robocup_agents/__init__.py b/mava/utils/environments/RoboCup_env/robocup_agents/__init__.py index e69de29bb..06240e71c 100644 --- a/mava/utils/environments/RoboCup_env/robocup_agents/__init__.py +++ b/mava/utils/environments/RoboCup_env/robocup_agents/__init__.py @@ -0,0 +1,14 @@ +# python3 +# Copyright 2021 [...placeholder...]. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. \ No newline at end of file From b9f960c0a0d3a4e3f3f67332defca51a45bcd6f3 Mon Sep 17 00:00:00 2001 From: Dries Date: Fri, 4 Jun 2021 16:21:38 +0200 Subject: [PATCH 171/188] Fix mypy. --- .../environments/RoboCup_env/RoboCup2D_env.py | 60 +++++++++++-------- mava/wrappers/robocup.py | 8 +-- 2 files changed, 39 insertions(+), 29 deletions(-) diff --git a/mava/utils/environments/RoboCup_env/RoboCup2D_env.py b/mava/utils/environments/RoboCup_env/RoboCup2D_env.py index ae96675c7..4355d42a4 100755 --- a/mava/utils/environments/RoboCup_env/RoboCup2D_env.py +++ b/mava/utils/environments/RoboCup_env/RoboCup2D_env.py @@ -1,20 +1,25 @@ -# type: ignore import multiprocessing as mp import os import time -from typing import Dict +from typing import Any, Dict, List, Tuple import numpy as np -from mava.utils.environments.RoboCup_env.robocup_utils.agent import Agent as basic_agent -from mava.utils.environments.RoboCup_env.robocup_utils.trainer import Trainer -from mava.utils.environments.RoboCup_env.robocup_utils.util_functions import ( +from mava.utils.environments.RoboCup_env.robocup_utils.agent import ( # type: ignore # noqa: E501 + Agent as basic_agent, +) +from mava.utils.environments.RoboCup_env.robocup_utils.trainer import ( # type: ignore # noqa: E501 + Trainer, +) +from mava.utils.environments.RoboCup_env.robocup_utils.util_functions import ( # type: ignore # noqa: E501 wait_for_next_observations, ) # spawn an agent of team_name, with position -def spawn_agent(team_name, team_id, agent_id, num_players, port): +def spawn_agent( + team_name: str, team_id: int, agent_id: int, num_players: int, port: int +) -> basic_agent: """ Used to run an agent in a seperate physical process. """ @@ -26,7 +31,7 @@ def spawn_agent(team_name, team_id, agent_id, num_players, port): return a -def run_server_thread(game_setting, include_wait, port): +def run_server_thread(game_setting: str, include_wait: bool, port: int) -> None: # ./usr/local/bin/rcssmonitor command = ( "/usr/local/bin/rcssserver -server::coach_w_referee=on" @@ -60,18 +65,18 @@ def run_server_thread(game_setting, include_wait, port): os.system(command) -def start_trainer(port): +def start_trainer(port: int) -> Trainer: trainer = Trainer() trainer.connect("localhost", port + 1) return trainer -def run_monitor_thread(port): +def run_monitor_thread(port: int) -> None: # ./usr/local/bin/rcssmonitor os.system("/usr/local/bin/rcssmonitor --server-port=" + str(port)) -def start_server(game_setting, include_wait, port): +def start_server(game_setting: str, include_wait: bool, port: int) -> None: # Wait for server to startup completely # print("mode: ", mode) at = mp.Process(target=run_server_thread, args=(game_setting, include_wait, port)) @@ -80,7 +85,7 @@ def start_server(game_setting, include_wait, port): time.sleep(0.2) -def start_monitor(port): +def start_monitor(port: int) -> None: # Wait for monitor process to complete at = mp.Process(target=run_monitor_thread, args=(port,)) at.daemon = True @@ -88,7 +93,14 @@ def start_monitor(port): time.sleep(0.2) -def connect_agents(team_name, team_id, num_per_team, num_players, start_id, port): +def connect_agents( + team_name: str, + team_id: int, + num_per_team: int, + num_players: int, + start_id: int, + port: int, +) -> List: # spawn all agents as seperate processes for maximum processing efficiency agents = [] for agent_id in range(start_id, start_id + num_per_team): @@ -110,14 +122,14 @@ def connect_agents(team_name, team_id, num_per_team, num_players, start_id, port class RoboCup2D: def __init__( self, - game_setting="reward_shaping", - include_wait=False, - team_names=["Team1", "Team2"], - players_per_team=11, - render_game=False, - game_length=6000, - beta=0.1, - port=6000, + game_setting: str = "reward_shaping", + include_wait: bool = False, + team_names: List = ["Team1", "Team2"], + players_per_team: List = [11, 11], + render_game: bool = False, + game_length: int = 6000, + beta: float = 0.1, + port: int = 6000, ): self.game_setting = game_setting @@ -198,7 +210,7 @@ def __init__( self.agents["player_" + str(agent.agent_id)] = agent start_id += players_per_team[t_i] - def step(self, actions: Dict[str, str]): + def step(self, actions: Dict[str, Any]) -> Tuple[Any, Any, Any, Any]: # Do agent update # start_step = time.time() self.game_step += 1 @@ -299,7 +311,7 @@ def step(self, actions: Dict[str, str]): return self.__get_latest_obs(), rewards, self.__get_state(), done - def reset(self): + def reset(self) -> Tuple[Any, Any, Any]: self.game_step = 0 if self.game_setting == "domain_randomisation": goal_diff = self.game_scores[0] - self.game_scores[1] @@ -362,7 +374,7 @@ def reset(self): wait_for_next_observations([self.trainer]) return self.__get_latest_obs(), rewards, self.__get_state() - def __get_state(self): + def __get_state(self) -> Dict[str, Any]: # Return latest observations to agents state_dict = self.trainer.get_state_dict() # TODO: Return per agent states. The states should be mirrored. So a critic and @@ -372,7 +384,7 @@ def __get_state(self): state_dict["game_length"] = self.game_length return state_dict - def __get_latest_obs(self): + def __get_latest_obs(self) -> Dict[str, Any]: # Return latest observations to agents obs = {} for agent_key, agent in self.agents.items(): diff --git a/mava/wrappers/robocup.py b/mava/wrappers/robocup.py index c1694d3df..2c5ae636a 100644 --- a/mava/wrappers/robocup.py +++ b/mava/wrappers/robocup.py @@ -13,7 +13,6 @@ # See the License for the specific language governing permissions and # limitations under the License. -# type: ignore from typing import Tuple import dm_env @@ -22,7 +21,9 @@ from acme import types from mava.utils.environments.RoboCup_env.RoboCup2D_env import RoboCup2D -from mava.utils.environments.RoboCup_env.robocup_utils.util_functions import SpecWrapper +from mava.utils.environments.RoboCup_env.robocup_utils.util_functions import ( # type: ignore # noqa: E501 + SpecWrapper, +) class RoboCupWrapper(SpecWrapper): @@ -80,6 +81,3 @@ def step(self, nn_actions: types.NestedArray) -> Tuple[dm_env.TimeStep, np.array def environment(self) -> gym.Env: """Returns the wrapped environment.""" return self._environment - - def close(self): - self._environment.close() From cfdbc48005da3c18d1b0486db275048ccd834e10 Mon Sep 17 00:00:00 2001 From: Dries Date: Fri, 4 Jun 2021 16:26:16 +0200 Subject: [PATCH 172/188] Automatically reformat code. --- mava/utils/environments/RoboCup_env/__init__.py | 2 +- mava/utils/environments/RoboCup_env/robocup_agents/__init__.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/mava/utils/environments/RoboCup_env/__init__.py b/mava/utils/environments/RoboCup_env/__init__.py index 06240e71c..f6af6a34e 100644 --- a/mava/utils/environments/RoboCup_env/__init__.py +++ b/mava/utils/environments/RoboCup_env/__init__.py @@ -11,4 +11,4 @@ # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and -# limitations under the License. \ No newline at end of file +# limitations under the License. diff --git a/mava/utils/environments/RoboCup_env/robocup_agents/__init__.py b/mava/utils/environments/RoboCup_env/robocup_agents/__init__.py index 06240e71c..f6af6a34e 100644 --- a/mava/utils/environments/RoboCup_env/robocup_agents/__init__.py +++ b/mava/utils/environments/RoboCup_env/robocup_agents/__init__.py @@ -11,4 +11,4 @@ # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and -# limitations under the License. \ No newline at end of file +# limitations under the License. From 886d9027e727bb557cde075d14ef28c3f3b90014 Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Fri, 4 Jun 2021 16:36:19 +0200 Subject: [PATCH 173/188] update stub --- mava/utils/environments/RoboCup_env/helper.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mava/utils/environments/RoboCup_env/helper.py b/mava/utils/environments/RoboCup_env/helper.py index f982c2917..238fa070a 100644 --- a/mava/utils/environments/RoboCup_env/helper.py +++ b/mava/utils/environments/RoboCup_env/helper.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2018 DeepMind Technologies Limited. All rights reserved. +# Copyright 2021 [...placeholder...]. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. From 8e0556c93c7936af7296f57956f71f879d99426b Mon Sep 17 00:00:00 2001 From: Kale-ab Date: Mon, 7 Jun 2021 11:22:42 +0200 Subject: [PATCH 174/188] feature: Make checkpointing optional. --- mava/systems/tf/dial/system.py | 6 ++++-- mava/systems/tf/maddpg/system.py | 6 ++++-- mava/systems/tf/madqn/system.py | 6 ++++-- mava/systems/tf/mappo/system.py | 6 ++++-- tests/systems/maddpg_system_test.py | 9 --------- tests/systems/madqn_system_test.py | 9 --------- tests/systems/mappo_system_test.py | 9 --------- 7 files changed, 16 insertions(+), 35 deletions(-) diff --git a/mava/systems/tf/dial/system.py b/mava/systems/tf/dial/system.py index 9570ef437..68db5936c 100644 --- a/mava/systems/tf/dial/system.py +++ b/mava/systems/tf/dial/system.py @@ -440,12 +440,14 @@ def evaluator( def build(self, name: str = "madqn") -> Any: """Build the distributed system topology.""" program = lp.Program(name=name) + counter = None with program.group("replay"): replay = program.add_node(lp.ReverbNode(self.replay)) - with program.group("counter"): - counter = program.add_node(lp.CourierNode(self.counter)) + if self._checkpoint: + with program.group("counter"): + counter = program.add_node(lp.CourierNode(self.counter)) if self._max_executor_steps: with program.group("coordinator"): diff --git a/mava/systems/tf/maddpg/system.py b/mava/systems/tf/maddpg/system.py index aa44538fe..ebdd82ed2 100644 --- a/mava/systems/tf/maddpg/system.py +++ b/mava/systems/tf/maddpg/system.py @@ -418,12 +418,14 @@ def evaluator( def build(self, name: str = "maddpg") -> Any: """Build the distributed system topology.""" program = lp.Program(name=name) + counter = None with program.group("replay"): replay = program.add_node(lp.ReverbNode(self.replay)) - with program.group("counter"): - counter = program.add_node(lp.CourierNode(self.counter)) + if self._checkpoint: + with program.group("counter"): + counter = program.add_node(lp.CourierNode(self.counter)) if self._max_executor_steps: with program.group("coordinator"): diff --git a/mava/systems/tf/madqn/system.py b/mava/systems/tf/madqn/system.py index 607affc4d..17d455d27 100644 --- a/mava/systems/tf/madqn/system.py +++ b/mava/systems/tf/madqn/system.py @@ -437,12 +437,14 @@ def evaluator( def build(self, name: str = "madqn") -> Any: """Build the distributed system topology.""" program = lp.Program(name=name) + counter = None with program.group("replay"): replay = program.add_node(lp.ReverbNode(self.replay)) - with program.group("counter"): - counter = program.add_node(lp.CourierNode(self.counter)) + if self._checkpoint: + with program.group("counter"): + counter = program.add_node(lp.CourierNode(self.counter)) if self._max_executor_steps: with program.group("coordinator"): diff --git a/mava/systems/tf/mappo/system.py b/mava/systems/tf/mappo/system.py index 3bbe186bb..b69841344 100644 --- a/mava/systems/tf/mappo/system.py +++ b/mava/systems/tf/mappo/system.py @@ -362,12 +362,14 @@ def evaluator( def build(self, name: str = "mappo") -> Any: """Build the distributed system topology.""" program = lp.Program(name=name) + counter = None with program.group("replay"): replay = program.add_node(lp.ReverbNode(self.replay)) - with program.group("counter"): - counter = program.add_node(lp.CourierNode(self.counter)) + if self._checkpoint: + with program.group("counter"): + counter = program.add_node(lp.CourierNode(self.counter)) if self._max_executor_steps: with program.group("coordinator"): diff --git a/tests/systems/maddpg_system_test.py b/tests/systems/maddpg_system_test.py index e3cd35f1b..2f46781ce 100644 --- a/tests/systems/maddpg_system_test.py +++ b/tests/systems/maddpg_system_test.py @@ -115,12 +115,6 @@ def test_maddpg_on_debugging_env(self) -> None: """Tests that the system can run on the simple spread debugging environment without crashing.""" - # set loggers info - # TODO Allow for no checkpointing and no loggers to be - # passed in. - mava_id = "tests/maddpg" - base_dir = "~/mava" - # environment environment_factory = functools.partial( debugging_utils.make_environment, @@ -132,8 +126,6 @@ def test_maddpg_on_debugging_env(self) -> None: network_factory = lp_utils.partial_kwargs(make_networks) # system - checkpoint_dir = f"{base_dir}/{mava_id}" - system = maddpg.MADDPG( environment_factory=environment_factory, network_factory=network_factory, @@ -144,7 +136,6 @@ def test_maddpg_on_debugging_env(self) -> None: policy_optimizer=snt.optimizers.Adam(learning_rate=1e-4), critic_optimizer=snt.optimizers.Adam(learning_rate=1e-4), checkpoint=False, - checkpoint_subpath=checkpoint_dir, ) program = system.build() diff --git a/tests/systems/madqn_system_test.py b/tests/systems/madqn_system_test.py index 21367e29f..50255eabe 100644 --- a/tests/systems/madqn_system_test.py +++ b/tests/systems/madqn_system_test.py @@ -90,12 +90,6 @@ def test_madqn_on_debugging_env(self) -> None: """Tests that the system can run on the simple spread debugging environment without crashing.""" - # set loggers info - # TODO Allow for no checkpointing and no loggers to be - # passed in. - mava_id = "tests/madqn" - base_dir = "~/mava" - # environment environment_factory = functools.partial( debugging_utils.make_environment, @@ -107,8 +101,6 @@ def test_madqn_on_debugging_env(self) -> None: network_factory = lp_utils.partial_kwargs(make_networks) # system - checkpoint_dir = f"{base_dir}/{mava_id}" - system = madqn.MADQN( environment_factory=environment_factory, network_factory=network_factory, @@ -118,7 +110,6 @@ def test_madqn_on_debugging_env(self) -> None: max_replay_size=1000, optimizer=snt.optimizers.Adam(learning_rate=1e-3), checkpoint=False, - checkpoint_subpath=checkpoint_dir, ) program = system.build() diff --git a/tests/systems/mappo_system_test.py b/tests/systems/mappo_system_test.py index 15c12ba68..ac2b015d6 100644 --- a/tests/systems/mappo_system_test.py +++ b/tests/systems/mappo_system_test.py @@ -126,12 +126,6 @@ def test_mappo_on_debugging_env(self) -> None: """Tests that the system can run on the simple spread debugging environment without crashing.""" - # set loggers info - # TODO Allow for no checkpointing and no loggers to be - # passed in. - mava_id = "tests/mappo" - base_dir = "~/mava" - # environment environment_factory = functools.partial( debugging_utils.make_environment, @@ -143,8 +137,6 @@ def test_mappo_on_debugging_env(self) -> None: network_factory = lp_utils.partial_kwargs(make_networks) # system - checkpoint_dir = f"{base_dir}/{mava_id}" - system = mappo.MAPPO( environment_factory=environment_factory, network_factory=network_factory, @@ -154,7 +146,6 @@ def test_mappo_on_debugging_env(self) -> None: policy_optimizer=snt.optimizers.Adam(learning_rate=1e-3), critic_optimizer=snt.optimizers.Adam(learning_rate=1e-3), checkpoint=False, - checkpoint_subpath=checkpoint_dir, ) program = system.build() From a50fb8fe7420f85c95b27cef20b66057c50c8390 Mon Sep 17 00:00:00 2001 From: Kale-ab Date: Mon, 7 Jun 2021 15:06:03 +0200 Subject: [PATCH 175/188] feat(training): Allow different policy optims per agent. --- mava/systems/replay.py | 2 -- mava/systems/tf/dial/builder.py | 4 +-- mava/systems/tf/dial/system.py | 6 ++-- mava/systems/tf/dial/training.py | 4 +-- mava/systems/tf/mad4pg/system.py | 7 +++- mava/systems/tf/mad4pg/training.py | 18 +++++------ mava/systems/tf/maddpg/builder.py | 2 +- mava/systems/tf/maddpg/system.py | 8 +++-- mava/systems/tf/maddpg/training.py | 52 ++++++++++++++++++------------ mava/systems/tf/madqn/builder.py | 4 +-- mava/systems/tf/madqn/system.py | 4 ++- mava/systems/tf/madqn/training.py | 19 ++++++----- mava/systems/tf/mappo/builder.py | 4 +-- mava/systems/tf/mappo/system.py | 6 ++-- mava/systems/tf/mappo/training.py | 24 ++++++++------ mava/systems/tf/qmix/system.py | 6 ++-- mava/systems/tf/qmix/training.py | 4 +-- mava/systems/tf/vdn/system.py | 6 ++-- mava/systems/tf/vdn/training.py | 4 +-- 19 files changed, 111 insertions(+), 73 deletions(-) diff --git a/mava/systems/replay.py b/mava/systems/replay.py index 5b61a4f45..8d1c4f879 100644 --- a/mava/systems/replay.py +++ b/mava/systems/replay.py @@ -14,5 +14,3 @@ # limitations under the License. """Common tools for reverb replay used for specifically for MARL.""" - -# TODO(Kale-ab): custom replay for MARL systems (we will implement them as we need them) diff --git a/mava/systems/tf/dial/builder.py b/mava/systems/tf/dial/builder.py index 148f90b97..478a98a3a 100644 --- a/mava/systems/tf/dial/builder.py +++ b/mava/systems/tf/dial/builder.py @@ -14,7 +14,7 @@ # limitations under the License. import dataclasses -from typing import Any, Dict, Iterator, List, Optional, Type +from typing import Any, Dict, Iterator, List, Optional, Type, Union import numpy as np import reverb @@ -70,7 +70,7 @@ class DIALConfig: period: int discount: float checkpoint: bool - optimizer: snt.Optimizer + optimizer: Union[snt.Optimizer, Dict[str, snt.Optimizer]] replay_table_name: str = reverb_adders.DEFAULT_PRIORITY_TABLE checkpoint_subpath: str = "~/mava/" diff --git a/mava/systems/tf/dial/system.py b/mava/systems/tf/dial/system.py index 68db5936c..2eb70a5b9 100644 --- a/mava/systems/tf/dial/system.py +++ b/mava/systems/tf/dial/system.py @@ -15,7 +15,7 @@ """Defines the DIAL system class.""" import functools -from typing import Any, Callable, Dict, Optional, Type +from typing import Any, Callable, Dict, Optional, Type, Union import acme import dm_env @@ -111,7 +111,9 @@ def __init__( period: int = 20, max_gradient_norm: float = None, discount: float = 1, - optimizer: snt.Optimizer = snt.optimizers.Adam(learning_rate=1e-4), + optimizer: Union[snt.Optimizer, Dict[str, snt.Optimizer]] = snt.optimizers.Adam( + learning_rate=1e-4 + ), target_update_period: int = 100, executor_variable_update_period: int = 1000, max_executor_steps: int = None, diff --git a/mava/systems/tf/dial/training.py b/mava/systems/tf/dial/training.py index 080650edc..54ed0f8b9 100644 --- a/mava/systems/tf/dial/training.py +++ b/mava/systems/tf/dial/training.py @@ -16,7 +16,7 @@ """DIAL trainer implementation.""" -from typing import Any, Dict, List +from typing import Any, Dict, List, Union import sonnet as snt import tensorflow as tf @@ -48,7 +48,7 @@ def __init__( target_q_networks: Dict[str, snt.Module], target_update_period: int, dataset: tf.data.Dataset, - optimizer: snt.Optimizer, + optimizer: Union[snt.Optimizer, Dict[str, snt.Optimizer]], discount: float, shared_weights: bool, exploration_scheduler: LinearExplorationScheduler, diff --git a/mava/systems/tf/mad4pg/system.py b/mava/systems/tf/mad4pg/system.py index d399f1355..ccf40928b 100644 --- a/mava/systems/tf/mad4pg/system.py +++ b/mava/systems/tf/mad4pg/system.py @@ -65,7 +65,9 @@ def __init__( min_replay_size: int = 1000, max_replay_size: int = 1000000, samples_per_insert: float = 32.0, - policy_optimizer: snt.Optimizer = snt.optimizers.Adam(learning_rate=1e-4), + policy_optimizer: Union[ + snt.Optimizer, Dict[str, snt.Optimizer] + ] = snt.optimizers.Adam(learning_rate=1e-4), critic_optimizer: snt.Optimizer = snt.optimizers.Adam(learning_rate=1e-4), n_step: int = 5, sequence_length: int = 20, @@ -118,6 +120,9 @@ def __init__( replay_table_name: string indicating what name to give the replay table. train_loop_fn: loop for training. eval_loop_fn: loop for evaluation. + policy_optimizer: the optimizer to be applied to the policy loss. + This can be a single optimizer or an optimizer per agent key. + critic_optimizer: the optimizer to be applied to the critic loss. """ super().__init__( environment_factory=environment_factory, diff --git a/mava/systems/tf/mad4pg/training.py b/mava/systems/tf/mad4pg/training.py index 0507ef564..85824c67a 100644 --- a/mava/systems/tf/mad4pg/training.py +++ b/mava/systems/tf/mad4pg/training.py @@ -15,7 +15,7 @@ """MAD4PG trainer implementation.""" -from typing import Any, Dict, List +from typing import Any, Dict, List, Union import sonnet as snt import tensorflow as tf @@ -52,7 +52,7 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizer: snt.Optimizer, + policy_optimizer: Union[snt.Optimizer, Dict[str, snt.Optimizer]], critic_optimizer: snt.Optimizer, discount: float, target_averaging: bool, @@ -203,7 +203,7 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizer: snt.Optimizer, + policy_optimizer: Union[snt.Optimizer, Dict[str, snt.Optimizer]], critic_optimizer: snt.Optimizer, discount: float, target_averaging: bool, @@ -281,7 +281,7 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizer: snt.Optimizer, + policy_optimizer: Union[snt.Optimizer, Dict[str, snt.Optimizer]], critic_optimizer: snt.Optimizer, discount: float, target_averaging: bool, @@ -359,7 +359,7 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizer: snt.Optimizer, + policy_optimizer: Union[snt.Optimizer, Dict[str, snt.Optimizer]], critic_optimizer: snt.Optimizer, discount: float, target_averaging: bool, @@ -437,7 +437,7 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizer: snt.Optimizer, + policy_optimizer: Union[snt.Optimizer, Dict[str, snt.Optimizer]], critic_optimizer: snt.Optimizer, discount: float, target_averaging: bool, @@ -652,7 +652,7 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizer: snt.Optimizer, + policy_optimizer: Union[snt.Optimizer, Dict[str, snt.Optimizer]], critic_optimizer: snt.Optimizer, discount: float, target_averaging: bool, @@ -734,7 +734,7 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizer: snt.Optimizer, + policy_optimizer: Union[snt.Optimizer, Dict[str, snt.Optimizer]], critic_optimizer: snt.Optimizer, discount: float, target_averaging: bool, @@ -816,7 +816,7 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizer: snt.Optimizer, + policy_optimizer: Union[snt.Optimizer, Dict[str, snt.Optimizer]], critic_optimizer: snt.Optimizer, discount: float, target_averaging: bool, diff --git a/mava/systems/tf/maddpg/builder.py b/mava/systems/tf/maddpg/builder.py index 1388d3ffa..22f8efd9a 100644 --- a/mava/systems/tf/maddpg/builder.py +++ b/mava/systems/tf/maddpg/builder.py @@ -66,7 +66,7 @@ class MADDPGConfig: replay_table_name: string indicating what name to give the replay table.""" environment_spec: specs.MAEnvironmentSpec - policy_optimizer: snt.Optimizer + policy_optimizer: Union[snt.Optimizer, Dict[str, snt.Optimizer]] critic_optimizer: snt.Optimizer shared_weights: bool = True discount: float = 0.99 diff --git a/mava/systems/tf/maddpg/system.py b/mava/systems/tf/maddpg/system.py index ebdd82ed2..0750e8070 100644 --- a/mava/systems/tf/maddpg/system.py +++ b/mava/systems/tf/maddpg/system.py @@ -75,7 +75,9 @@ def __init__( min_replay_size: int = 1000, max_replay_size: int = 1000000, samples_per_insert: float = 32.0, - policy_optimizer: snt.Optimizer = snt.optimizers.Adam(learning_rate=1e-4), + policy_optimizer: Union[ + snt.Optimizer, Dict[str, snt.Optimizer] + ] = snt.optimizers.Adam(learning_rate=1e-4), critic_optimizer: snt.Optimizer = snt.optimizers.Adam(learning_rate=1e-4), n_step: int = 5, sequence_length: int = 20, @@ -128,7 +130,9 @@ def __init__( replay_table_name: string indicating what name to give the replay table. train_loop_fn: loop for training. eval_loop_fn: loop for evaluation. - + policy_optimizer: the optimizer to be applied to the policy loss. + This can be a single optimizer or an optimizer per agent key. + critic_optimizer: the optimizer to be applied to the critic loss. """ if not environment_spec: diff --git a/mava/systems/tf/maddpg/training.py b/mava/systems/tf/maddpg/training.py index 139fba243..1605557a6 100644 --- a/mava/systems/tf/maddpg/training.py +++ b/mava/systems/tf/maddpg/training.py @@ -19,7 +19,7 @@ import copy import os import time -from typing import Any, Dict, List, Optional, Sequence, Tuple +from typing import Any, Dict, List, Optional, Sequence, Tuple, Union import numpy as np import reverb @@ -51,8 +51,8 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizer: snt.Optimizer, - critic_optimizer: snt.Optimizer, + policy_optimizer: Union[snt.Optimizer, Dict[str, snt.Optimizer]], + critic_optimizer: Union[snt.Optimizer, Dict[str, snt.Optimizer]], discount: float, target_averaging: bool, target_update_period: int, @@ -87,6 +87,7 @@ def __init__( before the policy and the critic. target_observation_network: the target observation network. policy_optimizer: the optimizer to be applied to the DPG (policy) loss. + This can be a single optimizer or an optimizer per agent key. critic_optimizer: the optimizer to be applied to the critic loss. clipping: whether to clip gradients by global norm. counter: counter object used to keep track of steps. @@ -144,11 +145,16 @@ def __init__( self.unique_net_keys = self._agent_types if shared_weights else self._agents # Create optimizers for different agent types. - # TODO(Kale-ab): Allow this to be passed as a system param. - self._policy_optimizers: snt.Optimizer = {} - self._critic_optimizers: snt.Optimizer = {} + # If dict with optimizers passed in, use that. + if not isinstance(policy_optimizer, dict): + self._policy_optimizers: Dict[str, snt.Optimizer] = {} + for agent in self.unique_net_keys: + self._policy_optimizers[agent] = copy.deepcopy(policy_optimizer) + else: + self._policy_optimizers = policy_optimizer + + self._critic_optimizers: Dict[str, snt.Optimizer] = {} for agent in self.unique_net_keys: - self._policy_optimizers[agent] = copy.deepcopy(policy_optimizer) self._critic_optimizers[agent] = copy.deepcopy(critic_optimizer) # Expose the variables. @@ -468,7 +474,7 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizer: snt.Optimizer, + policy_optimizer: Union[snt.Optimizer, Dict[str, snt.Optimizer]], critic_optimizer: snt.Optimizer, discount: float, target_averaging: bool, @@ -546,7 +552,7 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizer: snt.Optimizer, + policy_optimizer: Union[snt.Optimizer, Dict[str, snt.Optimizer]], critic_optimizer: snt.Optimizer, discount: float, target_averaging: bool, @@ -667,7 +673,7 @@ def __init__( target_update_period: int, target_update_rate: float, dataset: tf.data.Dataset, - policy_optimizer: snt.Optimizer, + policy_optimizer: Union[snt.Optimizer, Dict[str, snt.Optimizer]], critic_optimizer: snt.Optimizer, observation_networks: Dict[str, snt.Module], target_observation_networks: Dict[str, snt.Module], @@ -797,7 +803,7 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizer: snt.Optimizer, + policy_optimizer: Union[snt.Optimizer, Dict[str, snt.Optimizer]], critic_optimizer: snt.Optimizer, discount: float, target_averaging: bool, @@ -912,8 +918,8 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizer: snt.Optimizer, - critic_optimizer: snt.Optimizer, + policy_optimizer: Union[snt.Optimizer, Dict[str, snt.Optimizer]], + critic_optimizer: Union[snt.Optimizer, Dict[str, snt.Optimizer]], discount: float, target_averaging: bool, target_update_period: int, @@ -945,6 +951,7 @@ def __init__( before the policy and the critic. target_observation_network: the target observation network. policy_optimizer: the optimizer to be applied to the DPG (policy) loss. + This can be a single optimizer or an optimizer per agent key. critic_optimizer: the optimizer to be applied to the critic loss. clipping: whether to clip gradients by global norm. counter: counter object used to keep track of steps. @@ -1003,11 +1010,16 @@ def __init__( self.unique_net_keys = self._agent_types if shared_weights else self._agents # Create optimizers for different agent types. - # TODO(Kale-ab): Allow this to be passed as a system param. - self._policy_optimizers: snt.Optimizer = {} - self._critic_optimizers: snt.Optimizer = {} + # If dict with optimizers passed in, use that. + if not isinstance(policy_optimizer, dict): + self._policy_optimizers: Dict[str, snt.Optimizer] = {} + for agent in self.unique_net_keys: + self._policy_optimizers[agent] = copy.deepcopy(policy_optimizer) + else: + self._policy_optimizers = policy_optimizer + + self._critic_optimizers: Dict[str, snt.Optimizer] = {} for agent in self.unique_net_keys: - self._policy_optimizers[agent] = copy.deepcopy(policy_optimizer) self._critic_optimizers[agent] = copy.deepcopy(critic_optimizer) # Expose the variables. @@ -1417,7 +1429,7 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizer: snt.Optimizer, + policy_optimizer: Union[snt.Optimizer, Dict[str, snt.Optimizer]], critic_optimizer: snt.Optimizer, discount: float, target_averaging: bool, @@ -1497,7 +1509,7 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizer: snt.Optimizer, + policy_optimizer: Union[snt.Optimizer, Dict[str, snt.Optimizer]], critic_optimizer: snt.Optimizer, discount: float, target_averaging: bool, @@ -1615,7 +1627,7 @@ def __init__( critic_networks: Dict[str, snt.Module], target_policy_networks: Dict[str, snt.Module], target_critic_networks: Dict[str, snt.Module], - policy_optimizer: snt.Optimizer, + policy_optimizer: Union[snt.Optimizer, Dict[str, snt.Optimizer]], critic_optimizer: snt.Optimizer, discount: float, target_averaging: bool, diff --git a/mava/systems/tf/madqn/builder.py b/mava/systems/tf/madqn/builder.py index ffa9604c5..2abcf56dc 100644 --- a/mava/systems/tf/madqn/builder.py +++ b/mava/systems/tf/madqn/builder.py @@ -14,7 +14,7 @@ # limitations under the License. import dataclasses -from typing import Any, Dict, Iterator, List, Optional, Type +from typing import Any, Dict, Iterator, List, Optional, Type, Union import numpy as np import reverb @@ -71,7 +71,7 @@ class MADQNConfig: period: int discount: float checkpoint: bool - optimizer: snt.Optimizer + optimizer: Union[snt.Optimizer, Dict[str, snt.Optimizer]] replay_table_name: str = reverb_adders.DEFAULT_PRIORITY_TABLE checkpoint_subpath: str = "~/mava/" diff --git a/mava/systems/tf/madqn/system.py b/mava/systems/tf/madqn/system.py index 17d455d27..bf5d43109 100644 --- a/mava/systems/tf/madqn/system.py +++ b/mava/systems/tf/madqn/system.py @@ -108,7 +108,9 @@ def __init__( clipping: bool = True, max_gradient_norm: float = None, discount: float = 0.99, - optimizer: snt.Optimizer = snt.optimizers.Adam(learning_rate=1e-4), + optimizer: Union[snt.Optimizer, Dict[str, snt.Optimizer]] = snt.optimizers.Adam( + learning_rate=1e-4 + ), target_update_period: int = 100, executor_variable_update_period: int = 1000, max_executor_steps: int = None, diff --git a/mava/systems/tf/madqn/training.py b/mava/systems/tf/madqn/training.py index ab971894b..9a54a049f 100644 --- a/mava/systems/tf/madqn/training.py +++ b/mava/systems/tf/madqn/training.py @@ -15,7 +15,7 @@ import copy import time -from typing import Any, Dict, List, Optional, Sequence, Tuple +from typing import Any, Dict, List, Optional, Sequence, Tuple, Union import numpy as np import sonnet as snt @@ -49,7 +49,7 @@ def __init__( target_q_networks: Dict[str, snt.Module], target_update_period: int, dataset: tf.data.Dataset, - optimizer: snt.Optimizer, + optimizer: Union[Dict[str, snt.Optimizer], snt.Optimizer], discount: float, shared_weights: bool, exploration_scheduler: LinearExplorationScheduler, @@ -103,10 +103,13 @@ def __init__( self.unique_net_keys = self._agent_types if shared_weights else self._agents # Create optimizers for different agent types. - # TODO(Kale-ab): Allow this to be passed as a system param. - self._optimizers: snt.Optimizer = {} - for agent in self.unique_net_keys: - self._optimizers[agent] = copy.deepcopy(optimizer) + # If dict with optimizers passed in, use that. + if not isinstance(optimizer, dict): + self._optimizers: Dict[str, snt.Optimizer] = {} + for agent in self.unique_net_keys: + self._optimizers[agent] = copy.deepcopy(optimizer) + else: + self._optimizers = optimizer # Expose the variables. q_networks_to_expose = {} @@ -340,7 +343,7 @@ def __init__( target_q_networks: Dict[str, snt.Module], target_update_period: int, dataset: tf.data.Dataset, - optimizer: snt.Optimizer, + optimizer: Union[snt.Optimizer, Dict[str, snt.Optimizer]], discount: float, shared_weights: bool, exploration_scheduler: LinearExplorationScheduler, @@ -435,7 +438,7 @@ def __init__( target_q_networks: Dict[str, snt.Module], target_update_period: int, dataset: tf.data.Dataset, - optimizer: snt.Optimizer, + optimizer: Union[snt.Optimizer, Dict[str, snt.Optimizer]], discount: float, shared_weights: bool, exploration_scheduler: LinearExplorationScheduler, diff --git a/mava/systems/tf/mappo/builder.py b/mava/systems/tf/mappo/builder.py index 431ecdeee..ee413f360 100644 --- a/mava/systems/tf/mappo/builder.py +++ b/mava/systems/tf/mappo/builder.py @@ -15,7 +15,7 @@ """MAPPO builder and config.""" import dataclasses -from typing import Dict, Iterator, List, Optional, Type +from typing import Dict, Iterator, List, Optional, Type, Union import reverb import sonnet as snt @@ -62,7 +62,7 @@ class MAPPOConfig: """ environment_spec: specs.EnvironmentSpec - policy_optimizer: snt.Optimizer + policy_optimizer: Union[snt.Optimizer, Dict[str, snt.Optimizer]] critic_optimizer: snt.Optimizer sequence_length: int = 10 sequence_period: int = 5 diff --git a/mava/systems/tf/mappo/system.py b/mava/systems/tf/mappo/system.py index b69841344..a5e81691c 100644 --- a/mava/systems/tf/mappo/system.py +++ b/mava/systems/tf/mappo/system.py @@ -15,7 +15,7 @@ """MAPPO system implementation.""" import functools -from typing import Any, Callable, Dict, Optional, Type +from typing import Any, Callable, Dict, Optional, Type, Union import acme import dm_env @@ -60,7 +60,9 @@ def __init__( environment_spec: mava_specs.MAEnvironmentSpec = None, shared_weights: bool = True, executor_variable_update_period: int = 100, - policy_optimizer: snt.Optimizer = snt.optimizers.Adam(learning_rate=5e-4), + policy_optimizer: Union[ + snt.Optimizer, Dict[str, snt.Optimizer] + ] = snt.optimizers.Adam(learning_rate=5e-4), critic_optimizer: snt.Optimizer = snt.optimizers.Adam(learning_rate=1e-5), discount: float = 0.99, lambda_gae: float = 0.99, diff --git a/mava/systems/tf/mappo/training.py b/mava/systems/tf/mappo/training.py index 793600218..6d03fd2f1 100644 --- a/mava/systems/tf/mappo/training.py +++ b/mava/systems/tf/mappo/training.py @@ -17,7 +17,7 @@ import copy import os import time -from typing import Any, Dict, List, Optional, Sequence +from typing import Any, Dict, List, Optional, Sequence, Union import numpy as np import sonnet as snt @@ -48,8 +48,8 @@ def __init__( policy_networks: Dict[str, snt.Module], critic_networks: Dict[str, snt.Module], dataset: tf.data.Dataset, - policy_optimizer: snt.Optimizer, - critic_optimizer: snt.Optimizer, + policy_optimizer: Union[snt.Optimizer, Dict[str, snt.Optimizer]], + critic_optimizer: Union[snt.Optimizer, Dict[str, snt.Optimizer]], shared_weights: bool, discount: float = 0.99, lambda_gae: float = 1.0, @@ -67,6 +67,7 @@ def __init__( policy_networks: ... critic_networks: ... policy_optimizer: policy optimizer, + This can be a single optimizer or an optimizer per agent key. critic_optimizer: critic optimizer, shared_weights: ... discount: discount to use for TD updates. @@ -104,11 +105,16 @@ def __init__( self.unique_net_keys = self._agent_types if shared_weights else self._agents # Create optimizers for different agent types. - # TODO(Kale-ab): Allow this to be passed as a system param. - self._policy_optimizers: snt.Optimizer = {} - self._critic_optimizers: snt.Optimizer = {} + # If dict with optimizers passed in, use that. + if not isinstance(policy_optimizer, dict): + self._policy_optimizers: Dict[str, snt.Optimizer] = {} + for agent in self.unique_net_keys: + self._policy_optimizers[agent] = copy.deepcopy(policy_optimizer) + else: + self._policy_optimizers = policy_optimizer + + self._critic_optimizers: Dict[str, snt.Optimizer] = {} for agent in self.unique_net_keys: - self._policy_optimizers[agent] = copy.deepcopy(policy_optimizer) self._critic_optimizers[agent] = copy.deepcopy(critic_optimizer) # Expose the variables. @@ -417,8 +423,8 @@ def __init__( policy_networks: Dict[str, snt.Module], critic_networks: Dict[str, snt.Module], dataset: tf.data.Dataset, - policy_optimizer: snt.Optimizer, - critic_optimizer: snt.Optimizer, + policy_optimizer: Union[snt.Optimizer, Dict[str, snt.Optimizer]], + critic_optimizer: Union[snt.Optimizer, Dict[str, snt.Optimizer]], shared_weights: bool, discount: float = 0.99, lambda_gae: float = 1.0, diff --git a/mava/systems/tf/qmix/system.py b/mava/systems/tf/qmix/system.py index c5952c9f0..f79891b99 100644 --- a/mava/systems/tf/qmix/system.py +++ b/mava/systems/tf/qmix/system.py @@ -15,7 +15,7 @@ """Defines the QMIX system class.""" import functools -from typing import Any, Callable, Dict, Optional, Type +from typing import Any, Callable, Dict, Optional, Type, Union import dm_env import reverb @@ -100,7 +100,9 @@ def __init__( period: int = 20, max_gradient_norm: float = None, discount: float = 0.99, - optimizer: snt.Optimizer = snt.optimizers.Adam(learning_rate=1e-4), + optimizer: Union[snt.Optimizer, Dict[str, snt.Optimizer]] = snt.optimizers.Adam( + learning_rate=1e-4 + ), target_update_period: int = 100, executor_variable_update_period: int = 1000, max_executor_steps: int = None, diff --git a/mava/systems/tf/qmix/training.py b/mava/systems/tf/qmix/training.py index c06e0024b..f5cfd55c8 100644 --- a/mava/systems/tf/qmix/training.py +++ b/mava/systems/tf/qmix/training.py @@ -15,7 +15,7 @@ """Qmix trainer implementation.""" -from typing import Any, Dict, List, Sequence +from typing import Any, Dict, List, Sequence, Union import numpy as np import sonnet as snt @@ -49,7 +49,7 @@ def __init__( target_mixing_network: snt.Module, target_update_period: int, dataset: tf.data.Dataset, - optimizer: snt.Optimizer, + optimizer: Union[snt.Optimizer, Dict[str, snt.Optimizer]], discount: float, shared_weights: bool, exploration_scheduler: LinearExplorationScheduler, diff --git a/mava/systems/tf/vdn/system.py b/mava/systems/tf/vdn/system.py index ff53125f0..c680b2832 100644 --- a/mava/systems/tf/vdn/system.py +++ b/mava/systems/tf/vdn/system.py @@ -15,7 +15,7 @@ """Defines the VDN system class.""" import functools -from typing import Any, Callable, Dict, Optional, Type +from typing import Any, Callable, Dict, Optional, Type, Union import dm_env import reverb @@ -100,7 +100,9 @@ def __init__( period: int = 20, max_gradient_norm: float = None, discount: float = 0.99, - optimizer: snt.Optimizer = snt.optimizers.Adam(learning_rate=1e-4), + optimizer: Union[snt.Optimizer, Dict[str, snt.Optimizer]] = snt.optimizers.Adam( + learning_rate=1e-4 + ), target_update_period: int = 100, executor_variable_update_period: int = 1000, max_executor_steps: int = None, diff --git a/mava/systems/tf/vdn/training.py b/mava/systems/tf/vdn/training.py index 232468f68..c6d803a5e 100644 --- a/mava/systems/tf/vdn/training.py +++ b/mava/systems/tf/vdn/training.py @@ -15,7 +15,7 @@ """VDN trainer implementation.""" -from typing import Any, Dict, List +from typing import Any, Dict, List, Union import sonnet as snt import tensorflow as tf @@ -45,7 +45,7 @@ def __init__( target_mixing_network: snt.Module, target_update_period: int, dataset: tf.data.Dataset, - optimizer: snt.Optimizer, + optimizer: Union[snt.Optimizer, Dict[str, snt.Optimizer]], discount: float, shared_weights: bool, exploration_scheduler: LinearExplorationScheduler, From bd3a8dca86f9a02f2514e3e7f721dd819a863d19 Mon Sep 17 00:00:00 2001 From: Kale-ab Date: Mon, 7 Jun 2021 16:29:33 +0200 Subject: [PATCH 176/188] feat(wrapper): Cleaned up PZ wrapper. --- mava/utils/environments/pettingzoo_utils.py | 3 +- mava/wrappers/pettingzoo.py | 41 ++++++++++----------- tests/mocks.py | 16 ++++++-- 3 files changed, 33 insertions(+), 27 deletions(-) diff --git a/mava/utils/environments/pettingzoo_utils.py b/mava/utils/environments/pettingzoo_utils.py index a972e933e..6a4f0e9ae 100644 --- a/mava/utils/environments/pettingzoo_utils.py +++ b/mava/utils/environments/pettingzoo_utils.py @@ -21,6 +21,7 @@ import dm_env import numpy as np import supersuit +from supersuit import black_death_v1 from mava.wrappers import ( ParallelEnvWrapper, @@ -63,7 +64,7 @@ def make_environment( env_type: str = "parallel", env_class: str = "mpe", env_name: str = "simple_spread_v2", - env_preprocess_wrappers: Optional[List] = None, + env_preprocess_wrappers: Optional[List] = [(black_death_v1, None)], random_seed: Optional[int] = None, **kwargs: Any, ) -> dm_env.Environment: diff --git a/mava/wrappers/pettingzoo.py b/mava/wrappers/pettingzoo.py index 1671fd3ea..6a1abde8f 100644 --- a/mava/wrappers/pettingzoo.py +++ b/mava/wrappers/pettingzoo.py @@ -29,12 +29,6 @@ convert_np_type, parameterized_restart, ) -from mava.wrappers.env_preprocess_wrappers import ( - StandardizeObservationParallel, - StandardizeObservationSequential, - StandardizeRewardParallel, - StandardizeRewardSequential, -) from mava.wrappers.env_wrappers import ParallelEnvWrapper, SequentialEnvWrapper @@ -48,8 +42,6 @@ def __init__( env_preprocess_wrappers: Optional[List] = [ # (env_preprocessor, dict_with_preprocessor_params) (black_death_v1, None), - (StandardizeObservationSequential, None), - (StandardizeRewardSequential, None), ], ): self._environment = environment @@ -139,7 +131,7 @@ def _convert_observation( def observation_spec(self) -> types.Observation: observation_specs = {} - for agent in self._environment.possible_agents: + for agent in self.possible_agents: observation_specs[agent] = types.OLT( observation=_convert_to_spec( self._environment.observation_spaces[agent] @@ -151,7 +143,7 @@ def observation_spec(self) -> types.Observation: def action_spec(self) -> Dict[str, specs.DiscreteArray]: action_specs = {} - for agent in self._environment.possible_agents: + for agent in self.possible_agents: action_specs[agent] = _convert_to_spec( self._environment.action_spaces[agent] ) @@ -159,14 +151,14 @@ def action_spec(self) -> Dict[str, specs.DiscreteArray]: def reward_spec(self) -> Dict[str, specs.Array]: reward_specs = {} - for agent in self._environment.possible_agents: + for agent in self.possible_agents: reward_specs[agent] = specs.Array((), np.float32) return reward_specs def discount_spec(self) -> Dict[str, specs.BoundedArray]: discount_specs = {} - for agent in self._environment.possible_agents: + for agent in self.possible_agents: discount_specs[agent] = specs.BoundedArray( (), np.float32, minimum=0, maximum=1.0 ) @@ -194,7 +186,10 @@ def current_agent(self) -> Any: def __getattr__(self, name: str) -> Any: """Expose any other attributes of the underlying environment.""" - return getattr(self._environment, name) + if hasattr(self.__class__, name): + return self.__getattribute__(name) + else: + return getattr(self._environment, name) class PettingZooParallelEnvWrapper(ParallelEnvWrapper): @@ -206,8 +201,6 @@ def __init__( env_preprocess_wrappers: Optional[List] = [ # (env_preprocessor, dict_with_preprocessor_params) (black_death_v1, None), - (StandardizeObservationParallel, None), - (StandardizeRewardParallel, None), ], ): self._environment = environment @@ -223,11 +216,12 @@ def reset(self) -> dm_env.TimeStep: self._reset_next_step = False self._step_type = dm_env.StepType.FIRST discount_spec = self.discount_spec() + observe = self._environment.reset() + self._discounts = { agent: convert_np_type(discount_spec[agent].dtype, 1) - for agent in self._environment.possible_agents + for agent in self.possible_agents } - observe = self._environment.reset() if type(observe) == tuple: observe, env_extras = observe @@ -313,7 +307,7 @@ def _convert_observations( def observation_spec(self) -> types.Observation: observation_specs = {} - for agent in self._environment.possible_agents: + for agent in self.possible_agents: observation_specs[agent] = types.OLT( observation=_convert_to_spec( self._environment.observation_spaces[agent] @@ -326,20 +320,20 @@ def observation_spec(self) -> types.Observation: def action_spec(self) -> Dict[str, Union[specs.DiscreteArray, specs.BoundedArray]]: action_specs = {} action_spaces = self._environment.action_spaces - for agent in self._environment.possible_agents: + for agent in self.possible_agents: action_specs[agent] = _convert_to_spec(action_spaces[agent]) return action_specs def reward_spec(self) -> Dict[str, specs.Array]: reward_specs = {} - for agent in self._environment.possible_agents: + for agent in self.possible_agents: reward_specs[agent] = specs.Array((), np.float32) return reward_specs def discount_spec(self) -> Dict[str, specs.BoundedArray]: discount_specs = {} - for agent in self._environment.possible_agents: + for agent in self.possible_agents: discount_specs[agent] = specs.BoundedArray( (), np.float32, minimum=0, maximum=1.0 ) @@ -367,4 +361,7 @@ def current_agent(self) -> Any: def __getattr__(self, name: str) -> Any: """Expose any other attributes of the underlying environment.""" - return getattr(self._environment, name) + if hasattr(self.__class__, name): + return self.__getattribute__(name) + else: + return getattr(self._environment, name) diff --git a/tests/mocks.py b/tests/mocks.py index 08d6b42d0..283e24a45 100644 --- a/tests/mocks.py +++ b/tests/mocks.py @@ -77,10 +77,18 @@ def observe( ) -> None: for agent, observation_spec in self._spec.items(): - _validate_spec(observation_spec.actions, action[agent]) - _validate_spec(observation_spec.rewards, next_timestep.reward[agent]) - _validate_spec(observation_spec.discounts, next_timestep.discount[agent]) - if next_timestep.observation: + if agent in action.keys(): + _validate_spec(observation_spec.actions, action[agent]) + + if agent in next_timestep.reward.keys(): + _validate_spec(observation_spec.rewards, next_timestep.reward[agent]) + + if agent in next_timestep.discount.keys(): + _validate_spec( + observation_spec.discounts, next_timestep.discount[agent] + ) + + if next_timestep.observation and agent in next_timestep.observation.keys(): _validate_spec( observation_spec.observations, next_timestep.observation[agent] ) From 362b93e982995043edeb9dd342df3830a9a670ba Mon Sep 17 00:00:00 2001 From: Kale-ab Date: Mon, 7 Jun 2021 17:14:58 +0200 Subject: [PATCH 177/188] bugfix: Reverted optim change in qmix. --- mava/systems/tf/qmix/training.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/mava/systems/tf/qmix/training.py b/mava/systems/tf/qmix/training.py index fc1ebd845..967403663 100644 --- a/mava/systems/tf/qmix/training.py +++ b/mava/systems/tf/qmix/training.py @@ -15,7 +15,7 @@ """Qmix trainer implementation.""" import time -from typing import Any, Dict, List, Optional, Sequence, Union +from typing import Any, Dict, List, Optional, Sequence import numpy as np import sonnet as snt @@ -52,7 +52,7 @@ def __init__( target_mixing_network: snt.Module, target_update_period: int, dataset: tf.data.Dataset, - optimizer: Union[snt.Optimizer, Dict[str, snt.Optimizer]], + optimizer: snt.Optimizer, discount: float, shared_weights: bool, exploration_scheduler: LinearExplorationScheduler, @@ -254,8 +254,7 @@ def _backward(self) -> None: ] gradients = self.tape.gradient(self.loss, variables) gradients = tf.clip_by_global_norm(gradients, self._max_gradient_norm)[0] - # TODO We need to use correct optim for mixing - # self._optimizer.apply(gradients, variables) + self._optimizer.apply(gradients, variables) train_utils.safe_del(self, "tape") From 5fa28ef32d4e7f8c45841b50a9aea4d1bb5d30af Mon Sep 17 00:00:00 2001 From: Kale-ab Date: Mon, 7 Jun 2021 17:20:21 +0200 Subject: [PATCH 178/188] chore: Cleaned up comments. --- mava/systems/tf/maddpg/training.py | 2 -- mava/systems/tf/madqn/training.py | 1 - mava/systems/tf/mappo/training.py | 1 - 3 files changed, 4 deletions(-) diff --git a/mava/systems/tf/maddpg/training.py b/mava/systems/tf/maddpg/training.py index fad52114b..d6a320200 100644 --- a/mava/systems/tf/maddpg/training.py +++ b/mava/systems/tf/maddpg/training.py @@ -147,7 +147,6 @@ def __init__( self.unique_net_keys = self._agent_types if shared_weights else self._agents # Create optimizers for different agent types. - # If dict with optimizers passed in, use that. if not isinstance(policy_optimizer, dict): self._policy_optimizers: Dict[str, snt.Optimizer] = {} for agent in self.unique_net_keys: @@ -1012,7 +1011,6 @@ def __init__( self.unique_net_keys = self._agent_types if shared_weights else self._agents # Create optimizers for different agent types. - # If dict with optimizers passed in, use that. if not isinstance(policy_optimizer, dict): self._policy_optimizers: Dict[str, snt.Optimizer] = {} for agent in self.unique_net_keys: diff --git a/mava/systems/tf/madqn/training.py b/mava/systems/tf/madqn/training.py index 27ecde6b3..92a405c75 100644 --- a/mava/systems/tf/madqn/training.py +++ b/mava/systems/tf/madqn/training.py @@ -105,7 +105,6 @@ def __init__( self.unique_net_keys = self._agent_types if shared_weights else self._agents # Create optimizers for different agent types. - # If dict with optimizers passed in, use that. if not isinstance(optimizer, dict): self._optimizers: Dict[str, snt.Optimizer] = {} for agent in self.unique_net_keys: diff --git a/mava/systems/tf/mappo/training.py b/mava/systems/tf/mappo/training.py index 768523342..4853ce178 100644 --- a/mava/systems/tf/mappo/training.py +++ b/mava/systems/tf/mappo/training.py @@ -107,7 +107,6 @@ def __init__( self.unique_net_keys = self._agent_types if shared_weights else self._agents # Create optimizers for different agent types. - # If dict with optimizers passed in, use that. if not isinstance(policy_optimizer, dict): self._policy_optimizers: Dict[str, snt.Optimizer] = {} for agent in self.unique_net_keys: From a970bd3cd1c28c7674e5e748bff6239fb11531fe Mon Sep 17 00:00:00 2001 From: Kale-ab Date: Tue, 8 Jun 2021 09:08:53 +0200 Subject: [PATCH 179/188] bugfix: Only checkpoint if checkpointer is not none. --- mava/wrappers/system_trainer_statistics.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/mava/wrappers/system_trainer_statistics.py b/mava/wrappers/system_trainer_statistics.py index 9e1ba6fe4..9c3d807b7 100644 --- a/mava/wrappers/system_trainer_statistics.py +++ b/mava/wrappers/system_trainer_statistics.py @@ -78,7 +78,8 @@ def step(self) -> None: counts = self._counter.increment(steps=1, walltime=elapsed_time) fetches.update(counts) - train_utils.checkpoint_networks(self._system_checkpointer) + if self._system_checkpointer: + train_utils.checkpoint_networks(self._system_checkpointer) if self._logger: self._logger.write(fetches) @@ -184,7 +185,8 @@ def step(self) -> None: counts = self._counter.increment(steps=1, walltime=elapsed_time) fetches.update(counts) - train_utils.checkpoint_networks(self._system_checkpointer) + if self._system_checkpointer: + train_utils.checkpoint_networks(self._system_checkpointer) fetches["epsilon"] = self.get_epsilon() self._trainer._decrement_epsilon() # type: ignore @@ -333,7 +335,8 @@ def step(self) -> None: counts = self._counter.increment(steps=1, walltime=elapsed_time) fetches.update(counts) - train_utils.checkpoint_networks(self._system_checkpointer) + if self._system_checkpointer: + train_utils.checkpoint_networks(self._system_checkpointer) if self._logger: self._logger.write(fetches) From 0a47cbde67e3a12566df2d38cff655991648249b Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Tue, 8 Jun 2021 13:24:26 +0200 Subject: [PATCH 180/188] update copyright to InstaDeep Ltd --- LICENSE | 2 +- examples/debugging_envs/__init__.py | 2 +- .../debugging_envs/run_centralised_feedforward_maddpg.py | 2 +- .../debugging_envs/run_centralised_feedforward_mappo.py | 2 +- .../debugging_envs/run_decentralised_feedforward_mad4pg.py | 2 +- .../run_decentralised_feedforward_mad4pg_discrete.py | 2 +- .../debugging_envs/run_decentralised_feedforward_maddpg.py | 2 +- .../run_decentralised_feedforward_maddpg_discrete.py | 2 +- .../debugging_envs/run_decentralised_feedforward_mappo.py | 2 +- .../debugging_envs/run_decentralised_recurrent_mad4pg.py | 2 +- .../debugging_envs/run_decentralised_recurrent_maddpg.py | 2 +- .../debugging_envs/run_feedforward_maddpg_record_video.py | 2 +- examples/debugging_envs/run_feedforward_madqn.py | 2 +- .../run_feedforward_madqn_with_fingerprints.py | 2 +- examples/debugging_envs/run_feedforward_qmix.py | 2 +- examples/debugging_envs/run_feedforward_vdn.py | 2 +- examples/debugging_envs/run_networked_feedforward_maddpg.py | 2 +- examples/debugging_envs/run_recurrent_comm_madqn.py | 2 +- examples/debugging_envs/run_recurrent_dial.py | 2 +- examples/debugging_envs/run_recurrent_madqn.py | 2 +- .../debugging_envs/run_state_based_feedforward_maddpg.py | 2 +- examples/debugging_envs/run_state_based_recurrent_maddpg.py | 2 +- examples/flatland/run_madqn.py | 2 +- examples/petting_zoo/__init__.py | 2 +- examples/petting_zoo/run_centralised_feedforward_mappo.py | 2 +- .../run_decentralised_feedforward_mad4pg_continuous.py | 2 +- .../run_decentralised_feedforward_mad4pg_discrete.py | 2 +- .../run_decentralised_feedforward_maddpg_continuous.py | 2 +- .../run_decentralised_feedforward_maddpg_discrete.py | 2 +- examples/petting_zoo/run_decentralised_feedforward_madqn.py | 2 +- examples/petting_zoo/run_decentralised_feedforward_mappo.py | 2 +- examples/petting_zoo/run_decentralised_recurrent_maddpg.py | 2 +- examples/petting_zoo/run_feedforward_maddpg_record_video.py | 2 +- .../petting_zoo/run_feedforward_madqn_with_fingerprints.py | 2 +- examples/petting_zoo/run_feedforward_qmix.py | 2 +- examples/petting_zoo/run_feedforward_vdn.py | 2 +- .../petting_zoo/run_networked_feedforward_maddpg_sparse.py | 2 +- examples/smac/run_feedforward_madqn.py | 2 +- examples/smac/run_recurrent_madqn.py | 2 +- mava/_metadata.py | 2 +- mava/adders/__init__.py | 2 +- mava/adders/base.py | 2 +- mava/adders/reverb/__init__.py | 2 +- mava/adders/reverb/base.py | 2 +- mava/adders/reverb/episode.py | 2 +- mava/adders/reverb/sequence.py | 2 +- mava/adders/reverb/transition.py | 2 +- mava/adders/reverb/utils.py | 2 +- mava/components/__init__.py | 2 +- mava/components/tf/__init__.py | 2 +- mava/components/tf/architectures/__init__.py | 2 +- mava/components/tf/architectures/base.py | 2 +- mava/components/tf/architectures/centralised.py | 2 +- mava/components/tf/architectures/decentralised.py | 2 +- mava/components/tf/architectures/networked.py | 2 +- mava/components/tf/architectures/state_based.py | 2 +- mava/components/tf/architectures/utils.py | 2 +- mava/components/tf/losses/__init__.py | 2 +- mava/components/tf/modules/__init__.py | 2 +- mava/components/tf/modules/communication/__init__.py | 2 +- mava/components/tf/modules/communication/base.py | 2 +- mava/components/tf/modules/communication/broadcasted.py | 2 +- mava/components/tf/modules/communication/targeted.py | 2 +- mava/components/tf/modules/exploration/__init__.py | 2 +- .../tf/modules/exploration/exploration_scheduling.py | 2 +- mava/components/tf/modules/mixing/__init__.py | 2 +- mava/components/tf/modules/mixing/additive.py | 2 +- mava/components/tf/modules/mixing/base.py | 2 +- mava/components/tf/modules/mixing/monotonic.py | 2 +- mava/components/tf/modules/stabilising/__init__.py | 2 +- mava/components/tf/modules/stabilising/base.py | 2 +- mava/components/tf/modules/stabilising/fingerprints.py | 2 +- .../tf/modules/stabilising/importance_sampling.py | 2 +- mava/components/tf/networks/__init__.py | 2 +- mava/components/tf/networks/additive.py | 2 +- mava/components/tf/networks/communication.py | 2 +- mava/components/tf/networks/epsilon_greedy.py | 2 +- mava/components/tf/networks/fingerprints.py | 2 +- mava/components/tf/networks/hypernetwork.py | 2 +- mava/components/tf/networks/monotonic.py | 2 +- mava/components/tf/networks/transformed.py | 2 +- mava/components/tf/networks/weighted.py | 2 +- mava/core.py | 2 +- mava/environment_loop.py | 2 +- mava/environment_loops/__init__.py | 2 +- mava/environment_loops/debugging_envs.py | 2 +- mava/systems/__init__.py | 2 +- mava/systems/builders.py | 2 +- mava/systems/replay.py | 2 +- mava/systems/system.py | 2 +- mava/systems/tf/__init__.py | 2 +- mava/systems/tf/dial/__init__.py | 2 +- mava/systems/tf/dial/builder.py | 2 +- mava/systems/tf/dial/execution.py | 2 +- mava/systems/tf/dial/system.py | 2 +- mava/systems/tf/dial/training.py | 2 +- mava/systems/tf/mad4pg/__init__.py | 2 +- mava/systems/tf/mad4pg/builder.py | 2 +- mava/systems/tf/mad4pg/execution.py | 2 +- mava/systems/tf/mad4pg/system.py | 2 +- mava/systems/tf/mad4pg/training.py | 2 +- mava/systems/tf/maddpg/__init__.py | 2 +- mava/systems/tf/maddpg/builder.py | 2 +- mava/systems/tf/maddpg/execution.py | 2 +- mava/systems/tf/maddpg/system.py | 2 +- mava/systems/tf/maddpg/training.py | 2 +- mava/systems/tf/madqn/__init__.py | 2 +- mava/systems/tf/madqn/builder.py | 2 +- mava/systems/tf/madqn/execution.py | 2 +- mava/systems/tf/madqn/system.py | 2 +- mava/systems/tf/madqn/training.py | 2 +- mava/systems/tf/mappo/__init__.py | 2 +- mava/systems/tf/mappo/builder.py | 2 +- mava/systems/tf/mappo/execution.py | 2 +- mava/systems/tf/mappo/system.py | 2 +- mava/systems/tf/mappo/training.py | 2 +- mava/systems/tf/qmix/__init__.py | 2 +- mava/systems/tf/qmix/builder.py | 2 +- mava/systems/tf/qmix/execution.py | 2 +- mava/systems/tf/qmix/system.py | 2 +- mava/systems/tf/qmix/training.py | 2 +- mava/systems/tf/vdn/__init__.py | 2 +- mava/systems/tf/vdn/builder.py | 2 +- mava/systems/tf/vdn/execution.py | 2 +- mava/systems/tf/vdn/system.py | 2 +- mava/systems/tf/vdn/training.py | 2 +- mava/types.py | 2 +- mava/utils/__init__.py | 2 +- mava/utils/debugging/environment.py | 2 +- mava/utils/debugging/environments/__init__.py | 2 +- mava/utils/debugging/environments/switch_game.py | 2 +- mava/utils/debugging/environments/two_step.py | 2 +- mava/utils/environments/RoboCup_env/__init__.py | 2 +- mava/utils/environments/RoboCup_env/helper.py | 2 +- .../environments/RoboCup_env/robocup_agents/__init__.py | 2 +- .../RoboCup_env/robocup_agents/custom_executor.py | 2 +- .../environments/RoboCup_env/robocup_agents/fixed_agent.py | 2 +- .../environments/RoboCup_env/robocup_agents/nn_agent.py | 2 +- mava/utils/environments/RoboCup_env/robocup_base.py | 2 +- .../environments/RoboCup_env/robocup_utils/__init__.py | 2 +- mava/utils/environments/RoboCup_env/robocup_utils/agent.py | 2 +- .../RoboCup_env/robocup_utils/extract_policy.py | 2 +- .../environments/RoboCup_env/robocup_utils/game_object.py | 2 +- .../utils/environments/RoboCup_env/robocup_utils/handler.py | 2 +- .../RoboCup_env/robocup_utils/message_parser.py | 2 +- .../RoboCup_env/robocup_utils/player_world_model.py | 2 +- mava/utils/environments/RoboCup_env/robocup_utils/sock.py | 2 +- .../environments/RoboCup_env/robocup_utils/sp_exceptions.py | 2 +- .../utils/environments/RoboCup_env/robocup_utils/trainer.py | 2 +- .../RoboCup_env/robocup_utils/trainer_world_model.py | 2 +- .../RoboCup_env/robocup_utils/util_functions.py | 2 +- mava/utils/environments/__init__.py | 2 +- mava/utils/environments/debugging_utils.py | 2 +- mava/utils/environments/flatland_utils.py | 2 +- mava/utils/environments/pettingzoo_utils.py | 2 +- mava/utils/environments/robocup_utils.py | 2 +- mava/utils/environments/smac_utils.py | 2 +- mava/utils/loggers/__init__.py | 2 +- mava/utils/loggers/base.py | 2 +- mava/utils/loggers/logger_utils.py | 2 +- mava/utils/loggers/tf_logger.py | 2 +- mava/utils/lp_utils.py | 2 +- mava/utils/tf_utils.py | 2 +- mava/wrappers/__init__.py | 2 +- mava/wrappers/debugging_envs.py | 2 +- mava/wrappers/env_preprocess_wrappers.py | 2 +- mava/wrappers/env_wrappers.py | 2 +- mava/wrappers/environment_loop_wrappers.py | 2 +- mava/wrappers/flatland.py | 2 +- mava/wrappers/pettingzoo.py | 2 +- mava/wrappers/robocup.py | 2 +- mava/wrappers/smac.py | 2 +- mava/wrappers/system_trainer_statistics.py | 2 +- setup.py | 6 +++--- test.sh | 2 +- tests/__init__.py | 2 +- tests/conftest.py | 2 +- tests/enums.py | 2 +- tests/environment_loops/environment_loop_test.py | 2 +- tests/mocks.py | 2 +- tests/systems/maddpg_system_test.py | 2 +- tests/systems/madqn_system_test.py | 2 +- tests/systems/mappo_system_test.py | 2 +- tests/utils/test_data.py | 2 +- tests/utils/utils_test.py | 2 +- tests/wrappers/env_preprocess_wrappers_test.py | 2 +- tests/wrappers/wrapper_test.py | 2 +- 187 files changed, 189 insertions(+), 189 deletions(-) diff --git a/LICENSE b/LICENSE index 2c758549a..0810f0b96 100644 --- a/LICENSE +++ b/LICENSE @@ -186,7 +186,7 @@ same "printed page" as the copyright notice for easier identification within third-party archives. - Copyright 2021 [...placeholder...] + Copyright 2021 InstaDeep Ltd Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. diff --git a/examples/debugging_envs/__init__.py b/examples/debugging_envs/__init__.py index f6af6a34e..e61d07c59 100644 --- a/examples/debugging_envs/__init__.py +++ b/examples/debugging_envs/__init__.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/examples/debugging_envs/run_centralised_feedforward_maddpg.py b/examples/debugging_envs/run_centralised_feedforward_maddpg.py index e15a71a70..a33ae511b 100644 --- a/examples/debugging_envs/run_centralised_feedforward_maddpg.py +++ b/examples/debugging_envs/run_centralised_feedforward_maddpg.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/examples/debugging_envs/run_centralised_feedforward_mappo.py b/examples/debugging_envs/run_centralised_feedforward_mappo.py index d7c4cf261..5ef1bfd60 100644 --- a/examples/debugging_envs/run_centralised_feedforward_mappo.py +++ b/examples/debugging_envs/run_centralised_feedforward_mappo.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/examples/debugging_envs/run_decentralised_feedforward_mad4pg.py b/examples/debugging_envs/run_decentralised_feedforward_mad4pg.py index 1f47a13db..db84c4745 100644 --- a/examples/debugging_envs/run_decentralised_feedforward_mad4pg.py +++ b/examples/debugging_envs/run_decentralised_feedforward_mad4pg.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/examples/debugging_envs/run_decentralised_feedforward_mad4pg_discrete.py b/examples/debugging_envs/run_decentralised_feedforward_mad4pg_discrete.py index a1d568905..d25259bf9 100644 --- a/examples/debugging_envs/run_decentralised_feedforward_mad4pg_discrete.py +++ b/examples/debugging_envs/run_decentralised_feedforward_mad4pg_discrete.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/examples/debugging_envs/run_decentralised_feedforward_maddpg.py b/examples/debugging_envs/run_decentralised_feedforward_maddpg.py index a961a4100..e4aee7f66 100644 --- a/examples/debugging_envs/run_decentralised_feedforward_maddpg.py +++ b/examples/debugging_envs/run_decentralised_feedforward_maddpg.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/examples/debugging_envs/run_decentralised_feedforward_maddpg_discrete.py b/examples/debugging_envs/run_decentralised_feedforward_maddpg_discrete.py index 4ee00b3d3..2df332d1a 100644 --- a/examples/debugging_envs/run_decentralised_feedforward_maddpg_discrete.py +++ b/examples/debugging_envs/run_decentralised_feedforward_maddpg_discrete.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/examples/debugging_envs/run_decentralised_feedforward_mappo.py b/examples/debugging_envs/run_decentralised_feedforward_mappo.py index 4c2e7bd59..639963673 100644 --- a/examples/debugging_envs/run_decentralised_feedforward_mappo.py +++ b/examples/debugging_envs/run_decentralised_feedforward_mappo.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/examples/debugging_envs/run_decentralised_recurrent_mad4pg.py b/examples/debugging_envs/run_decentralised_recurrent_mad4pg.py index c201588b2..9d4b172b5 100644 --- a/examples/debugging_envs/run_decentralised_recurrent_mad4pg.py +++ b/examples/debugging_envs/run_decentralised_recurrent_mad4pg.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/examples/debugging_envs/run_decentralised_recurrent_maddpg.py b/examples/debugging_envs/run_decentralised_recurrent_maddpg.py index 8d11e9dc3..d3f751236 100644 --- a/examples/debugging_envs/run_decentralised_recurrent_maddpg.py +++ b/examples/debugging_envs/run_decentralised_recurrent_maddpg.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/examples/debugging_envs/run_feedforward_maddpg_record_video.py b/examples/debugging_envs/run_feedforward_maddpg_record_video.py index 36b1b99de..41058d87a 100644 --- a/examples/debugging_envs/run_feedforward_maddpg_record_video.py +++ b/examples/debugging_envs/run_feedforward_maddpg_record_video.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/examples/debugging_envs/run_feedforward_madqn.py b/examples/debugging_envs/run_feedforward_madqn.py index 21126f745..fa82581aa 100644 --- a/examples/debugging_envs/run_feedforward_madqn.py +++ b/examples/debugging_envs/run_feedforward_madqn.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/examples/debugging_envs/run_feedforward_madqn_with_fingerprints.py b/examples/debugging_envs/run_feedforward_madqn_with_fingerprints.py index 5773f8555..41ff9086e 100644 --- a/examples/debugging_envs/run_feedforward_madqn_with_fingerprints.py +++ b/examples/debugging_envs/run_feedforward_madqn_with_fingerprints.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/examples/debugging_envs/run_feedforward_qmix.py b/examples/debugging_envs/run_feedforward_qmix.py index b0417862c..029b50b59 100644 --- a/examples/debugging_envs/run_feedforward_qmix.py +++ b/examples/debugging_envs/run_feedforward_qmix.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/examples/debugging_envs/run_feedforward_vdn.py b/examples/debugging_envs/run_feedforward_vdn.py index fae4ea2a4..1dffce7f4 100644 --- a/examples/debugging_envs/run_feedforward_vdn.py +++ b/examples/debugging_envs/run_feedforward_vdn.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/examples/debugging_envs/run_networked_feedforward_maddpg.py b/examples/debugging_envs/run_networked_feedforward_maddpg.py index 303160d98..b054518f0 100644 --- a/examples/debugging_envs/run_networked_feedforward_maddpg.py +++ b/examples/debugging_envs/run_networked_feedforward_maddpg.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/examples/debugging_envs/run_recurrent_comm_madqn.py b/examples/debugging_envs/run_recurrent_comm_madqn.py index 9dbb17bda..7cd9358d1 100644 --- a/examples/debugging_envs/run_recurrent_comm_madqn.py +++ b/examples/debugging_envs/run_recurrent_comm_madqn.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/examples/debugging_envs/run_recurrent_dial.py b/examples/debugging_envs/run_recurrent_dial.py index fa456fbbc..2d0cc9975 100644 --- a/examples/debugging_envs/run_recurrent_dial.py +++ b/examples/debugging_envs/run_recurrent_dial.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/examples/debugging_envs/run_recurrent_madqn.py b/examples/debugging_envs/run_recurrent_madqn.py index 3f755744e..f3abbc1cc 100644 --- a/examples/debugging_envs/run_recurrent_madqn.py +++ b/examples/debugging_envs/run_recurrent_madqn.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/examples/debugging_envs/run_state_based_feedforward_maddpg.py b/examples/debugging_envs/run_state_based_feedforward_maddpg.py index 5728508ef..f30973dde 100644 --- a/examples/debugging_envs/run_state_based_feedforward_maddpg.py +++ b/examples/debugging_envs/run_state_based_feedforward_maddpg.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/examples/debugging_envs/run_state_based_recurrent_maddpg.py b/examples/debugging_envs/run_state_based_recurrent_maddpg.py index e987f0c81..3bcea3099 100644 --- a/examples/debugging_envs/run_state_based_recurrent_maddpg.py +++ b/examples/debugging_envs/run_state_based_recurrent_maddpg.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/examples/flatland/run_madqn.py b/examples/flatland/run_madqn.py index d9941ebb3..8290062dc 100644 --- a/examples/flatland/run_madqn.py +++ b/examples/flatland/run_madqn.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/examples/petting_zoo/__init__.py b/examples/petting_zoo/__init__.py index f6af6a34e..e61d07c59 100644 --- a/examples/petting_zoo/__init__.py +++ b/examples/petting_zoo/__init__.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/examples/petting_zoo/run_centralised_feedforward_mappo.py b/examples/petting_zoo/run_centralised_feedforward_mappo.py index cf649b5d0..ec1bd5567 100644 --- a/examples/petting_zoo/run_centralised_feedforward_mappo.py +++ b/examples/petting_zoo/run_centralised_feedforward_mappo.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/examples/petting_zoo/run_decentralised_feedforward_mad4pg_continuous.py b/examples/petting_zoo/run_decentralised_feedforward_mad4pg_continuous.py index a1d3f6eda..c92cf48d9 100644 --- a/examples/petting_zoo/run_decentralised_feedforward_mad4pg_continuous.py +++ b/examples/petting_zoo/run_decentralised_feedforward_mad4pg_continuous.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/examples/petting_zoo/run_decentralised_feedforward_mad4pg_discrete.py b/examples/petting_zoo/run_decentralised_feedforward_mad4pg_discrete.py index 6b44bedcd..63bce2d7e 100644 --- a/examples/petting_zoo/run_decentralised_feedforward_mad4pg_discrete.py +++ b/examples/petting_zoo/run_decentralised_feedforward_mad4pg_discrete.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/examples/petting_zoo/run_decentralised_feedforward_maddpg_continuous.py b/examples/petting_zoo/run_decentralised_feedforward_maddpg_continuous.py index 84a5f76e3..427fa8b6d 100644 --- a/examples/petting_zoo/run_decentralised_feedforward_maddpg_continuous.py +++ b/examples/petting_zoo/run_decentralised_feedforward_maddpg_continuous.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/examples/petting_zoo/run_decentralised_feedforward_maddpg_discrete.py b/examples/petting_zoo/run_decentralised_feedforward_maddpg_discrete.py index 0917f4240..78bf9ec21 100644 --- a/examples/petting_zoo/run_decentralised_feedforward_maddpg_discrete.py +++ b/examples/petting_zoo/run_decentralised_feedforward_maddpg_discrete.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/examples/petting_zoo/run_decentralised_feedforward_madqn.py b/examples/petting_zoo/run_decentralised_feedforward_madqn.py index 1ef4a1aed..9126bff95 100644 --- a/examples/petting_zoo/run_decentralised_feedforward_madqn.py +++ b/examples/petting_zoo/run_decentralised_feedforward_madqn.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/examples/petting_zoo/run_decentralised_feedforward_mappo.py b/examples/petting_zoo/run_decentralised_feedforward_mappo.py index 8712490aa..9812a36a2 100644 --- a/examples/petting_zoo/run_decentralised_feedforward_mappo.py +++ b/examples/petting_zoo/run_decentralised_feedforward_mappo.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/examples/petting_zoo/run_decentralised_recurrent_maddpg.py b/examples/petting_zoo/run_decentralised_recurrent_maddpg.py index 2d3158be4..ee51c1c24 100644 --- a/examples/petting_zoo/run_decentralised_recurrent_maddpg.py +++ b/examples/petting_zoo/run_decentralised_recurrent_maddpg.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/examples/petting_zoo/run_feedforward_maddpg_record_video.py b/examples/petting_zoo/run_feedforward_maddpg_record_video.py index 78790694b..1ed32fdc0 100644 --- a/examples/petting_zoo/run_feedforward_maddpg_record_video.py +++ b/examples/petting_zoo/run_feedforward_maddpg_record_video.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/examples/petting_zoo/run_feedforward_madqn_with_fingerprints.py b/examples/petting_zoo/run_feedforward_madqn_with_fingerprints.py index f69930e84..b457e61ae 100644 --- a/examples/petting_zoo/run_feedforward_madqn_with_fingerprints.py +++ b/examples/petting_zoo/run_feedforward_madqn_with_fingerprints.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/examples/petting_zoo/run_feedforward_qmix.py b/examples/petting_zoo/run_feedforward_qmix.py index d351c0b22..c9632b06b 100644 --- a/examples/petting_zoo/run_feedforward_qmix.py +++ b/examples/petting_zoo/run_feedforward_qmix.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/examples/petting_zoo/run_feedforward_vdn.py b/examples/petting_zoo/run_feedforward_vdn.py index 8b44b953e..1f22dd564 100644 --- a/examples/petting_zoo/run_feedforward_vdn.py +++ b/examples/petting_zoo/run_feedforward_vdn.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/examples/petting_zoo/run_networked_feedforward_maddpg_sparse.py b/examples/petting_zoo/run_networked_feedforward_maddpg_sparse.py index 1f623e7df..124a25c65 100644 --- a/examples/petting_zoo/run_networked_feedforward_maddpg_sparse.py +++ b/examples/petting_zoo/run_networked_feedforward_maddpg_sparse.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/examples/smac/run_feedforward_madqn.py b/examples/smac/run_feedforward_madqn.py index 2c061fb5d..9fe5e64a8 100644 --- a/examples/smac/run_feedforward_madqn.py +++ b/examples/smac/run_feedforward_madqn.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/examples/smac/run_recurrent_madqn.py b/examples/smac/run_recurrent_madqn.py index 731f24005..5f81d840f 100644 --- a/examples/smac/run_recurrent_madqn.py +++ b/examples/smac/run_recurrent_madqn.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/_metadata.py b/mava/_metadata.py index 9b05eae8a..3bb2d01a1 100644 --- a/mava/_metadata.py +++ b/mava/_metadata.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/adders/__init__.py b/mava/adders/__init__.py index d26c9084b..09b57acb7 100644 --- a/mava/adders/__init__.py +++ b/mava/adders/__init__.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/adders/base.py b/mava/adders/base.py index a6cef1009..abc67e220 100644 --- a/mava/adders/base.py +++ b/mava/adders/base.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/adders/reverb/__init__.py b/mava/adders/reverb/__init__.py index 13a5dfae5..03405eec0 100644 --- a/mava/adders/reverb/__init__.py +++ b/mava/adders/reverb/__init__.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/adders/reverb/base.py b/mava/adders/reverb/base.py index 0f6cb0428..c3ae086bf 100644 --- a/mava/adders/reverb/base.py +++ b/mava/adders/reverb/base.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/adders/reverb/episode.py b/mava/adders/reverb/episode.py index f88ada45a..46e626a1e 100644 --- a/mava/adders/reverb/episode.py +++ b/mava/adders/reverb/episode.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/adders/reverb/sequence.py b/mava/adders/reverb/sequence.py index 0b19de43f..168fd0a02 100644 --- a/mava/adders/reverb/sequence.py +++ b/mava/adders/reverb/sequence.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/adders/reverb/transition.py b/mava/adders/reverb/transition.py index f3878236d..804bebbcf 100644 --- a/mava/adders/reverb/transition.py +++ b/mava/adders/reverb/transition.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/adders/reverb/utils.py b/mava/adders/reverb/utils.py index 5adcbcdd5..64770f3a4 100644 --- a/mava/adders/reverb/utils.py +++ b/mava/adders/reverb/utils.py @@ -1,4 +1,4 @@ -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/components/__init__.py b/mava/components/__init__.py index f6af6a34e..e61d07c59 100644 --- a/mava/components/__init__.py +++ b/mava/components/__init__.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/components/tf/__init__.py b/mava/components/tf/__init__.py index f6af6a34e..e61d07c59 100644 --- a/mava/components/tf/__init__.py +++ b/mava/components/tf/__init__.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/components/tf/architectures/__init__.py b/mava/components/tf/architectures/__init__.py index 02be316eb..fe9385f5a 100644 --- a/mava/components/tf/architectures/__init__.py +++ b/mava/components/tf/architectures/__init__.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/components/tf/architectures/base.py b/mava/components/tf/architectures/base.py index d9a1aeec4..16845cee0 100644 --- a/mava/components/tf/architectures/base.py +++ b/mava/components/tf/architectures/base.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/components/tf/architectures/centralised.py b/mava/components/tf/architectures/centralised.py index 0c92c5dfd..6253c9127 100644 --- a/mava/components/tf/architectures/centralised.py +++ b/mava/components/tf/architectures/centralised.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/components/tf/architectures/decentralised.py b/mava/components/tf/architectures/decentralised.py index cbdc0b79d..e2591cdd4 100644 --- a/mava/components/tf/architectures/decentralised.py +++ b/mava/components/tf/architectures/decentralised.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/components/tf/architectures/networked.py b/mava/components/tf/architectures/networked.py index 3b63ea40d..71ea71a02 100644 --- a/mava/components/tf/architectures/networked.py +++ b/mava/components/tf/architectures/networked.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/components/tf/architectures/state_based.py b/mava/components/tf/architectures/state_based.py index d99b16cb5..ee67aba1e 100644 --- a/mava/components/tf/architectures/state_based.py +++ b/mava/components/tf/architectures/state_based.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/components/tf/architectures/utils.py b/mava/components/tf/architectures/utils.py index d7624bea9..f49901526 100644 --- a/mava/components/tf/architectures/utils.py +++ b/mava/components/tf/architectures/utils.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/components/tf/losses/__init__.py b/mava/components/tf/losses/__init__.py index f6af6a34e..e61d07c59 100644 --- a/mava/components/tf/losses/__init__.py +++ b/mava/components/tf/losses/__init__.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/components/tf/modules/__init__.py b/mava/components/tf/modules/__init__.py index f6af6a34e..e61d07c59 100644 --- a/mava/components/tf/modules/__init__.py +++ b/mava/components/tf/modules/__init__.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/components/tf/modules/communication/__init__.py b/mava/components/tf/modules/communication/__init__.py index 12cdcb17c..c1c7def1a 100644 --- a/mava/components/tf/modules/communication/__init__.py +++ b/mava/components/tf/modules/communication/__init__.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/components/tf/modules/communication/base.py b/mava/components/tf/modules/communication/base.py index 3eb71d861..11299c58b 100644 --- a/mava/components/tf/modules/communication/base.py +++ b/mava/components/tf/modules/communication/base.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/components/tf/modules/communication/broadcasted.py b/mava/components/tf/modules/communication/broadcasted.py index 1ab599496..2604e8756 100644 --- a/mava/components/tf/modules/communication/broadcasted.py +++ b/mava/components/tf/modules/communication/broadcasted.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/components/tf/modules/communication/targeted.py b/mava/components/tf/modules/communication/targeted.py index b44a6d6f1..9ff9da1a5 100644 --- a/mava/components/tf/modules/communication/targeted.py +++ b/mava/components/tf/modules/communication/targeted.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/components/tf/modules/exploration/__init__.py b/mava/components/tf/modules/exploration/__init__.py index b03c5a638..58a08e119 100644 --- a/mava/components/tf/modules/exploration/__init__.py +++ b/mava/components/tf/modules/exploration/__init__.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/components/tf/modules/exploration/exploration_scheduling.py b/mava/components/tf/modules/exploration/exploration_scheduling.py index b34bc7a1f..8d61a3492 100644 --- a/mava/components/tf/modules/exploration/exploration_scheduling.py +++ b/mava/components/tf/modules/exploration/exploration_scheduling.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/components/tf/modules/mixing/__init__.py b/mava/components/tf/modules/mixing/__init__.py index deb69a39b..b1fea8417 100644 --- a/mava/components/tf/modules/mixing/__init__.py +++ b/mava/components/tf/modules/mixing/__init__.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/components/tf/modules/mixing/additive.py b/mava/components/tf/modules/mixing/additive.py index a435ef44d..259d3287c 100644 --- a/mava/components/tf/modules/mixing/additive.py +++ b/mava/components/tf/modules/mixing/additive.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/components/tf/modules/mixing/base.py b/mava/components/tf/modules/mixing/base.py index b1826fcf3..70f28647e 100644 --- a/mava/components/tf/modules/mixing/base.py +++ b/mava/components/tf/modules/mixing/base.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/components/tf/modules/mixing/monotonic.py b/mava/components/tf/modules/mixing/monotonic.py index 245bc4c49..b4f9140d9 100644 --- a/mava/components/tf/modules/mixing/monotonic.py +++ b/mava/components/tf/modules/mixing/monotonic.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/components/tf/modules/stabilising/__init__.py b/mava/components/tf/modules/stabilising/__init__.py index ee06f4b0a..9d6685ec8 100644 --- a/mava/components/tf/modules/stabilising/__init__.py +++ b/mava/components/tf/modules/stabilising/__init__.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/components/tf/modules/stabilising/base.py b/mava/components/tf/modules/stabilising/base.py index 11f10c0b0..967df6d8f 100644 --- a/mava/components/tf/modules/stabilising/base.py +++ b/mava/components/tf/modules/stabilising/base.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/components/tf/modules/stabilising/fingerprints.py b/mava/components/tf/modules/stabilising/fingerprints.py index d17172e58..52a99e59f 100644 --- a/mava/components/tf/modules/stabilising/fingerprints.py +++ b/mava/components/tf/modules/stabilising/fingerprints.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/components/tf/modules/stabilising/importance_sampling.py b/mava/components/tf/modules/stabilising/importance_sampling.py index 1569f720c..85ed9638e 100644 --- a/mava/components/tf/modules/stabilising/importance_sampling.py +++ b/mava/components/tf/modules/stabilising/importance_sampling.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/components/tf/networks/__init__.py b/mava/components/tf/networks/__init__.py index 12bfe9e2b..240eefcd4 100644 --- a/mava/components/tf/networks/__init__.py +++ b/mava/components/tf/networks/__init__.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/components/tf/networks/additive.py b/mava/components/tf/networks/additive.py index cf5549439..4ae7ab4a3 100644 --- a/mava/components/tf/networks/additive.py +++ b/mava/components/tf/networks/additive.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/components/tf/networks/communication.py b/mava/components/tf/networks/communication.py index faeead858..44d11a15a 100644 --- a/mava/components/tf/networks/communication.py +++ b/mava/components/tf/networks/communication.py @@ -1,4 +1,4 @@ -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/components/tf/networks/epsilon_greedy.py b/mava/components/tf/networks/epsilon_greedy.py index 52771c90a..61174cfc8 100644 --- a/mava/components/tf/networks/epsilon_greedy.py +++ b/mava/components/tf/networks/epsilon_greedy.py @@ -1,4 +1,4 @@ -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/components/tf/networks/fingerprints.py b/mava/components/tf/networks/fingerprints.py index a6efe92cc..2aa4fb6bb 100644 --- a/mava/components/tf/networks/fingerprints.py +++ b/mava/components/tf/networks/fingerprints.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/components/tf/networks/hypernetwork.py b/mava/components/tf/networks/hypernetwork.py index 8771d4ef9..a2729c641 100644 --- a/mava/components/tf/networks/hypernetwork.py +++ b/mava/components/tf/networks/hypernetwork.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/components/tf/networks/monotonic.py b/mava/components/tf/networks/monotonic.py index dff1010e6..f86b6b353 100644 --- a/mava/components/tf/networks/monotonic.py +++ b/mava/components/tf/networks/monotonic.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/components/tf/networks/transformed.py b/mava/components/tf/networks/transformed.py index eae3acbb4..ee432804e 100644 --- a/mava/components/tf/networks/transformed.py +++ b/mava/components/tf/networks/transformed.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/components/tf/networks/weighted.py b/mava/components/tf/networks/weighted.py index 34e6c6d3e..e9b9fb98b 100644 --- a/mava/components/tf/networks/weighted.py +++ b/mava/components/tf/networks/weighted.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/core.py b/mava/core.py index dc3caa70f..09ba5893f 100644 --- a/mava/core.py +++ b/mava/core.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/environment_loop.py b/mava/environment_loop.py index 6599abef5..0c7455234 100644 --- a/mava/environment_loop.py +++ b/mava/environment_loop.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/environment_loops/__init__.py b/mava/environment_loops/__init__.py index f6af6a34e..e61d07c59 100644 --- a/mava/environment_loops/__init__.py +++ b/mava/environment_loops/__init__.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/environment_loops/debugging_envs.py b/mava/environment_loops/debugging_envs.py index 60c1d8745..7ed406d7d 100644 --- a/mava/environment_loops/debugging_envs.py +++ b/mava/environment_loops/debugging_envs.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/systems/__init__.py b/mava/systems/__init__.py index f6af6a34e..e61d07c59 100644 --- a/mava/systems/__init__.py +++ b/mava/systems/__init__.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/systems/builders.py b/mava/systems/builders.py index ac0480ebd..6f9b19286 100644 --- a/mava/systems/builders.py +++ b/mava/systems/builders.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/systems/replay.py b/mava/systems/replay.py index 8d1c4f879..2e230938b 100644 --- a/mava/systems/replay.py +++ b/mava/systems/replay.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/systems/system.py b/mava/systems/system.py index 58ca13b2a..101a000f1 100644 --- a/mava/systems/system.py +++ b/mava/systems/system.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/systems/tf/__init__.py b/mava/systems/tf/__init__.py index f6af6a34e..e61d07c59 100644 --- a/mava/systems/tf/__init__.py +++ b/mava/systems/tf/__init__.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/systems/tf/dial/__init__.py b/mava/systems/tf/dial/__init__.py index dbfea7e80..b3bf4541a 100644 --- a/mava/systems/tf/dial/__init__.py +++ b/mava/systems/tf/dial/__init__.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/systems/tf/dial/builder.py b/mava/systems/tf/dial/builder.py index 478a98a3a..5f5804bec 100644 --- a/mava/systems/tf/dial/builder.py +++ b/mava/systems/tf/dial/builder.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/systems/tf/dial/execution.py b/mava/systems/tf/dial/execution.py index 65ff18242..eda663698 100644 --- a/mava/systems/tf/dial/execution.py +++ b/mava/systems/tf/dial/execution.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/systems/tf/dial/system.py b/mava/systems/tf/dial/system.py index 2eb70a5b9..770e1a0ff 100644 --- a/mava/systems/tf/dial/system.py +++ b/mava/systems/tf/dial/system.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/systems/tf/dial/training.py b/mava/systems/tf/dial/training.py index 6aef3ecb4..7615c9526 100644 --- a/mava/systems/tf/dial/training.py +++ b/mava/systems/tf/dial/training.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/systems/tf/mad4pg/__init__.py b/mava/systems/tf/mad4pg/__init__.py index e540edebb..a3b042217 100644 --- a/mava/systems/tf/mad4pg/__init__.py +++ b/mava/systems/tf/mad4pg/__init__.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/systems/tf/mad4pg/builder.py b/mava/systems/tf/mad4pg/builder.py index 4f2ded222..958847184 100644 --- a/mava/systems/tf/mad4pg/builder.py +++ b/mava/systems/tf/mad4pg/builder.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/systems/tf/mad4pg/execution.py b/mava/systems/tf/mad4pg/execution.py index 8819618c5..33f8d2b67 100644 --- a/mava/systems/tf/mad4pg/execution.py +++ b/mava/systems/tf/mad4pg/execution.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/systems/tf/mad4pg/system.py b/mava/systems/tf/mad4pg/system.py index ccf40928b..313a56887 100644 --- a/mava/systems/tf/mad4pg/system.py +++ b/mava/systems/tf/mad4pg/system.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/systems/tf/mad4pg/training.py b/mava/systems/tf/mad4pg/training.py index b51449a56..d8a56d6a6 100644 --- a/mava/systems/tf/mad4pg/training.py +++ b/mava/systems/tf/mad4pg/training.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/systems/tf/maddpg/__init__.py b/mava/systems/tf/maddpg/__init__.py index 2f823085d..93edd7f3b 100644 --- a/mava/systems/tf/maddpg/__init__.py +++ b/mava/systems/tf/maddpg/__init__.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/systems/tf/maddpg/builder.py b/mava/systems/tf/maddpg/builder.py index 22f8efd9a..c54ef849a 100644 --- a/mava/systems/tf/maddpg/builder.py +++ b/mava/systems/tf/maddpg/builder.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/systems/tf/maddpg/execution.py b/mava/systems/tf/maddpg/execution.py index 8c70b408a..de7c0f2fd 100644 --- a/mava/systems/tf/maddpg/execution.py +++ b/mava/systems/tf/maddpg/execution.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/systems/tf/maddpg/system.py b/mava/systems/tf/maddpg/system.py index 0750e8070..49f50879e 100644 --- a/mava/systems/tf/maddpg/system.py +++ b/mava/systems/tf/maddpg/system.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/systems/tf/maddpg/training.py b/mava/systems/tf/maddpg/training.py index d6a320200..b529166b0 100644 --- a/mava/systems/tf/maddpg/training.py +++ b/mava/systems/tf/maddpg/training.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/systems/tf/madqn/__init__.py b/mava/systems/tf/madqn/__init__.py index 274426e19..90d6e8b07 100644 --- a/mava/systems/tf/madqn/__init__.py +++ b/mava/systems/tf/madqn/__init__.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/systems/tf/madqn/builder.py b/mava/systems/tf/madqn/builder.py index 2abcf56dc..adde60888 100644 --- a/mava/systems/tf/madqn/builder.py +++ b/mava/systems/tf/madqn/builder.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/systems/tf/madqn/execution.py b/mava/systems/tf/madqn/execution.py index 1e7fed0da..57dfdd0ed 100644 --- a/mava/systems/tf/madqn/execution.py +++ b/mava/systems/tf/madqn/execution.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/systems/tf/madqn/system.py b/mava/systems/tf/madqn/system.py index bf5d43109..7db105905 100644 --- a/mava/systems/tf/madqn/system.py +++ b/mava/systems/tf/madqn/system.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/systems/tf/madqn/training.py b/mava/systems/tf/madqn/training.py index 92a405c75..b12661c84 100644 --- a/mava/systems/tf/madqn/training.py +++ b/mava/systems/tf/madqn/training.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/systems/tf/mappo/__init__.py b/mava/systems/tf/mappo/__init__.py index df71860a8..0150450d3 100644 --- a/mava/systems/tf/mappo/__init__.py +++ b/mava/systems/tf/mappo/__init__.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/systems/tf/mappo/builder.py b/mava/systems/tf/mappo/builder.py index ee413f360..08904dcc4 100644 --- a/mava/systems/tf/mappo/builder.py +++ b/mava/systems/tf/mappo/builder.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/systems/tf/mappo/execution.py b/mava/systems/tf/mappo/execution.py index eb65b00b0..834cb9297 100644 --- a/mava/systems/tf/mappo/execution.py +++ b/mava/systems/tf/mappo/execution.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/systems/tf/mappo/system.py b/mava/systems/tf/mappo/system.py index a5e81691c..d724072cd 100644 --- a/mava/systems/tf/mappo/system.py +++ b/mava/systems/tf/mappo/system.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/systems/tf/mappo/training.py b/mava/systems/tf/mappo/training.py index 4853ce178..a08d9deff 100644 --- a/mava/systems/tf/mappo/training.py +++ b/mava/systems/tf/mappo/training.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/systems/tf/qmix/__init__.py b/mava/systems/tf/qmix/__init__.py index 717056c30..257a4cbb5 100644 --- a/mava/systems/tf/qmix/__init__.py +++ b/mava/systems/tf/qmix/__init__.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/systems/tf/qmix/builder.py b/mava/systems/tf/qmix/builder.py index 9104df2d0..886a5565e 100644 --- a/mava/systems/tf/qmix/builder.py +++ b/mava/systems/tf/qmix/builder.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/systems/tf/qmix/execution.py b/mava/systems/tf/qmix/execution.py index bd320ea90..08a96824f 100644 --- a/mava/systems/tf/qmix/execution.py +++ b/mava/systems/tf/qmix/execution.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/systems/tf/qmix/system.py b/mava/systems/tf/qmix/system.py index 398305285..7030a6742 100644 --- a/mava/systems/tf/qmix/system.py +++ b/mava/systems/tf/qmix/system.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/systems/tf/qmix/training.py b/mava/systems/tf/qmix/training.py index 967403663..d5f445390 100644 --- a/mava/systems/tf/qmix/training.py +++ b/mava/systems/tf/qmix/training.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/systems/tf/vdn/__init__.py b/mava/systems/tf/vdn/__init__.py index 9e174d66b..54931fbb3 100644 --- a/mava/systems/tf/vdn/__init__.py +++ b/mava/systems/tf/vdn/__init__.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/systems/tf/vdn/builder.py b/mava/systems/tf/vdn/builder.py index bd34b26f5..b14ae162d 100644 --- a/mava/systems/tf/vdn/builder.py +++ b/mava/systems/tf/vdn/builder.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/systems/tf/vdn/execution.py b/mava/systems/tf/vdn/execution.py index 22dd62fa3..c89224c91 100644 --- a/mava/systems/tf/vdn/execution.py +++ b/mava/systems/tf/vdn/execution.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/systems/tf/vdn/system.py b/mava/systems/tf/vdn/system.py index eeecd2185..84a22553c 100644 --- a/mava/systems/tf/vdn/system.py +++ b/mava/systems/tf/vdn/system.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/systems/tf/vdn/training.py b/mava/systems/tf/vdn/training.py index f7b9f336f..da7d7818b 100644 --- a/mava/systems/tf/vdn/training.py +++ b/mava/systems/tf/vdn/training.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/types.py b/mava/types.py index 2a586edba..0f9c124f3 100644 --- a/mava/types.py +++ b/mava/types.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/utils/__init__.py b/mava/utils/__init__.py index f6af6a34e..e61d07c59 100644 --- a/mava/utils/__init__.py +++ b/mava/utils/__init__.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/utils/debugging/environment.py b/mava/utils/debugging/environment.py index 769c17fcc..a27c4f586 100644 --- a/mava/utils/debugging/environment.py +++ b/mava/utils/debugging/environment.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/utils/debugging/environments/__init__.py b/mava/utils/debugging/environments/__init__.py index d0a60af55..e56c34ebe 100644 --- a/mava/utils/debugging/environments/__init__.py +++ b/mava/utils/debugging/environments/__init__.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/utils/debugging/environments/switch_game.py b/mava/utils/debugging/environments/switch_game.py index 21fc6845a..c9c8fa2dd 100644 --- a/mava/utils/debugging/environments/switch_game.py +++ b/mava/utils/debugging/environments/switch_game.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/utils/debugging/environments/two_step.py b/mava/utils/debugging/environments/two_step.py index f289177ee..89c2d8e53 100644 --- a/mava/utils/debugging/environments/two_step.py +++ b/mava/utils/debugging/environments/two_step.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/utils/environments/RoboCup_env/__init__.py b/mava/utils/environments/RoboCup_env/__init__.py index f6af6a34e..e61d07c59 100644 --- a/mava/utils/environments/RoboCup_env/__init__.py +++ b/mava/utils/environments/RoboCup_env/__init__.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/utils/environments/RoboCup_env/helper.py b/mava/utils/environments/RoboCup_env/helper.py index 238fa070a..6c60331b2 100644 --- a/mava/utils/environments/RoboCup_env/helper.py +++ b/mava/utils/environments/RoboCup_env/helper.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/utils/environments/RoboCup_env/robocup_agents/__init__.py b/mava/utils/environments/RoboCup_env/robocup_agents/__init__.py index f6af6a34e..e61d07c59 100644 --- a/mava/utils/environments/RoboCup_env/robocup_agents/__init__.py +++ b/mava/utils/environments/RoboCup_env/robocup_agents/__init__.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/utils/environments/RoboCup_env/robocup_agents/custom_executor.py b/mava/utils/environments/RoboCup_env/robocup_agents/custom_executor.py index b6bd70170..b2ba4350a 100644 --- a/mava/utils/environments/RoboCup_env/robocup_agents/custom_executor.py +++ b/mava/utils/environments/RoboCup_env/robocup_agents/custom_executor.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/utils/environments/RoboCup_env/robocup_agents/fixed_agent.py b/mava/utils/environments/RoboCup_env/robocup_agents/fixed_agent.py index 0f11d629f..5cfe59b0c 100755 --- a/mava/utils/environments/RoboCup_env/robocup_agents/fixed_agent.py +++ b/mava/utils/environments/RoboCup_env/robocup_agents/fixed_agent.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/utils/environments/RoboCup_env/robocup_agents/nn_agent.py b/mava/utils/environments/RoboCup_env/robocup_agents/nn_agent.py index 2dab2c3f2..505b985d1 100755 --- a/mava/utils/environments/RoboCup_env/robocup_agents/nn_agent.py +++ b/mava/utils/environments/RoboCup_env/robocup_agents/nn_agent.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/utils/environments/RoboCup_env/robocup_base.py b/mava/utils/environments/RoboCup_env/robocup_base.py index 628c33e4f..0a5eea50c 100644 --- a/mava/utils/environments/RoboCup_env/robocup_base.py +++ b/mava/utils/environments/RoboCup_env/robocup_base.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/__init__.py b/mava/utils/environments/RoboCup_env/robocup_utils/__init__.py index f6af6a34e..e61d07c59 100755 --- a/mava/utils/environments/RoboCup_env/robocup_utils/__init__.py +++ b/mava/utils/environments/RoboCup_env/robocup_utils/__init__.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/agent.py b/mava/utils/environments/RoboCup_env/robocup_utils/agent.py index f03c0c4c0..83fa3309f 100755 --- a/mava/utils/environments/RoboCup_env/robocup_utils/agent.py +++ b/mava/utils/environments/RoboCup_env/robocup_utils/agent.py @@ -1,5 +1,5 @@ # !/usr/bin/env python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/extract_policy.py b/mava/utils/environments/RoboCup_env/robocup_utils/extract_policy.py index 5900c9336..d8322515b 100644 --- a/mava/utils/environments/RoboCup_env/robocup_utils/extract_policy.py +++ b/mava/utils/environments/RoboCup_env/robocup_utils/extract_policy.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/game_object.py b/mava/utils/environments/RoboCup_env/robocup_utils/game_object.py index a04584a58..c49b68329 100755 --- a/mava/utils/environments/RoboCup_env/robocup_utils/game_object.py +++ b/mava/utils/environments/RoboCup_env/robocup_utils/game_object.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/handler.py b/mava/utils/environments/RoboCup_env/robocup_utils/handler.py index 92a4811b7..a98cd9784 100755 --- a/mava/utils/environments/RoboCup_env/robocup_utils/handler.py +++ b/mava/utils/environments/RoboCup_env/robocup_utils/handler.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/message_parser.py b/mava/utils/environments/RoboCup_env/robocup_utils/message_parser.py index ca98b6ca2..c36ac0f8b 100755 --- a/mava/utils/environments/RoboCup_env/robocup_utils/message_parser.py +++ b/mava/utils/environments/RoboCup_env/robocup_utils/message_parser.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/player_world_model.py b/mava/utils/environments/RoboCup_env/robocup_utils/player_world_model.py index 4a4517fac..92057e173 100755 --- a/mava/utils/environments/RoboCup_env/robocup_utils/player_world_model.py +++ b/mava/utils/environments/RoboCup_env/robocup_utils/player_world_model.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/sock.py b/mava/utils/environments/RoboCup_env/robocup_utils/sock.py index 02d1e9b4f..481e5f756 100755 --- a/mava/utils/environments/RoboCup_env/robocup_utils/sock.py +++ b/mava/utils/environments/RoboCup_env/robocup_utils/sock.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/sp_exceptions.py b/mava/utils/environments/RoboCup_env/robocup_utils/sp_exceptions.py index 6b7368e3c..64fc4d97d 100755 --- a/mava/utils/environments/RoboCup_env/robocup_utils/sp_exceptions.py +++ b/mava/utils/environments/RoboCup_env/robocup_utils/sp_exceptions.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/trainer.py b/mava/utils/environments/RoboCup_env/robocup_utils/trainer.py index 31ea65c80..ff8d0314e 100755 --- a/mava/utils/environments/RoboCup_env/robocup_utils/trainer.py +++ b/mava/utils/environments/RoboCup_env/robocup_utils/trainer.py @@ -1,5 +1,5 @@ # !/usr/bin/env python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/trainer_world_model.py b/mava/utils/environments/RoboCup_env/robocup_utils/trainer_world_model.py index aab4210f5..75cb46630 100755 --- a/mava/utils/environments/RoboCup_env/robocup_utils/trainer_world_model.py +++ b/mava/utils/environments/RoboCup_env/robocup_utils/trainer_world_model.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/utils/environments/RoboCup_env/robocup_utils/util_functions.py b/mava/utils/environments/RoboCup_env/robocup_utils/util_functions.py index 9405c6fc4..5a033139d 100644 --- a/mava/utils/environments/RoboCup_env/robocup_utils/util_functions.py +++ b/mava/utils/environments/RoboCup_env/robocup_utils/util_functions.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/utils/environments/__init__.py b/mava/utils/environments/__init__.py index f6af6a34e..e61d07c59 100644 --- a/mava/utils/environments/__init__.py +++ b/mava/utils/environments/__init__.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/utils/environments/debugging_utils.py b/mava/utils/environments/debugging_utils.py index 37a1c4ba7..129a6ae62 100644 --- a/mava/utils/environments/debugging_utils.py +++ b/mava/utils/environments/debugging_utils.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/utils/environments/flatland_utils.py b/mava/utils/environments/flatland_utils.py index 97296e5d8..494b4a8b2 100644 --- a/mava/utils/environments/flatland_utils.py +++ b/mava/utils/environments/flatland_utils.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/utils/environments/pettingzoo_utils.py b/mava/utils/environments/pettingzoo_utils.py index 6a4f0e9ae..70c2ea511 100644 --- a/mava/utils/environments/pettingzoo_utils.py +++ b/mava/utils/environments/pettingzoo_utils.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/utils/environments/robocup_utils.py b/mava/utils/environments/robocup_utils.py index fb5c85b60..f1044eea4 100644 --- a/mava/utils/environments/robocup_utils.py +++ b/mava/utils/environments/robocup_utils.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/utils/environments/smac_utils.py b/mava/utils/environments/smac_utils.py index d2cacba16..39d30cc26 100644 --- a/mava/utils/environments/smac_utils.py +++ b/mava/utils/environments/smac_utils.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/utils/loggers/__init__.py b/mava/utils/loggers/__init__.py index 2381ee940..a05c144b7 100644 --- a/mava/utils/loggers/__init__.py +++ b/mava/utils/loggers/__init__.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/utils/loggers/base.py b/mava/utils/loggers/base.py index 639f780ea..e046eadb0 100644 --- a/mava/utils/loggers/base.py +++ b/mava/utils/loggers/base.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/utils/loggers/logger_utils.py b/mava/utils/loggers/logger_utils.py index ff4def9b5..702d52d12 100644 --- a/mava/utils/loggers/logger_utils.py +++ b/mava/utils/loggers/logger_utils.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/utils/loggers/tf_logger.py b/mava/utils/loggers/tf_logger.py index 87064afe6..b5ca1254e 100644 --- a/mava/utils/loggers/tf_logger.py +++ b/mava/utils/loggers/tf_logger.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/utils/lp_utils.py b/mava/utils/lp_utils.py index 4ec7eab72..cd3287a0d 100644 --- a/mava/utils/lp_utils.py +++ b/mava/utils/lp_utils.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/utils/tf_utils.py b/mava/utils/tf_utils.py index b1f8038d6..0916a45a4 100644 --- a/mava/utils/tf_utils.py +++ b/mava/utils/tf_utils.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/wrappers/__init__.py b/mava/wrappers/__init__.py index beee9cab4..9609afe2b 100644 --- a/mava/wrappers/__init__.py +++ b/mava/wrappers/__init__.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/wrappers/debugging_envs.py b/mava/wrappers/debugging_envs.py index 26af15a2a..df5ac7222 100644 --- a/mava/wrappers/debugging_envs.py +++ b/mava/wrappers/debugging_envs.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/wrappers/env_preprocess_wrappers.py b/mava/wrappers/env_preprocess_wrappers.py index df68d3c97..80ae4274a 100644 --- a/mava/wrappers/env_preprocess_wrappers.py +++ b/mava/wrappers/env_preprocess_wrappers.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/wrappers/env_wrappers.py b/mava/wrappers/env_wrappers.py index ff2ecda5f..8138e87ae 100644 --- a/mava/wrappers/env_wrappers.py +++ b/mava/wrappers/env_wrappers.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/wrappers/environment_loop_wrappers.py b/mava/wrappers/environment_loop_wrappers.py index 3c6e6ed85..3e0699715 100644 --- a/mava/wrappers/environment_loop_wrappers.py +++ b/mava/wrappers/environment_loop_wrappers.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/wrappers/flatland.py b/mava/wrappers/flatland.py index 77a2a278f..23006a109 100644 --- a/mava/wrappers/flatland.py +++ b/mava/wrappers/flatland.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/wrappers/pettingzoo.py b/mava/wrappers/pettingzoo.py index 6a1abde8f..1033198be 100644 --- a/mava/wrappers/pettingzoo.py +++ b/mava/wrappers/pettingzoo.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/wrappers/robocup.py b/mava/wrappers/robocup.py index 3f29a65dd..b4014783b 100644 --- a/mava/wrappers/robocup.py +++ b/mava/wrappers/robocup.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/wrappers/smac.py b/mava/wrappers/smac.py index c6c5784be..556cbdd74 100644 --- a/mava/wrappers/smac.py +++ b/mava/wrappers/smac.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/mava/wrappers/system_trainer_statistics.py b/mava/wrappers/system_trainer_statistics.py index 9c3d807b7..b83479756 100644 --- a/mava/wrappers/system_trainer_statistics.py +++ b/mava/wrappers/system_trainer_statistics.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/setup.py b/setup.py index 8bc32a08a..3f40fd393 100644 --- a/setup.py +++ b/setup.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...] Ltd. All rights reserved. +# Copyright 2021 InstaDeep Ltd Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -70,7 +70,7 @@ serve both as reference implementations of popular algorithms and as strong baselines, while still providing enough flexibility to do novel research. For more information see -[github repository](https://github.com/[...placeholder...]ai/mava).""" +[github repository](https://github.com/InstaDeep Ltdai/mava).""" # Get the version from metadata. version = _metadata.__version__ # type: ignore @@ -86,7 +86,7 @@ description="A Python library for Multi-Agent Reinforcement Learning.", long_description=long_description, long_description_content_type="text/markdown", - author="[...placeholder...]", + author="InstaDeep Ltd", license="Apache License, Version 2.0", keywords="multi-agent reinforcement-learning python machine learning", packages=find_packages(), diff --git a/test.sh b/test.sh index 3270256dc..b523a6d65 100755 --- a/test.sh +++ b/test.sh @@ -1,5 +1,5 @@ #!/bin/bash -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/tests/__init__.py b/tests/__init__.py index f6af6a34e..e61d07c59 100644 --- a/tests/__init__.py +++ b/tests/__init__.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/tests/conftest.py b/tests/conftest.py index 31291d25c..a2ff4f17b 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/tests/enums.py b/tests/enums.py index 6f7989573..6d0164dd4 100644 --- a/tests/enums.py +++ b/tests/enums.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/tests/environment_loops/environment_loop_test.py b/tests/environment_loops/environment_loop_test.py index f3ef42060..2f741a9e1 100644 --- a/tests/environment_loops/environment_loop_test.py +++ b/tests/environment_loops/environment_loop_test.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/tests/mocks.py b/tests/mocks.py index 283e24a45..d08dd72fe 100644 --- a/tests/mocks.py +++ b/tests/mocks.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/tests/systems/maddpg_system_test.py b/tests/systems/maddpg_system_test.py index 2f46781ce..64c248ac9 100644 --- a/tests/systems/maddpg_system_test.py +++ b/tests/systems/maddpg_system_test.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/tests/systems/madqn_system_test.py b/tests/systems/madqn_system_test.py index 50255eabe..05aabe19f 100644 --- a/tests/systems/madqn_system_test.py +++ b/tests/systems/madqn_system_test.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/tests/systems/mappo_system_test.py b/tests/systems/mappo_system_test.py index ac2b015d6..e15d6533b 100644 --- a/tests/systems/mappo_system_test.py +++ b/tests/systems/mappo_system_test.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/tests/utils/test_data.py b/tests/utils/test_data.py index 82575a846..3d8fc7c86 100644 --- a/tests/utils/test_data.py +++ b/tests/utils/test_data.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/tests/utils/utils_test.py b/tests/utils/utils_test.py index 8a9dcc4fe..d181eb943 100644 --- a/tests/utils/utils_test.py +++ b/tests/utils/utils_test.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/tests/wrappers/env_preprocess_wrappers_test.py b/tests/wrappers/env_preprocess_wrappers_test.py index ed794cc5c..297eb42df 100644 --- a/tests/wrappers/env_preprocess_wrappers_test.py +++ b/tests/wrappers/env_preprocess_wrappers_test.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/tests/wrappers/wrapper_test.py b/tests/wrappers/wrapper_test.py index fee9d39d9..a51083f7f 100644 --- a/tests/wrappers/wrapper_test.py +++ b/tests/wrappers/wrapper_test.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 [...placeholder...]. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. From 47e650a3e8604bcc5c4267ddb079faa508d4eb0c Mon Sep 17 00:00:00 2001 From: arnupretorius Date: Tue, 8 Jun 2021 13:26:35 +0200 Subject: [PATCH 181/188] fix setup file --- setup.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/setup.py b/setup.py index 3f40fd393..01697631e 100644 --- a/setup.py +++ b/setup.py @@ -1,5 +1,5 @@ # python3 -# Copyright 2021 InstaDeep Ltd Ltd. All rights reserved. +# Copyright 2021 InstaDeep Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -70,7 +70,7 @@ serve both as reference implementations of popular algorithms and as strong baselines, while still providing enough flexibility to do novel research. For more information see -[github repository](https://github.com/InstaDeep Ltdai/mava).""" +[github repository](https://github.com/instadeepai/mava).""" # Get the version from metadata. version = _metadata.__version__ # type: ignore From b3634480f9f397d5fde68a933fdeab1e2a00f6d0 Mon Sep 17 00:00:00 2001 From: Kale-ab Date: Tue, 8 Jun 2021 16:12:44 +0200 Subject: [PATCH 182/188] fix(launchpad): Fixed stopping of launchpad program. --- mava/systems/tf/maddpg/system.py | 23 ++++++++++++----------- mava/systems/tf/madqn/system.py | 23 ++++++++++++----------- mava/systems/tf/mappo/system.py | 23 ++++++++++++----------- mava/utils/lp_utils.py | 2 +- setup.py | 2 +- 5 files changed, 38 insertions(+), 35 deletions(-) diff --git a/mava/systems/tf/maddpg/system.py b/mava/systems/tf/maddpg/system.py index 0750e8070..cf1119143 100644 --- a/mava/systems/tf/maddpg/system.py +++ b/mava/systems/tf/maddpg/system.py @@ -226,13 +226,16 @@ def replay(self) -> Any: """The replay storage.""" return self._builder.make_replay_tables(self._environment_spec) - def counter(self) -> Any: - return tf2_savers.CheckpointingRunner( - counting.Counter(), - time_delta_minutes=15, - directory=self._checkpoint_subpath, - subdirectory="counter", - ) + def counter(self, checkpoint: bool) -> Any: + if checkpoint: + return tf2_savers.CheckpointingRunner( + counting.Counter(), + time_delta_minutes=15, + directory=self._checkpoint_subpath, + subdirectory="counter", + ) + else: + return counting.Counter() def coordinator(self, counter: counting.Counter) -> Any: return lp_utils.StepsLimiter(counter, self._max_executor_steps) @@ -422,14 +425,12 @@ def evaluator( def build(self, name: str = "maddpg") -> Any: """Build the distributed system topology.""" program = lp.Program(name=name) - counter = None with program.group("replay"): replay = program.add_node(lp.ReverbNode(self.replay)) - if self._checkpoint: - with program.group("counter"): - counter = program.add_node(lp.CourierNode(self.counter)) + with program.group("counter"): + counter = program.add_node(lp.CourierNode(self.counter, self._checkpoint)) if self._max_executor_steps: with program.group("coordinator"): diff --git a/mava/systems/tf/madqn/system.py b/mava/systems/tf/madqn/system.py index bf5d43109..ae9155167 100644 --- a/mava/systems/tf/madqn/system.py +++ b/mava/systems/tf/madqn/system.py @@ -221,13 +221,16 @@ def replay(self) -> Any: """The replay storage.""" return self._builder.make_replay_tables(self._environment_spec) - def counter(self) -> Any: - return tf2_savers.CheckpointingRunner( - counting.Counter(), - time_delta_minutes=15, - directory=self._checkpoint_subpath, - subdirectory="counter", - ) + def counter(self, checkpoint: bool) -> Any: + if checkpoint: + return tf2_savers.CheckpointingRunner( + counting.Counter(), + time_delta_minutes=15, + directory=self._checkpoint_subpath, + subdirectory="counter", + ) + else: + return counting.Counter() def coordinator(self, counter: counting.Counter) -> Any: return lp_utils.StepsLimiter(counter, self._max_executor_steps) # type: ignore @@ -439,14 +442,12 @@ def evaluator( def build(self, name: str = "madqn") -> Any: """Build the distributed system topology.""" program = lp.Program(name=name) - counter = None with program.group("replay"): replay = program.add_node(lp.ReverbNode(self.replay)) - if self._checkpoint: - with program.group("counter"): - counter = program.add_node(lp.CourierNode(self.counter)) + with program.group("counter"): + counter = program.add_node(lp.CourierNode(self.counter, self._checkpoint)) if self._max_executor_steps: with program.group("coordinator"): diff --git a/mava/systems/tf/mappo/system.py b/mava/systems/tf/mappo/system.py index a5e81691c..1c5609250 100644 --- a/mava/systems/tf/mappo/system.py +++ b/mava/systems/tf/mappo/system.py @@ -185,13 +185,16 @@ def replay(self) -> Any: """The replay storage.""" return self._builder.make_replay_tables(self._environment_spec) - def counter(self) -> Any: - return tf2_savers.CheckpointingRunner( - counting.Counter(), - time_delta_minutes=15, - directory=self._checkpoint_subpath, - subdirectory="counter", - ) + def counter(self, checkpoint: bool) -> Any: + if checkpoint: + return tf2_savers.CheckpointingRunner( + counting.Counter(), + time_delta_minutes=15, + directory=self._checkpoint_subpath, + subdirectory="counter", + ) + else: + return counting.Counter() def coordinator(self, counter: counting.Counter) -> Any: return lp_utils.StepsLimiter(counter, self._max_executor_steps) # type: ignore @@ -364,14 +367,12 @@ def evaluator( def build(self, name: str = "mappo") -> Any: """Build the distributed system topology.""" program = lp.Program(name=name) - counter = None with program.group("replay"): replay = program.add_node(lp.ReverbNode(self.replay)) - if self._checkpoint: - with program.group("counter"): - counter = program.add_node(lp.CourierNode(self.counter)) + with program.group("counter"): + counter = program.add_node(lp.CourierNode(self.counter, self._checkpoint)) if self._max_executor_steps: with program.group("coordinator"): diff --git a/mava/utils/lp_utils.py b/mava/utils/lp_utils.py index 4ec7eab72..ebd9672f5 100644 --- a/mava/utils/lp_utils.py +++ b/mava/utils/lp_utils.py @@ -95,7 +95,7 @@ def run(self) -> None: "StepsLimiter: Max steps of %d was reached, terminating", self._max_steps, ) - self._stop_program() + lp.stop() # Don't spam the counter. time.sleep(10.0) diff --git a/setup.py b/setup.py index 8bc32a08a..4fb7b2dd7 100644 --- a/setup.py +++ b/setup.py @@ -48,7 +48,7 @@ ] launchpad_requirements = [ - "dm-launchpad", + "dm-launchpad-nightly", ] testing_formatting_requirements = [ From 7581e66804d9f8dac28f6b0d4481425878284fd2 Mon Sep 17 00:00:00 2001 From: Kale-ab Date: Tue, 8 Jun 2021 16:15:21 +0200 Subject: [PATCH 183/188] chore: Removed unused var. --- mava/utils/lp_utils.py | 1 - 1 file changed, 1 deletion(-) diff --git a/mava/utils/lp_utils.py b/mava/utils/lp_utils.py index cac6a18ba..3117e48ab 100644 --- a/mava/utils/lp_utils.py +++ b/mava/utils/lp_utils.py @@ -72,7 +72,6 @@ def __init__( ): self._counter = counter self._max_steps = max_steps - self._stop_program = lp.make_program_stopper(FLAGS.lp_launch_type) self._steps_key = steps_key def run(self) -> None: From cc18b2f921409ad1ed05024b0c5398373e268e1a Mon Sep 17 00:00:00 2001 From: Kale-ab Date: Tue, 8 Jun 2021 17:11:25 +0200 Subject: [PATCH 184/188] debug: Debugging github actions. --- .github/workflows/ci.yaml | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index ad0a57274..2fb58df1e 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -50,6 +50,10 @@ jobs: flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics # exit-zero treats all errors as warnings. flake8 . --count --exit-zero --statistics + pip show tensorflow + pip show dm-reverb + pip show dm-acme + pip show dm-launchpad - name: Check types with mypy run: | mypy . --exclude docs/ From 27ce63340196bc6ca97340e84dbc3788aa95d8b5 Mon Sep 17 00:00:00 2001 From: Kale-ab Date: Tue, 8 Jun 2021 17:21:34 +0200 Subject: [PATCH 185/188] fix(ci): Updated tf and reverb versions. --- .github/workflows/ci.yaml | 4 ---- setup.py | 5 ++--- 2 files changed, 2 insertions(+), 7 deletions(-) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index 2fb58df1e..ad0a57274 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -50,10 +50,6 @@ jobs: flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics # exit-zero treats all errors as warnings. flake8 . --count --exit-zero --statistics - pip show tensorflow - pip show dm-reverb - pip show dm-acme - pip show dm-launchpad - name: Check types with mypy run: | mypy . --exclude docs/ diff --git a/setup.py b/setup.py index b5e62b7f1..c1179617e 100644 --- a/setup.py +++ b/setup.py @@ -26,14 +26,13 @@ spec.loader.exec_module(_metadata) # type: ignore reverb_requirements = [ - "dm-reverb>=0.2.0", - "tensorflow>=2.4.1", + "dm-reverb>=0.3.0", "jax", "jaxlib", ] tf_requirements = [ - "tensorflow>=2.4.1", + "tensorflow>=2.5.0", "tensorflow_probability", "dm-sonnet", "trfl", From e8b24283ba24b105f085a3712f7d4bb70d2c7783 Mon Sep 17 00:00:00 2001 From: Kale-ab Date: Wed, 9 Jun 2021 12:14:15 +0200 Subject: [PATCH 186/188] chore(release): Minor changes for release. --- OPTIONAL_INSTALL.md | 2 +- mava/_metadata.py | 4 ++-- setup.py | 1 + 3 files changed, 4 insertions(+), 3 deletions(-) diff --git a/OPTIONAL_INSTALL.md b/OPTIONAL_INSTALL.md index 0c4e683e6..8498e3d02 100644 --- a/OPTIONAL_INSTALL.md +++ b/OPTIONAL_INSTALL.md @@ -1,5 +1,5 @@ ### Optional Installs -1. **Optional**: To install ROM files for Atari-Py using AutoROM (https://github.com/PettingZoo-Team/AutoROM). +1. **Optional**: To install ROM files for Atari-Py using [AutoROM](https://github.com/PettingZoo-Team/AutoROM). ``` pip install autorom && AutoROM ``` diff --git a/mava/_metadata.py b/mava/_metadata.py index 3bb2d01a1..478f3bdda 100644 --- a/mava/_metadata.py +++ b/mava/_metadata.py @@ -20,8 +20,8 @@ # We follow Semantic Versioning (https://semver.org/) _MAJOR_VERSION = "0" -_MINOR_VERSION = "1" -_PATCH_VERSION = "0" +_MINOR_VERSION = "0" +_PATCH_VERSION = "1" # Example: '0.4.2' __version__ = ".".join([_MAJOR_VERSION, _MINOR_VERSION, _PATCH_VERSION]) diff --git a/setup.py b/setup.py index c1179617e..3f549a820 100644 --- a/setup.py +++ b/setup.py @@ -118,6 +118,7 @@ "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", + "Programming Language :: Python :: 3.8", "Topic :: Scientific/Engineering :: Artificial Intelligence", ], ) From a71af7ae3a44121309ee10bd37bff6b85142c4a2 Mon Sep 17 00:00:00 2001 From: Kale-ab Date: Wed, 9 Jun 2021 12:20:19 +0200 Subject: [PATCH 187/188] docs: Updated readme to split core dependencies and non-core dependencies. --- README.md | 22 +++++++++++++++++----- 1 file changed, 17 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index e4fda76bb..3b9bcdab5 100644 --- a/README.md +++ b/README.md @@ -171,15 +171,27 @@ We have tested `mava` on Python 3.6, 3.7 and 3.8. pip install --upgrade pip setuptools ``` -2. To install the core libraries with dependencies from source (from root directory): +2. To install the core libraries, including [Reverb](https://github.com/deepmind/reverb) - our storage dataset : ```bash - pip install -e ".[tf,envs,reverb,launchpad]" + pip install id-mava + pip install id-mava[reverb] ``` - Note that the dependencies may be installed selectively by adding and removing their identifiers. Additional optional dependencies include `record_episode` for installing packages required to make video recordings of evaluation runs and `testing_formatting` for running tests and code formatting checks. Extra information on optional installs are given below. +3. To install dependencies for tensorflow agents: + ```bash + pip install id-mava[tf] + ``` +4. For distributed agent support: + ```bash + pip install id-mava[launchpad] + ``` -3. **NB**: Flatland and SMAC installations have to be done separately. Flatland can be installed using: +5. To install example environments, such as [PettingZoo](https://github.com/PettingZoo-Team/PettingZoo): + ```bash + pip install id-mava[envs] + ``` +6. **NB**: For Flatland and SMAC environments, installations have to be done separately. Flatland can be installed using: ```bash pip install .[flatland] @@ -194,7 +206,7 @@ We have tested `mava` on Python 3.6, 3.7 and 3.8. pip install pysc2 pip install git+https://github.com/oxwhirl/smac.git ``` -3. For the 2D RoboCup environment, a local install has only been tested using the Ubuntu 18.04 operating system. +7. For the 2D RoboCup environment, a local install has only been tested using the Ubuntu 18.04 operating system. The installation can be performed by running the RoboCup bash script while inside the Mava python virtual environment. ```bash From 4b368329e90b7f86b5c8884dfcb4a6884c7cf022 Mon Sep 17 00:00:00 2001 From: Kale-ab Date: Wed, 9 Jun 2021 13:03:06 +0200 Subject: [PATCH 188/188] docs: Updated docs to include package information. --- README.md | 8 ++++++++ mava/_metadata.py | 2 +- 2 files changed, 9 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 3b9bcdab5..0abe75606 100644 --- a/README.md +++ b/README.md @@ -2,6 +2,8 @@ # Mava: a research framework for distributed multi-agent reinforcement learning +![PyPI Python Version](https://img.shields.io/pypi/pyversions/id-mava) +![PyPI version](https://badge.fury.io/py/id-mava.svg) ![pytest](https://github.com/arnupretorius/mava/workflows/format_and_test/badge.svg) # Table of Contents @@ -178,6 +180,12 @@ We have tested `mava` on Python 3.6, 3.7 and 3.8. pip install id-mava[reverb] ``` + Or for nightly builds: + ```bash + pip install id-mava-nightly + pip install id-mava-nightly[reverb] + ``` + 3. To install dependencies for tensorflow agents: ```bash pip install id-mava[tf] diff --git a/mava/_metadata.py b/mava/_metadata.py index 478f3bdda..4500b8043 100644 --- a/mava/_metadata.py +++ b/mava/_metadata.py @@ -21,7 +21,7 @@ # We follow Semantic Versioning (https://semver.org/) _MAJOR_VERSION = "0" _MINOR_VERSION = "0" -_PATCH_VERSION = "1" +_PATCH_VERSION = "9" # Example: '0.4.2' __version__ = ".".join([_MAJOR_VERSION, _MINOR_VERSION, _PATCH_VERSION])

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