From 08718c5b9165773edca3be3d96556e678e9b74b1 Mon Sep 17 00:00:00 2001 From: root Date: Thu, 31 Oct 2024 02:52:38 +0000 Subject: [PATCH 01/59] first commit --- examples/puffer_vec_ppo_trash_pickup.py | 336 ++++++++++++++++++ .../environments/trash_pickup/__init__.py | 12 + .../environments/trash_pickup/environment.py | 248 +++++++++++++ 3 files changed, 596 insertions(+) create mode 100644 examples/puffer_vec_ppo_trash_pickup.py create mode 100644 pufferlib/environments/trash_pickup/__init__.py create mode 100644 pufferlib/environments/trash_pickup/environment.py diff --git a/examples/puffer_vec_ppo_trash_pickup.py b/examples/puffer_vec_ppo_trash_pickup.py new file mode 100644 index 00000000..2618d7d6 --- /dev/null +++ b/examples/puffer_vec_ppo_trash_pickup.py @@ -0,0 +1,336 @@ +# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/ppo/#ppo_ataripy +import os +import random +import time +from dataclasses import dataclass + +import gymnasium as gym +import numpy as np +import torch +import torch.nn as nn +import torch.optim as optim +import tyro +from torch.distributions.categorical import Categorical +from torch.utils.tensorboard import SummaryWriter + +import gym as old_gym +import shimmy + +import pufferlib.emulation +import pufferlib.pytorch +import pufferlib.vector +import pufferlib.environments.trash_pickup + + +@dataclass +class Args: + exp_name: str = os.path.basename(__file__)[: -len(".py")] + """the name of this experiment""" + seed: int = 1 + """seed of the experiment""" + torch_deterministic: bool = True + """if toggled, `torch.backends.cudnn.deterministic=False`""" + cuda: bool = True + """if toggled, cuda will be enabled by default""" + track: bool = False + """if toggled, this experiment will be tracked with Weights and Biases""" + wandb_project_name: str = "cleanRL" + """the wandb's project name""" + wandb_entity: str = None + """the entity (team) of wandb's project""" + capture_video: bool = False + """whether to capture videos of the agent performances (check out `videos` folder)""" + + # Algorithm specific arguments + env_id: str = "TrashPickup" + """the id of the environment""" + total_timesteps: int = 10000000 + """total timesteps of the experiments""" + learning_rate: float = 2.5e-4 + """the learning rate of the optimizer""" + num_envs: int = 48 + """the number of parallel game environments""" + num_steps: int = 128 + """the number of steps to run in each environment per policy rollout""" + anneal_lr: bool = True + """Toggle learning rate annealing for policy and value networks""" + gamma: float = 0.99 + """the discount factor gamma""" + gae_lambda: float = 0.95 + """the lambda for the general advantage estimation""" + num_minibatches: int = 4 + """the number of mini-batches""" + update_epochs: int = 4 + """the K epochs to update the policy""" + norm_adv: bool = True + """Toggles advantages normalization""" + clip_coef: float = 0.1 + """the surrogate clipping coefficient""" + clip_vloss: bool = True + """Toggles whether or not to use a clipped loss for the value function, as per the paper.""" + ent_coef: float = 0.01 + """coefficient of the entropy""" + vf_coef: float = 0.5 + """coefficient of the value function""" + max_grad_norm: float = 0.5 + """the maximum norm for the gradient clipping""" + target_kl: float = None + """the target KL divergence threshold""" + + # to be filled in runtime + batch_size: int = 0 + """the batch size (computed in runtime)""" + minibatch_size: int = 0 + """the mini-batch size (computed in runtime)""" + num_iterations: int = 0 + """the number of iterations (computed in runtime)""" + + +def layer_init(layer, std=np.sqrt(2), bias_const=0.0): + torch.nn.init.orthogonal_(layer.weight, std) + torch.nn.init.constant_(layer.bias, bias_const) + return layer + + +class Agent(nn.Module): + def __init__(self, emulated): + super().__init__() + self.dtype = pufferlib.pytorch.nativize_dtype(emulated) + + self.blstats_net = nn.Sequential( + nn.Embedding(256, 32), + nn.Flatten(), + ) + + self.char_embed = nn.Embedding(256, 32) + self.chars_net = nn.Sequential( + layer_init(nn.Conv2d(32, 32, 5, stride=(2, 3))), + nn.ReLU(), + layer_init(nn.Conv2d(32, 64, 5, stride=(1, 3))), + nn.ReLU(), + layer_init(nn.Conv2d(64, 64, 3, stride=1)), + nn.ReLU(), + nn.Flatten(), + ) + + self.proj = nn.Linear(864+960, 256) + self.actor = layer_init(nn.Linear(256, 8), std=0.01) + self.critic = layer_init(nn.Linear(256, 1), std=1) + + def hidden(self, x): + x = x.type(torch.uint8) # Undo bad cleanrl cast + x = pufferlib.pytorch.nativize_tensor(x, self.dtype) + + blstats = torch.clip(x['blstats'] + 1, 0, 255).int() + blstats = self.blstats_net(blstats) + + chars = self.char_embed(x['chars'].int()) + chars = torch.permute(chars, (0, 3, 1, 2)) + chars = self.chars_net(chars) + + concat = torch.cat([blstats, chars], dim=1) + return self.proj(concat) + + def get_value(self, x): + hidden = self.hidden(x) + return self.critic(hidden) + + def get_action_and_value(self, x, action=None): + hidden = self.hidden(x) + logits = self.actor(hidden) + probs = Categorical(logits=logits) + if action is None: + action = probs.sample() + return action, probs.log_prob(action), probs.entropy(), self.critic(hidden) + + +if __name__ == "__main__": + args = tyro.cli(Args) + args.batch_size = int(args.num_envs * args.num_steps) + args.minibatch_size = int(args.batch_size // args.num_minibatches) + args.num_iterations = args.total_timesteps // args.batch_size + run_name = f"{args.env_id}__{args.exp_name}__{args.seed}__{int(time.time())}" + if args.track: + import wandb + + wandb.init( + project=args.wandb_project_name, + entity=args.wandb_entity, + sync_tensorboard=True, + config=vars(args), + name=run_name, + monitor_gym=True, + save_code=True, + ) + writer = SummaryWriter(f"runs/{run_name}") + writer.add_text( + "hyperparameters", + "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key, value in vars(args).items()])), + ) + + # TRY NOT TO MODIFY: seeding + random.seed(args.seed) + np.random.seed(args.seed) + torch.manual_seed(args.seed) + torch.backends.cudnn.deterministic = args.torch_deterministic + + device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu") + + # PufferLib vectorization + envs = pufferlib.vector.make( + pufferlib.environments.trash_pickup.env_creator(), + backend=pufferlib.vector.Multiprocessing, + num_envs=args.num_envs, + num_workers=args.num_envs//2, + ) + + assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported" + + # ALGO Logic: Storage setup + obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device) + actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device) + logprobs = torch.zeros((args.num_steps, args.num_envs)).to(device) + rewards = torch.zeros((args.num_steps, args.num_envs)).to(device) + dones = torch.zeros((args.num_steps, args.num_envs)).to(device) + values = torch.zeros((args.num_steps, args.num_envs)).to(device) + + # Annoyance: AsyncVectorEnv does not have a driver env + agent = Agent(envs.driver_env.emulated).to(device) + optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5) + + # TRY NOT TO MODIFY: start the game + global_step = 0 + start_time = time.time() + next_obs, _ = envs.reset(seed=args.seed) + next_obs = torch.Tensor(next_obs).to(device) + next_done = torch.zeros(args.num_envs).to(device) + + for iteration in range(1, args.num_iterations + 1): + # Annealing the rate if instructed to do so. + if args.anneal_lr: + frac = 1.0 - (iteration - 1.0) / args.num_iterations + lrnow = frac * args.learning_rate + optimizer.param_groups[0]["lr"] = lrnow + + for step in range(0, args.num_steps): + global_step += args.num_envs + obs[step] = next_obs + dones[step] = next_done + + # ALGO LOGIC: action logic + with torch.no_grad(): + action, logprob, _, value = agent.get_action_and_value(next_obs) + values[step] = value.flatten() + actions[step] = action + logprobs[step] = logprob + + # TRY NOT TO MODIFY: execute the game and log data. + next_obs, reward, terminations, truncations, infos = envs.step(action.cpu().numpy()) + next_done = np.logical_or(terminations, truncations) + rewards[step] = torch.tensor(reward).to(device).view(-1) + next_obs = torch.as_tensor(next_obs, device=device) + next_done = torch.as_tensor(next_done, dtype=torch.float32, device=device) + + if "final_info" in infos: + for info in infos["final_info"]: + if info and "episode" in info: + print(f"global_step={global_step}, episodic_return={info['episode']['r']}") + writer.add_scalar("charts/episodic_return", info["episode"]["r"], global_step) + writer.add_scalar("charts/episodic_length", info["episode"]["l"], global_step) + + # bootstrap value if not done + with torch.no_grad(): + next_value = agent.get_value(next_obs).reshape(1, -1) + advantages = torch.zeros_like(rewards).to(device) + lastgaelam = 0 + for t in reversed(range(args.num_steps)): + if t == args.num_steps - 1: + nextnonterminal = 1.0 - next_done + nextvalues = next_value + else: + nextnonterminal = 1.0 - dones[t + 1] + nextvalues = values[t + 1] + delta = rewards[t] + args.gamma * nextvalues * nextnonterminal - values[t] + advantages[t] = lastgaelam = delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam + returns = advantages + values + + # flatten the batch + b_obs = obs.reshape((-1,) + envs.single_observation_space.shape) + b_logprobs = logprobs.reshape(-1) + b_actions = actions.reshape((-1,) + envs.single_action_space.shape) + b_advantages = advantages.reshape(-1) + b_returns = returns.reshape(-1) + b_values = values.reshape(-1) + + # Optimizing the policy and value network + b_inds = np.arange(args.batch_size) + clipfracs = [] + for epoch in range(args.update_epochs): + np.random.shuffle(b_inds) + for start in range(0, args.batch_size, args.minibatch_size): + end = start + args.minibatch_size + mb_inds = b_inds[start:end] + + _, newlogprob, entropy, newvalue = agent.get_action_and_value(b_obs[mb_inds], b_actions.long()[mb_inds]) + logratio = newlogprob - b_logprobs[mb_inds] + ratio = logratio.exp() + + with torch.no_grad(): + # calculate approx_kl http://joschu.net/blog/kl-approx.html + old_approx_kl = (-logratio).mean() + approx_kl = ((ratio - 1) - logratio).mean() + clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean().item()] + + mb_advantages = b_advantages[mb_inds] + if args.norm_adv: + mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8) + + # Policy loss + pg_loss1 = -mb_advantages * ratio + pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - args.clip_coef, 1 + args.clip_coef) + pg_loss = torch.max(pg_loss1, pg_loss2).mean() + + # Value loss + newvalue = newvalue.view(-1) + if args.clip_vloss: + v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2 + v_clipped = b_values[mb_inds] + torch.clamp( + newvalue - b_values[mb_inds], + -args.clip_coef, + args.clip_coef, + ) + v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2 + v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped) + v_loss = 0.5 * v_loss_max.mean() + else: + v_loss = 0.5 * ((newvalue - b_returns[mb_inds]) ** 2).mean() + + entropy_loss = entropy.mean() + loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef + + optimizer.zero_grad() + loss.backward() + nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm) + optimizer.step() + + if args.target_kl is not None and approx_kl > args.target_kl: + break + + y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy() + var_y = np.var(y_true) + explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y + + # TRY NOT TO MODIFY: record rewards for plotting purposes + writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], global_step) + writer.add_scalar("losses/value_loss", v_loss.item(), global_step) + writer.add_scalar("losses/policy_loss", pg_loss.item(), global_step) + writer.add_scalar("losses/entropy", entropy_loss.item(), global_step) + writer.add_scalar("losses/old_approx_kl", old_approx_kl.item(), global_step) + writer.add_scalar("losses/approx_kl", approx_kl.item(), global_step) + writer.add_scalar("losses/clipfrac", np.mean(clipfracs), global_step) + writer.add_scalar("losses/explained_variance", explained_var, global_step) + print("SPS:", int(global_step / (time.time() - start_time))) + writer.add_scalar("charts/SPS", int(global_step / (time.time() - start_time)), global_step) + + envs.close() + writer.close() diff --git a/pufferlib/environments/trash_pickup/__init__.py b/pufferlib/environments/trash_pickup/__init__.py new file mode 100644 index 00000000..3225820c --- /dev/null +++ b/pufferlib/environments/trash_pickup/__init__.py @@ -0,0 +1,12 @@ +from .environment import env_creator, make + +try: + import torch +except ImportError: + pass +else: + # from .torch import Policy + try: + from .torch import Recurrent + except: + Recurrent = None diff --git a/pufferlib/environments/trash_pickup/environment.py b/pufferlib/environments/trash_pickup/environment.py new file mode 100644 index 00000000..882a2f36 --- /dev/null +++ b/pufferlib/environments/trash_pickup/environment.py @@ -0,0 +1,248 @@ +import functools +import numpy as np +from pettingzoo import ParallelEnv +from gymnasium import spaces +from enum import Enum + +from pettingzoo.utils.conversions import aec_to_parallel_wrapper + +import pufferlib.emulation +import pufferlib.environments +import pufferlib.wrappers + +import pufferlib + +import pyray + +class GridState(Enum): + EMPTY = 0 + TRASH = 1 + TRASH_BIN = 2 + AGENT = 3 + +class TrashPickupEnv(ParallelEnv): + metadata = {'render_modes': ['human'], 'name': 'TrashPickup'} + + def __init__(self, grid_size=10, num_agents=2, num_trash=10, num_bins=2): + self.grid_size = grid_size + self._num_agents = num_agents # Changed here + self.num_trash = num_trash + self.num_bins = num_bins + + self.possible_agents = ["agent_" + str(i) for i in range(self._num_agents)] + self.agents = self.possible_agents[:] + + # Define the action space: 0 = UP, 1 = DOWN, 2 = LEFT, 3 = RIGHT + self.action_spaces = {agent: spaces.Discrete(4) for agent in self.possible_agents} + + # Observation space includes the agent's position, whether it's carrying trash, and the grid state + self.observation_spaces = { + agent: spaces.Dict({ + 'agent_position': spaces.Box(low=0, high=self.grid_size - 1, shape=(2,), dtype=np.int32), + 'carrying_trash': spaces.Discrete(2), # 0 or 1 + 'grid': spaces.Box(low=0, high=5, shape=(self.grid_size, self.grid_size), dtype=np.int32) + }) + for agent in self.possible_agents + } + + # Initialize rendering variables + self.window_initialized = False + self.cell_size = 40 # Size of each cell in pixels + + self.reset() + + def reset(self, seed=None, options=None): + # Initialize agents + self.agents = self.possible_agents[:] + self._agent_positions = {} + self._agent_carrying = {} + + # Initialize the grid + # 0: empty, 1: trash, 2: trash bin, 3: agent + self.grid = np.zeros((self.grid_size, self.grid_size), dtype=np.int32) + + # Place trash + trash_positions = self._random_positions(self.num_trash) + for pos in trash_positions: + self.grid[pos] = GridState.TRASH.value # Trash + + # Place bins + bin_positions = self._random_positions(self.num_bins, exclude=trash_positions) + for pos in bin_positions: + self.grid[pos] = GridState.TRASH_BIN.value # Trash bin + + # Place agents + agent_positions = self._random_positions(self._num_agents, exclude=trash_positions + bin_positions) # Changed here + for idx, agent in enumerate(self.agents): + pos = agent_positions[idx] + self._agent_positions[agent] = pos + self._agent_carrying[agent] = 0 + self.grid[pos] = GridState.AGENT.value # Agent + + observations = self._get_observations() + return observations + + def step(self, actions): + rewards = {agent: 0 for agent in self.agents} + dones = {agent: False for agent in self.agents} + truncations = {agent: False for agent in self.agents} + infos = {agent: {} for agent in self.agents} + + # Remove agents from the grid to prepare for movement + for agent in self.agents: + pos = self._agent_positions[agent] + self.grid[pos] = GridState.EMPTY.value + + # Process each agent's action + for agent in self.agents: + action = actions[agent] + current_pos = self._agent_positions[agent] + carrying = self._agent_carrying[agent] + + new_pos = self._move(current_pos, action) + x, y = new_pos + + # Check grid boundaries + if not (0 <= x < self.grid_size and 0 <= y < self.grid_size): + new_pos = current_pos + + this_grid_state = self.grid[new_pos] + + if this_grid_state == GridState.EMPTY.value: + # Empty cell: move agent + self._agent_positions[agent] = new_pos + elif this_grid_state == GridState.TRASH.value and carrying == 0: + # Pick up trash + self._agent_carrying[agent] = 1 + self.grid[new_pos] = GridState.EMPTY.value + self._agent_positions[agent] = new_pos + rewards[agent] += 1 # Reward for picking up trash + elif this_grid_state == GridState.TRASH_BIN.value: + if carrying == 1: + # Deposit trash + self._agent_carrying[agent] = 0 + rewards[agent] += 1 # Reward for depositing trash + # Agent does not move onto the bin + self._agent_positions[agent] = current_pos + else: + # Push bin if possible + bin_new_pos = self._move(new_pos, action) + bx, by = bin_new_pos + if (0 <= bx < self.grid_size and 0 <= by < self.grid_size) and self.grid[bin_new_pos] == GridState.EMPTY.value: + # Move bin + self.grid[new_pos] = GridState.EMPTY.value + self.grid[bin_new_pos] = GridState.TRASH_BIN.value + # Move agent into bin's old position + # self._agent_positions[agent] = new_pos # actually lets make this cost a step by not moving the agent + else: + # Bin cannot be moved + self._agent_positions[agent] = current_pos + else: + # Invalid move or occupied cell: stay in place + self._agent_positions[agent] = current_pos + + rewards[agent] -= 0.01 # small penalty per step to encourage efficiency + + # Update agents on the grid + for agent in self.agents: + pos = self._agent_positions[agent] + self.grid[pos] = GridState.AGENT.value # Agent + + observations = self._get_observations() + return observations, rewards, dones, truncations, infos + + def render(self): + # Initialize window if not already initialized + if not self.window_initialized: + window_size = self.grid_size * self.cell_size + pyray.init_window(window_size, window_size, "Trash Pickup Environment") + self.window_initialized = True + + # Check if window should close + if pyray.window_should_close(): + self.close() + return + + pyray.begin_drawing() + pyray.clear_background(pyray.WHITE) + + # Draw grid cells + for x in range(self.grid_size): + for y in range(self.grid_size): + cell_value = self.grid[x, y] + screen_x = x * self.cell_size + screen_y = y * self.cell_size + + rect = pyray.Rectangle(screen_x, screen_y, self.cell_size, self.cell_size) + + # Draw cell background + pyray.draw_rectangle_lines(int(rect.x), int(rect.y), int(rect.width), int(rect.height), pyray.LIGHTGRAY) + + # Draw cell contents + if cell_value == GridState.EMPTY.value: + pass # Empty cell + elif cell_value == GridState.TRASH.value: + # Draw trash as a brown square + pyray.draw_rectangle(int(rect.x + self.cell_size * 0.25), int(rect.y + self.cell_size * 0.25), + int(self.cell_size * 0.5), int(self.cell_size * 0.5), pyray.BROWN) + elif cell_value == GridState.TRASH_BIN.value: + # Draw trash bin as a blue square + pyray.draw_rectangle(int(rect.x + self.cell_size * 0.1), int(rect.y + self.cell_size * 0.1), + int(self.cell_size * 0.8), int(self.cell_size * 0.8), pyray.BLUE) + elif cell_value == GridState.AGENT.value: + # Draw agent as a red circle + pyray.draw_circle(int(rect.x + self.cell_size / 2), int(rect.y + self.cell_size / 2), + self.cell_size * 0.4, pyray.RED) + + pyray.end_drawing() + + def close(self): + pass + + def _move(self, position, action): + # Map actions to grid movements + x, y = position + if action == 0: # UP + return (x, y + 1) + elif action == 1: # DOWN + return (x, y - 1) + elif action == 2: # LEFT + return (x - 1, y) + elif action == 3: # RIGHT + return (x + 1, y) + else: + raise ValueError(f"TrashPickup Env - Unexpected Move Action Received: {action}") + + def _random_positions(self, count, exclude=[]): + # Generate unique random positions on the grid + positions = [] + while len(positions) < count: + pos = (np.random.randint(0, self.grid_size), np.random.randint(0, self.grid_size)) + if pos not in positions and pos not in exclude: + positions.append(pos) + return positions + + def _get_observations(self): + # Provide observations for each agent + observations = {} + for agent in self.agents: + obs = { + 'agent_position': np.array(self._agent_positions[agent], dtype=np.int32), + 'carrying_trash': np.array(self._agent_carrying[agent], dtype=np.int32), + 'grid': self.grid.copy() + } + observations[agent] = obs + return observations + + def state(self): + # Return the global state (optional) + return self.grid.copy() + +def env_creator(): + return functools.partial(make) + +def make(): + env = TrashPickupEnv() + # env = aec_to_parallel_wrapper(env) + env = pufferlib.wrappers.PettingZooTruncatedWrapper(env) + return pufferlib.emulation.PettingZooPufferEnv(env=env) From 520473c57d458f4c49991113e70747f88ad45700 Mon Sep 17 00:00:00 2001 From: root Date: Sat, 2 Nov 2024 22:56:14 +0000 Subject: [PATCH 02/59] successfully training env --- examples/puffer_vec_ppo_trash_pickup.py | 148 ++++++++++++------ .../environments/trash_pickup/environment.py | 56 +++++-- 2 files changed, 143 insertions(+), 61 deletions(-) diff --git a/examples/puffer_vec_ppo_trash_pickup.py b/examples/puffer_vec_ppo_trash_pickup.py index 2618d7d6..1dbf77c0 100644 --- a/examples/puffer_vec_ppo_trash_pickup.py +++ b/examples/puffer_vec_ppo_trash_pickup.py @@ -3,6 +3,9 @@ import random import time from dataclasses import dataclass +from copy import deepcopy + +from tqdm import tqdm import gymnasium as gym import numpy as np @@ -38,17 +41,17 @@ class Args: """the wandb's project name""" wandb_entity: str = None """the entity (team) of wandb's project""" - capture_video: bool = False + capture_video: bool = True """whether to capture videos of the agent performances (check out `videos` folder)""" # Algorithm specific arguments env_id: str = "TrashPickup" """the id of the environment""" - total_timesteps: int = 10000000 + total_timesteps: int = 1000000000 """total timesteps of the experiments""" learning_rate: float = 2.5e-4 """the learning rate of the optimizer""" - num_envs: int = 48 + num_envs: int = 16 """the number of parallel game environments""" num_steps: int = 128 """the number of steps to run in each environment per policy rollout""" @@ -97,38 +100,58 @@ def __init__(self, emulated): super().__init__() self.dtype = pufferlib.pytorch.nativize_dtype(emulated) - self.blstats_net = nn.Sequential( - nn.Embedding(256, 32), + # Define network for processing the grid with multiple channels (4 channels for 0, 1, 2, 3) + self.grid_net = nn.Sequential( + layer_init(nn.Conv2d(4, 32, 3, stride=1)), # Now assuming grid has 4 channels + nn.ReLU(), + layer_init(nn.Conv2d(32, 64, 3, stride=1)), + nn.ReLU(), nn.Flatten(), ) - self.char_embed = nn.Embedding(256, 32) - self.chars_net = nn.Sequential( - layer_init(nn.Conv2d(32, 32, 5, stride=(2, 3))), - nn.ReLU(), - layer_init(nn.Conv2d(32, 64, 5, stride=(1, 3))), + # Linear layers for agent position and carrying status + self.position_net = nn.Sequential( + layer_init(nn.Linear(2, 32)), nn.ReLU(), - layer_init(nn.Conv2d(64, 64, 3, stride=1)), + ) + + self.carrying_net = nn.Sequential( + layer_init(nn.Linear(1, 32)), nn.ReLU(), - nn.Flatten(), ) - self.proj = nn.Linear(864+960, 256) - self.actor = layer_init(nn.Linear(256, 8), std=0.01) + # Combine outputs + self.proj = nn.Linear(2368, 256) # (2368) hard-coded for now, fix later Adjusted projection layer size based on concatenated features + self.actor = layer_init(nn.Linear(256, 4), std=0.01) # 4 is hard-coded for now (represents number of actions) self.critic = layer_init(nn.Linear(256, 1), std=1) + def preprocess_grid(self, grid): + # Separate grid into 4 channels for empty space, trash, bin, and agent + empty_space = (grid == 0).float() + trash = (grid == 1).float() + trash_bin = (grid == 2).float() + agent = (grid == 3).float() + + # Stack channels to create a (4, H, W) tensor + ret = torch.stack([empty_space, trash, trash_bin, agent], dim=1) + return ret + def hidden(self, x): - x = x.type(torch.uint8) # Undo bad cleanrl cast + x = x.type(torch.uint8) # Undo bad cleanrl cast x = pufferlib.pytorch.nativize_tensor(x, self.dtype) - blstats = torch.clip(x['blstats'] + 1, 0, 255).int() - blstats = self.blstats_net(blstats) + # Process each part of the observation + grid = self.preprocess_grid(x['grid']) + grid_features = self.grid_net(grid) - chars = self.char_embed(x['chars'].int()) - chars = torch.permute(chars, (0, 3, 1, 2)) - chars = self.chars_net(chars) + position = x['agent_position'].float() / 10 # divide by 10 (grid-size) hard-coded for now... + position_features = self.position_net(position) - concat = torch.cat([blstats, chars], dim=1) + carrying = x['carrying_trash'].float() + carrying_features = self.carrying_net(carrying) + + # Concatenate all features and project to a fixed-size hidden representation + concat = torch.cat([grid_features, position_features, carrying_features], dim=1) return self.proj(concat) def get_value(self, x): @@ -175,24 +198,28 @@ def get_action_and_value(self, x, action=None): torch.backends.cudnn.deterministic = args.torch_deterministic device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu") + torch.set_printoptions(threshold=torch.inf) # PufferLib vectorization envs = pufferlib.vector.make( pufferlib.environments.trash_pickup.env_creator(), backend=pufferlib.vector.Multiprocessing, num_envs=args.num_envs, - num_workers=args.num_envs//2, + num_workers=args.num_envs, # // 2 ) assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported" # ALGO Logic: Storage setup - obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device) - actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device) - logprobs = torch.zeros((args.num_steps, args.num_envs)).to(device) - rewards = torch.zeros((args.num_steps, args.num_envs)).to(device) - dones = torch.zeros((args.num_steps, args.num_envs)).to(device) - values = torch.zeros((args.num_steps, args.num_envs)).to(device) + num_agents = 3 # hard-coded for now, adjust this later + obs = torch.zeros((args.num_steps, args.num_envs, num_agents) + envs.single_observation_space.shape).to(device) + actions = torch.zeros((args.num_steps, args.num_envs, num_agents) + envs.single_action_space.shape).to(device) + logprobs = torch.zeros((args.num_steps, args.num_envs, num_agents)).to(device) + rewards = torch.zeros((args.num_steps, args.num_envs, num_agents)).to(device) + dones = torch.zeros((args.num_steps, args.num_envs, num_agents)).to(device) + values = torch.zeros((args.num_steps, args.num_envs, num_agents)).to(device) + + # dummy_obs = torch.zeros((args.num_steps, envs.single_observation_space.shape[0])).to(device) # Annoyance: AsyncVectorEnv does not have a driver env agent = Agent(envs.driver_env.emulated).to(device) @@ -201,11 +228,10 @@ def get_action_and_value(self, x, action=None): # TRY NOT TO MODIFY: start the game global_step = 0 start_time = time.time() - next_obs, _ = envs.reset(seed=args.seed) - next_obs = torch.Tensor(next_obs).to(device) - next_done = torch.zeros(args.num_envs).to(device) + next_obs, _ = envs.reset(seed=args.seed) # returns tensor of shape ([6, 120]), but we need it of shape ([3, 2, 120]) + next_done = torch.zeros((args.num_envs, num_agents)).to(device) - for iteration in range(1, args.num_iterations + 1): + for iteration in tqdm(range(1, args.num_iterations + 1)): # Annealing the rate if instructed to do so. if args.anneal_lr: frac = 1.0 - (iteration - 1.0) / args.num_iterations @@ -213,30 +239,47 @@ def get_action_and_value(self, x, action=None): optimizer.param_groups[0]["lr"] = lrnow for step in range(0, args.num_steps): - global_step += args.num_envs - obs[step] = next_obs - dones[step] = next_done + global_step += args.num_envs * num_agents + obs[step] = torch.Tensor(next_obs.reshape(args.num_envs, num_agents, envs.single_observation_space.shape[0])).to(device) # error here + dones[step] = torch.Tensor(next_done.reshape(args.num_envs, num_agents)).to(device) + + # dummy = next_obs.reshape(args.num_envs, num_agents, envs.single_observation_space.shape[0]) + # dummy_obs[step] = torch.Tensor(dummy[0][0]).to(device) # ALGO LOGIC: action logic with torch.no_grad(): - action, logprob, _, value = agent.get_action_and_value(next_obs) - values[step] = value.flatten() - actions[step] = action - logprobs[step] = logprob + action, logprob, _, value = agent.get_action_and_value(torch.Tensor(next_obs).to(device)) + values[step] = value.reshape(args.num_envs, num_agents) + actions[step] = action.reshape(args.num_envs, num_agents) + logprobs[step] = logprob.reshape(args.num_envs, num_agents) # TRY NOT TO MODIFY: execute the game and log data. next_obs, reward, terminations, truncations, infos = envs.step(action.cpu().numpy()) next_done = np.logical_or(terminations, truncations) - rewards[step] = torch.tensor(reward).to(device).view(-1) + rewards[step] = torch.tensor(reward).to(device).view(args.num_envs, num_agents) next_obs = torch.as_tensor(next_obs, device=device) next_done = torch.as_tensor(next_done, dtype=torch.float32, device=device) + + writer.add_scalar(f"charts/mean-reward", rewards[step].mean(), global_step) + + episode_reward_list = [] + episode_length_list = [] + trash_remaining_list = [] + for info in infos: + if "final_info" in info: + episode_reward_list.append(info["final_info"]["episode_reward"]) + episode_length_list.append(info["final_info"]["episode_length"]) + trash_remaining_list.append(info["final_info"]["trash_remaining"]) + + if len(episode_reward_list) > 0: + writer.add_scalar("charts/episodic_reward", np.mean(episode_reward_list), global_step) + + if len(episode_length_list) > 0: + writer.add_scalar("charts/episodic_length", np.mean(episode_length_list), global_step) + + if len(trash_remaining_list) > 0: + writer.add_scalar("charts/total_trash_remaining", np.mean(trash_remaining_list), global_step) - if "final_info" in infos: - for info in infos["final_info"]: - if info and "episode" in info: - print(f"global_step={global_step}, episodic_return={info['episode']['r']}") - writer.add_scalar("charts/episodic_return", info["episode"]["r"], global_step) - writer.add_scalar("charts/episodic_length", info["episode"]["l"], global_step) # bootstrap value if not done with torch.no_grad(): @@ -248,10 +291,12 @@ def get_action_and_value(self, x, action=None): nextnonterminal = 1.0 - next_done nextvalues = next_value else: - nextnonterminal = 1.0 - dones[t + 1] - nextvalues = values[t + 1] - delta = rewards[t] + args.gamma * nextvalues * nextnonterminal - values[t] - advantages[t] = lastgaelam = delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam + nextnonterminal = 1.0 - dones[t + 1].reshape(args.num_envs * num_agents) + nextvalues = values[t + 1].reshape(args.num_envs * num_agents) + delta = rewards[t].reshape(args.num_envs * num_agents) + args.gamma * nextvalues * nextnonterminal - values[t].reshape(args.num_envs * num_agents) + if not isinstance(lastgaelam, int): + lastgaelam = lastgaelam.reshape(args.num_envs * num_agents) + advantages[t] = lastgaelam = (delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam).reshape(args.num_envs, num_agents) returns = advantages + values # flatten the batch @@ -329,7 +374,8 @@ def get_action_and_value(self, x, action=None): writer.add_scalar("losses/approx_kl", approx_kl.item(), global_step) writer.add_scalar("losses/clipfrac", np.mean(clipfracs), global_step) writer.add_scalar("losses/explained_variance", explained_var, global_step) - print("SPS:", int(global_step / (time.time() - start_time))) + # if iteration % 5 == 0: + # print(f"SPS: {int(global_step / (time.time() - start_time))} | Current Iteration: {iteration} out of {args.num_iterations}") writer.add_scalar("charts/SPS", int(global_step / (time.time() - start_time)), global_step) envs.close() diff --git a/pufferlib/environments/trash_pickup/environment.py b/pufferlib/environments/trash_pickup/environment.py index 882a2f36..49354d69 100644 --- a/pufferlib/environments/trash_pickup/environment.py +++ b/pufferlib/environments/trash_pickup/environment.py @@ -4,8 +4,6 @@ from gymnasium import spaces from enum import Enum -from pettingzoo.utils.conversions import aec_to_parallel_wrapper - import pufferlib.emulation import pufferlib.environments import pufferlib.wrappers @@ -23,15 +21,23 @@ class GridState(Enum): class TrashPickupEnv(ParallelEnv): metadata = {'render_modes': ['human'], 'name': 'TrashPickup'} - def __init__(self, grid_size=10, num_agents=2, num_trash=10, num_bins=2): + def __init__(self, grid_size=10, num_agents=3, num_trash=15, num_bins=2, max_steps=300): self.grid_size = grid_size - self._num_agents = num_agents # Changed here + self._num_agents = num_agents self.num_trash = num_trash self.num_bins = num_bins self.possible_agents = ["agent_" + str(i) for i in range(self._num_agents)] self.agents = self.possible_agents[:] + self.max_steps = max_steps + self.current_step = 0 + + self.total_episode_reward = 0 + + self.POSITIVE_REWARD = 0.5 / num_trash # this makes the total episode reward +1 if all trash is picked up and put into bin + self.NEGATIVE_REWARD = 1 / (max_steps * num_agents) # this makes the max negative reward given per episode to be -1 + # Define the action space: 0 = UP, 1 = DOWN, 2 = LEFT, 3 = RIGHT self.action_spaces = {agent: spaces.Discrete(4) for agent in self.possible_agents} @@ -40,7 +46,7 @@ def __init__(self, grid_size=10, num_agents=2, num_trash=10, num_bins=2): agent: spaces.Dict({ 'agent_position': spaces.Box(low=0, high=self.grid_size - 1, shape=(2,), dtype=np.int32), 'carrying_trash': spaces.Discrete(2), # 0 or 1 - 'grid': spaces.Box(low=0, high=5, shape=(self.grid_size, self.grid_size), dtype=np.int32) + 'grid': spaces.Box(low=0, high=4, shape=(self.grid_size, self.grid_size), dtype=np.int32) }) for agent in self.possible_agents } @@ -52,6 +58,9 @@ def __init__(self, grid_size=10, num_agents=2, num_trash=10, num_bins=2): self.reset() def reset(self, seed=None, options=None): + self.current_step = 0 + self.total_episode_reward = 0 + # Initialize agents self.agents = self.possible_agents[:] self._agent_positions = {} @@ -83,9 +92,13 @@ def reset(self, seed=None, options=None): return observations def step(self, actions): + + if self.current_step > self.max_steps: + print("TrashPickup Env, environment did NOT reset after max steps reached...") + return + rewards = {agent: 0 for agent in self.agents} dones = {agent: False for agent in self.agents} - truncations = {agent: False for agent in self.agents} infos = {agent: {} for agent in self.agents} # Remove agents from the grid to prepare for movement @@ -116,12 +129,14 @@ def step(self, actions): self._agent_carrying[agent] = 1 self.grid[new_pos] = GridState.EMPTY.value self._agent_positions[agent] = new_pos - rewards[agent] += 1 # Reward for picking up trash + rewards[agent] += self.POSITIVE_REWARD # Reward for picking up trash + self.total_episode_reward += self.POSITIVE_REWARD elif this_grid_state == GridState.TRASH_BIN.value: if carrying == 1: # Deposit trash self._agent_carrying[agent] = 0 - rewards[agent] += 1 # Reward for depositing trash + rewards[agent] += self.POSITIVE_REWARD # Reward for depositing trash + self.total_episode_reward += self.POSITIVE_REWARD # Agent does not move onto the bin self._agent_positions[agent] = current_pos else: @@ -141,15 +156,26 @@ def step(self, actions): # Invalid move or occupied cell: stay in place self._agent_positions[agent] = current_pos - rewards[agent] -= 0.01 # small penalty per step to encourage efficiency + rewards[agent] -= self.NEGATIVE_REWARD # small penalty per step to encourage efficiency + self.total_episode_reward -= self.NEGATIVE_REWARD # Update agents on the grid for agent in self.agents: pos = self._agent_positions[agent] self.grid[pos] = GridState.AGENT.value # Agent + if self.current_step >= self.max_steps or self.is_episode_over(): + dones = {agent: True for agent in self.agents} + infos["final_info"] = { + "episode_reward": self.total_episode_reward, + "episode_length": self.current_step, + "trash_remaining": np.sum(self.grid == GridState.TRASH.value), + "any_agent_carrying": all(carrying == 0 for carrying in self._agent_carrying.values()), + } + self.current_step += 1 + observations = self._get_observations() - return observations, rewards, dones, truncations, infos + return observations, rewards, dones, infos def render(self): # Initialize window if not already initialized @@ -196,6 +222,16 @@ def render(self): pyray.end_drawing() + def is_episode_over(self): + # Check if there are any trash cells remaining in the grid + no_trash_left = not np.any(self.grid == GridState.TRASH.value) + + # Check if no agents are carrying trash + no_agent_carrying = all(carrying == 0 for carrying in self._agent_carrying.values()) + + # The episode is over if there's no trash left and no agents are carrying trash + return no_trash_left and no_agent_carrying + def close(self): pass From df12494d789afa9f9002f69cfec9db05f507be46 Mon Sep 17 00:00:00 2001 From: root Date: Tue, 5 Nov 2024 00:55:16 +0000 Subject: [PATCH 03/59] moving towards c implementation of env --- examples/puffer_vec_ppo_trash_pickup.py | 8 +- .../environments/trash_pickup/__init__.py | 1 + .../trash_pickup/cy_trash_pickup.pyx | 91 ++++++ .../environments/trash_pickup/environment.py | 258 ++-------------- .../trash_pickup/old_environment.py | 284 ++++++++++++++++++ pufferlib/environments/trash_pickup/setup.py | 17 ++ .../environments/trash_pickup/trash_pickup.c | 239 +++++++++++++++ .../environments/trash_pickup/trash_pickup.h | 50 +++ 8 files changed, 709 insertions(+), 239 deletions(-) create mode 100644 pufferlib/environments/trash_pickup/cy_trash_pickup.pyx create mode 100644 pufferlib/environments/trash_pickup/old_environment.py create mode 100644 pufferlib/environments/trash_pickup/setup.py create mode 100644 pufferlib/environments/trash_pickup/trash_pickup.c create mode 100644 pufferlib/environments/trash_pickup/trash_pickup.h diff --git a/examples/puffer_vec_ppo_trash_pickup.py b/examples/puffer_vec_ppo_trash_pickup.py index 1dbf77c0..eaa29517 100644 --- a/examples/puffer_vec_ppo_trash_pickup.py +++ b/examples/puffer_vec_ppo_trash_pickup.py @@ -47,13 +47,13 @@ class Args: # Algorithm specific arguments env_id: str = "TrashPickup" """the id of the environment""" - total_timesteps: int = 1000000000 + total_timesteps: int = 1_000_000_000 """total timesteps of the experiments""" - learning_rate: float = 2.5e-4 + learning_rate: float = 5e-5 """the learning rate of the optimizer""" - num_envs: int = 16 + num_envs: int = 2 """the number of parallel game environments""" - num_steps: int = 128 + num_steps: int = 1024 """the number of steps to run in each environment per policy rollout""" anneal_lr: bool = True """Toggle learning rate annealing for policy and value networks""" diff --git a/pufferlib/environments/trash_pickup/__init__.py b/pufferlib/environments/trash_pickup/__init__.py index 3225820c..51a69b3a 100644 --- a/pufferlib/environments/trash_pickup/__init__.py +++ b/pufferlib/environments/trash_pickup/__init__.py @@ -1,3 +1,4 @@ +# from .old_environment import env_creator, make from .environment import env_creator, make try: diff --git a/pufferlib/environments/trash_pickup/cy_trash_pickup.pyx b/pufferlib/environments/trash_pickup/cy_trash_pickup.pyx new file mode 100644 index 00000000..d5f65619 --- /dev/null +++ b/pufferlib/environments/trash_pickup/cy_trash_pickup.pyx @@ -0,0 +1,91 @@ +cimport numpy as cnp +from libc.stdlib cimport malloc, free # Import malloc and free +from libc.stdint cimport uint64_t +import os + +cdef extern from "trash_pickup.h": + ctypedef struct CTrashPickupEnv: + float* observations + int* actions + float* rewards + unsigned char* dones + + void initialize_env(CTrashPickupEnv* env, int grid_size, int num_agents, int num_trash, int num_bins, int max_steps) + void reset_env(CTrashPickupEnv* env) + void step_env(CTrashPickupEnv* env) + bint is_episode_over(CTrashPickupEnv* env) + void get_observations(CTrashPickupEnv* env) + void free_env(CTrashPickupEnv* env) + +cdef class CyTrashPickupEnv: + cdef CTrashPickupEnv* env + cdef int obs_size + cdef int num_agents + + def __init__(self, int grid_size=10, int num_agents=3, int num_trash=15, int num_bins=2, int max_steps=300): + # Allocate memory for the CTrashPickupEnv struct + self.env = malloc(sizeof(CTrashPickupEnv)) + if self.env == NULL: + raise MemoryError("Failed to allocate memory for CTrashPickupEnv") + + initialize_env(self.env, grid_size, num_agents, num_trash, num_bins, max_steps) + self.num_agents = num_agents + + # Calculate observation size + self.obs_size = 3 + grid_size * grid_size + + # Allocate memory for observations, actions, rewards, and dones arrays in the env struct + cdef int total_obs_size = self.num_agents * self.obs_size + self.env.observations = malloc(total_obs_size * sizeof(float)) + self.env.actions = malloc(self.num_agents * sizeof(int)) + self.env.rewards = malloc(self.num_agents * sizeof(float)) + self.env.dones = malloc(self.num_agents * sizeof(unsigned char)) + + if not (self.env.observations and self.env.actions and self.env.rewards and self.env.dones): + self.__dealloc__() # Free allocated memory + raise MemoryError("Failed to allocate memory for environment arrays") + + def reset(self): + reset_env(self.env) + return self.get_observations() + + def step(self, actions): + # Set actions + cdef int i + for i in range(self.num_agents): + self.env.actions[i] = actions[i] + + # Step the environment + step_env(self.env) + + observations = self.get_observations() + rewards = [self.env.rewards[i] for i in range(self.num_agents)] + dones = [bool(self.env.dones[i]) for i in range(self.num_agents)] + infos = [{} for _ in range(self.num_agents)] # You can add additional info if needed + + return observations, rewards, dones, infos + + def get_observations(self): + # Return observations as a list of dicts for each agent + observations = {} + cdef int i, j + for i in range(self.num_agents): + obs = {} + idx = i * self.obs_size + obs['agent_position'] = [self.env.observations[idx], self.env.observations[idx + 1]] + obs['carrying_trash'] = self.env.observations[idx + 2] + grid_data = [] + for j in range(idx + 3, idx + self.obs_size): + grid_data.append(self.env.observations[j]) + obs['grid'] = grid_data # You may need to reshape this into a 2D array + observations[f"agent-{i}"] = obs + return observations + + def __dealloc__(self): + if self.env is not NULL: + free_env(self.env) + free(self.env.observations) + free(self.env.actions) + free(self.env.rewards) + free(self.env.dones) + free(self.env) diff --git a/pufferlib/environments/trash_pickup/environment.py b/pufferlib/environments/trash_pickup/environment.py index 49354d69..18e4711c 100644 --- a/pufferlib/environments/trash_pickup/environment.py +++ b/pufferlib/environments/trash_pickup/environment.py @@ -1,44 +1,35 @@ import functools import numpy as np -from pettingzoo import ParallelEnv +import gymnasium as gym from gymnasium import spaces -from enum import Enum +from pettingzoo import ParallelEnv +from .cy_trash_pickup import CyTrashPickupEnv +import pufferlib import pufferlib.emulation import pufferlib.environments import pufferlib.wrappers -import pufferlib - -import pyray -class GridState(Enum): - EMPTY = 0 - TRASH = 1 - TRASH_BIN = 2 - AGENT = 3 class TrashPickupEnv(ParallelEnv): - metadata = {'render_modes': ['human'], 'name': 'TrashPickup'} - def __init__(self, grid_size=10, num_agents=3, num_trash=15, num_bins=2, max_steps=300): self.grid_size = grid_size - self._num_agents = num_agents self.num_trash = num_trash self.num_bins = num_bins - - self.possible_agents = ["agent_" + str(i) for i in range(self._num_agents)] - self.agents = self.possible_agents[:] - self.max_steps = max_steps - self.current_step = 0 - self.total_episode_reward = 0 + # self.single_observation_space = gym.spaces.Dict({ + # 'agent_position': gym.spaces.Box(low=0, high=grid_size - 1, shape=(2,), dtype=np.float32), + # 'carrying_trash': gym.spaces.Discrete(2), + # 'grid': gym.spaces.Box(low=0, high=4, shape=(grid_size, grid_size), dtype=np.float32), + # }) + # self.single_action_space = gym.spaces.Discrete(4) - self.POSITIVE_REWARD = 0.5 / num_trash # this makes the total episode reward +1 if all trash is picked up and put into bin - self.NEGATIVE_REWARD = 1 / (max_steps * num_agents) # this makes the max negative reward given per episode to be -1 + self._num_agents = num_agents + self.possible_agents = ["agent_" + str(i) for i in range(self._num_agents)] - # Define the action space: 0 = UP, 1 = DOWN, 2 = LEFT, 3 = RIGHT + # Define the action space self.action_spaces = {agent: spaces.Discrete(4) for agent in self.possible_agents} # Observation space includes the agent's position, whether it's carrying trash, and the grid state @@ -51,228 +42,25 @@ def __init__(self, grid_size=10, num_agents=3, num_trash=15, num_bins=2, max_ste for agent in self.possible_agents } - # Initialize rendering variables - self.window_initialized = False - self.cell_size = 40 # Size of each cell in pixels - - self.reset() - - def reset(self, seed=None, options=None): - self.current_step = 0 - self.total_episode_reward = 0 - - # Initialize agents - self.agents = self.possible_agents[:] - self._agent_positions = {} - self._agent_carrying = {} - - # Initialize the grid - # 0: empty, 1: trash, 2: trash bin, 3: agent - self.grid = np.zeros((self.grid_size, self.grid_size), dtype=np.int32) - - # Place trash - trash_positions = self._random_positions(self.num_trash) - for pos in trash_positions: - self.grid[pos] = GridState.TRASH.value # Trash + - # Place bins - bin_positions = self._random_positions(self.num_bins, exclude=trash_positions) - for pos in bin_positions: - self.grid[pos] = GridState.TRASH_BIN.value # Trash bin + super().__init__() + self.env = CyTrashPickupEnv(grid_size, num_agents, num_trash, num_bins, max_steps) - # Place agents - agent_positions = self._random_positions(self._num_agents, exclude=trash_positions + bin_positions) # Changed here - for idx, agent in enumerate(self.agents): - pos = agent_positions[idx] - self._agent_positions[agent] = pos - self._agent_carrying[agent] = 0 - self.grid[pos] = GridState.AGENT.value # Agent - - observations = self._get_observations() + def reset(self, seed=None): + observations = self.env.reset() return observations def step(self, actions): - - if self.current_step > self.max_steps: - print("TrashPickup Env, environment did NOT reset after max steps reached...") - return - - rewards = {agent: 0 for agent in self.agents} - dones = {agent: False for agent in self.agents} - infos = {agent: {} for agent in self.agents} - - # Remove agents from the grid to prepare for movement - for agent in self.agents: - pos = self._agent_positions[agent] - self.grid[pos] = GridState.EMPTY.value - - # Process each agent's action - for agent in self.agents: - action = actions[agent] - current_pos = self._agent_positions[agent] - carrying = self._agent_carrying[agent] - - new_pos = self._move(current_pos, action) - x, y = new_pos - - # Check grid boundaries - if not (0 <= x < self.grid_size and 0 <= y < self.grid_size): - new_pos = current_pos - - this_grid_state = self.grid[new_pos] - - if this_grid_state == GridState.EMPTY.value: - # Empty cell: move agent - self._agent_positions[agent] = new_pos - elif this_grid_state == GridState.TRASH.value and carrying == 0: - # Pick up trash - self._agent_carrying[agent] = 1 - self.grid[new_pos] = GridState.EMPTY.value - self._agent_positions[agent] = new_pos - rewards[agent] += self.POSITIVE_REWARD # Reward for picking up trash - self.total_episode_reward += self.POSITIVE_REWARD - elif this_grid_state == GridState.TRASH_BIN.value: - if carrying == 1: - # Deposit trash - self._agent_carrying[agent] = 0 - rewards[agent] += self.POSITIVE_REWARD # Reward for depositing trash - self.total_episode_reward += self.POSITIVE_REWARD - # Agent does not move onto the bin - self._agent_positions[agent] = current_pos - else: - # Push bin if possible - bin_new_pos = self._move(new_pos, action) - bx, by = bin_new_pos - if (0 <= bx < self.grid_size and 0 <= by < self.grid_size) and self.grid[bin_new_pos] == GridState.EMPTY.value: - # Move bin - self.grid[new_pos] = GridState.EMPTY.value - self.grid[bin_new_pos] = GridState.TRASH_BIN.value - # Move agent into bin's old position - # self._agent_positions[agent] = new_pos # actually lets make this cost a step by not moving the agent - else: - # Bin cannot be moved - self._agent_positions[agent] = current_pos - else: - # Invalid move or occupied cell: stay in place - self._agent_positions[agent] = current_pos - - rewards[agent] -= self.NEGATIVE_REWARD # small penalty per step to encourage efficiency - self.total_episode_reward -= self.NEGATIVE_REWARD - - # Update agents on the grid - for agent in self.agents: - pos = self._agent_positions[agent] - self.grid[pos] = GridState.AGENT.value # Agent - - if self.current_step >= self.max_steps or self.is_episode_over(): - dones = {agent: True for agent in self.agents} - infos["final_info"] = { - "episode_reward": self.total_episode_reward, - "episode_length": self.current_step, - "trash_remaining": np.sum(self.grid == GridState.TRASH.value), - "any_agent_carrying": all(carrying == 0 for carrying in self._agent_carrying.values()), - } - self.current_step += 1 - - observations = self._get_observations() + observations, rewards, dones, infos = self.env.step(actions) return observations, rewards, dones, infos def render(self): - # Initialize window if not already initialized - if not self.window_initialized: - window_size = self.grid_size * self.cell_size - pyray.init_window(window_size, window_size, "Trash Pickup Environment") - self.window_initialized = True - - # Check if window should close - if pyray.window_should_close(): - self.close() - return - - pyray.begin_drawing() - pyray.clear_background(pyray.WHITE) - - # Draw grid cells - for x in range(self.grid_size): - for y in range(self.grid_size): - cell_value = self.grid[x, y] - screen_x = x * self.cell_size - screen_y = y * self.cell_size - - rect = pyray.Rectangle(screen_x, screen_y, self.cell_size, self.cell_size) - - # Draw cell background - pyray.draw_rectangle_lines(int(rect.x), int(rect.y), int(rect.width), int(rect.height), pyray.LIGHTGRAY) - - # Draw cell contents - if cell_value == GridState.EMPTY.value: - pass # Empty cell - elif cell_value == GridState.TRASH.value: - # Draw trash as a brown square - pyray.draw_rectangle(int(rect.x + self.cell_size * 0.25), int(rect.y + self.cell_size * 0.25), - int(self.cell_size * 0.5), int(self.cell_size * 0.5), pyray.BROWN) - elif cell_value == GridState.TRASH_BIN.value: - # Draw trash bin as a blue square - pyray.draw_rectangle(int(rect.x + self.cell_size * 0.1), int(rect.y + self.cell_size * 0.1), - int(self.cell_size * 0.8), int(self.cell_size * 0.8), pyray.BLUE) - elif cell_value == GridState.AGENT.value: - # Draw agent as a red circle - pyray.draw_circle(int(rect.x + self.cell_size / 2), int(rect.y + self.cell_size / 2), - self.cell_size * 0.4, pyray.RED) - - pyray.end_drawing() - - def is_episode_over(self): - # Check if there are any trash cells remaining in the grid - no_trash_left = not np.any(self.grid == GridState.TRASH.value) - - # Check if no agents are carrying trash - no_agent_carrying = all(carrying == 0 for carrying in self._agent_carrying.values()) - - # The episode is over if there's no trash left and no agents are carrying trash - return no_trash_left and no_agent_carrying - - def close(self): + # If you want to implement rendering, you can do it here pass - def _move(self, position, action): - # Map actions to grid movements - x, y = position - if action == 0: # UP - return (x, y + 1) - elif action == 1: # DOWN - return (x, y - 1) - elif action == 2: # LEFT - return (x - 1, y) - elif action == 3: # RIGHT - return (x + 1, y) - else: - raise ValueError(f"TrashPickup Env - Unexpected Move Action Received: {action}") - - def _random_positions(self, count, exclude=[]): - # Generate unique random positions on the grid - positions = [] - while len(positions) < count: - pos = (np.random.randint(0, self.grid_size), np.random.randint(0, self.grid_size)) - if pos not in positions and pos not in exclude: - positions.append(pos) - return positions - - def _get_observations(self): - # Provide observations for each agent - observations = {} - for agent in self.agents: - obs = { - 'agent_position': np.array(self._agent_positions[agent], dtype=np.int32), - 'carrying_trash': np.array(self._agent_carrying[agent], dtype=np.int32), - 'grid': self.grid.copy() - } - observations[agent] = obs - return observations - - def state(self): - # Return the global state (optional) - return self.grid.copy() + def close(self): + self.env = None def env_creator(): return functools.partial(make) @@ -281,4 +69,4 @@ def make(): env = TrashPickupEnv() # env = aec_to_parallel_wrapper(env) env = pufferlib.wrappers.PettingZooTruncatedWrapper(env) - return pufferlib.emulation.PettingZooPufferEnv(env=env) + return pufferlib.emulation.PettingZooPufferEnv(env=env) \ No newline at end of file diff --git a/pufferlib/environments/trash_pickup/old_environment.py b/pufferlib/environments/trash_pickup/old_environment.py new file mode 100644 index 00000000..9efd8747 --- /dev/null +++ b/pufferlib/environments/trash_pickup/old_environment.py @@ -0,0 +1,284 @@ +import functools +import numpy as np +from pettingzoo import ParallelEnv +from gymnasium import spaces +from enum import Enum + +import pufferlib.emulation +import pufferlib.environments +import pufferlib.wrappers + +import pufferlib + +import pyray + +class GridState(Enum): + EMPTY = 0 + TRASH = 1 + TRASH_BIN = 2 + AGENT = 3 + +class TrashPickupOldEnv(ParallelEnv): + metadata = {'render_modes': ['human'], 'name': 'TrashPickupOld'} + + def __init__(self, grid_size=10, num_agents=3, num_trash=15, num_bins=2, max_steps=300): + self.grid_size = grid_size + self._num_agents = num_agents + self.num_trash = num_trash + self.num_bins = num_bins + + self.possible_agents = ["agent_" + str(i) for i in range(self._num_agents)] + self.agents = self.possible_agents[:] + + self.max_steps = max_steps + self.current_step = 0 + + self.total_episode_reward = 0 + + self.POSITIVE_REWARD = 0.5 / num_trash # this makes the total episode reward +1 if all trash is picked up and put into bin + self.NEGATIVE_REWARD = 1 / (max_steps * num_agents) # this makes the max negative reward given per episode to be -1 + + # Define the action space: 0 = UP, 1 = DOWN, 2 = LEFT, 3 = RIGHT + self.action_spaces = {agent: spaces.Discrete(4) for agent in self.possible_agents} + + # Observation space includes the agent's position, whether it's carrying trash, and the grid state + self.observation_spaces = { + agent: spaces.Dict({ + 'agent_position': spaces.Box(low=0, high=self.grid_size - 1, shape=(2,), dtype=np.int32), + 'carrying_trash': spaces.Discrete(2), # 0 or 1 + 'grid': spaces.Box(low=0, high=4, shape=(self.grid_size, self.grid_size), dtype=np.int32) + }) + for agent in self.possible_agents + } + + # Initialize rendering variables + self.window_initialized = False + self.cell_size = 40 # Size of each cell in pixels + + self.reset() + + def reset(self, seed=None, options=None): + self.current_step = 0 + self.total_episode_reward = 0 + + # Initialize agents + self.agents = self.possible_agents[:] + self._agent_positions = {} + self._agent_carrying = {} + + # Initialize the grid + # 0: empty, 1: trash, 2: trash bin, 3: agent + self.grid = np.zeros((self.grid_size, self.grid_size), dtype=np.int32) + + # Place trash + trash_positions = self._random_positions(self.num_trash) + for pos in trash_positions: + self.grid[pos] = GridState.TRASH.value # Trash + + # Place bins + bin_positions = self._random_positions(self.num_bins, exclude=trash_positions) + for pos in bin_positions: + self.grid[pos] = GridState.TRASH_BIN.value # Trash bin + + # Place agents + agent_positions = self._random_positions(self._num_agents, exclude=trash_positions + bin_positions) # Changed here + for idx, agent in enumerate(self.agents): + pos = agent_positions[idx] + self._agent_positions[agent] = pos + self._agent_carrying[agent] = 0 + self.grid[pos] = GridState.AGENT.value # Agent + + observations = self._get_observations() + return observations + + def step(self, actions): + + if self.current_step > self.max_steps: + print("TrashPickupOldEnv, environment did NOT reset after max steps reached...") + return + + rewards = {agent: 0 for agent in self.agents} + dones = {agent: False for agent in self.agents} + infos = {agent: {} for agent in self.agents} + + # Remove agents from the grid to prepare for movement + for agent in self.agents: + pos = self._agent_positions[agent] + self.grid[pos] = GridState.EMPTY.value + + # Process each agent's action + for agent in self.agents: + action = actions[agent] + current_pos = self._agent_positions[agent] + carrying = self._agent_carrying[agent] + + new_pos = self._move(current_pos, action) + x, y = new_pos + + # Check grid boundaries + if not (0 <= x < self.grid_size and 0 <= y < self.grid_size): + new_pos = current_pos + + this_grid_state = self.grid[new_pos] + + if this_grid_state == GridState.EMPTY.value: + # Empty cell: move agent + self._agent_positions[agent] = new_pos + elif this_grid_state == GridState.TRASH.value and carrying == 0: + # Pick up trash + self._agent_carrying[agent] = 1 + self.grid[new_pos] = GridState.EMPTY.value + self._agent_positions[agent] = new_pos + rewards[agent] += self.POSITIVE_REWARD # Reward for picking up trash + self.total_episode_reward += self.POSITIVE_REWARD + elif this_grid_state == GridState.TRASH_BIN.value: + if carrying == 1: + # Deposit trash + self._agent_carrying[agent] = 0 + rewards[agent] += self.POSITIVE_REWARD # Reward for depositing trash + self.total_episode_reward += self.POSITIVE_REWARD + # Agent does not move onto the bin + self._agent_positions[agent] = current_pos + else: + # Push bin if possible + bin_new_pos = self._move(new_pos, action) + bx, by = bin_new_pos + if (0 <= bx < self.grid_size and 0 <= by < self.grid_size) and self.grid[bin_new_pos] == GridState.EMPTY.value: + # Move bin + self.grid[new_pos] = GridState.EMPTY.value + self.grid[bin_new_pos] = GridState.TRASH_BIN.value + # Move agent into bin's old position + # self._agent_positions[agent] = new_pos # actually lets make this cost a step by not moving the agent + else: + # Bin cannot be moved + self._agent_positions[agent] = current_pos + else: + # Invalid move or occupied cell: stay in place + self._agent_positions[agent] = current_pos + + rewards[agent] -= self.NEGATIVE_REWARD # small penalty per step to encourage efficiency + self.total_episode_reward -= self.NEGATIVE_REWARD + + # Update agents on the grid + for agent in self.agents: + pos = self._agent_positions[agent] + self.grid[pos] = GridState.AGENT.value # Agent + + if self.current_step >= self.max_steps or self.is_episode_over(): + dones = {agent: True for agent in self.agents} + infos["final_info"] = { + "episode_reward": self.total_episode_reward, + "episode_length": self.current_step, + "trash_remaining": np.sum(self.grid == GridState.TRASH.value), + "any_agent_carrying": all(carrying == 0 for carrying in self._agent_carrying.values()), + } + self.current_step += 1 + + observations = self._get_observations() + return observations, rewards, dones, infos + + def render(self): + # Initialize window if not already initialized + if not self.window_initialized: + window_size = self.grid_size * self.cell_size + pyray.init_window(window_size, window_size, "Trash Pickup Environment") + self.window_initialized = True + + # Check if window should close + if pyray.window_should_close(): + self.close() + return + + pyray.begin_drawing() + pyray.clear_background(pyray.WHITE) + + # Draw grid cells + for x in range(self.grid_size): + for y in range(self.grid_size): + cell_value = self.grid[x, y] + screen_x = x * self.cell_size + screen_y = y * self.cell_size + + rect = pyray.Rectangle(screen_x, screen_y, self.cell_size, self.cell_size) + + # Draw cell background + pyray.draw_rectangle_lines(int(rect.x), int(rect.y), int(rect.width), int(rect.height), pyray.LIGHTGRAY) + + # Draw cell contents + if cell_value == GridState.EMPTY.value: + pass # Empty cell + elif cell_value == GridState.TRASH.value: + # Draw trash as a brown square + pyray.draw_rectangle(int(rect.x + self.cell_size * 0.25), int(rect.y + self.cell_size * 0.25), + int(self.cell_size * 0.5), int(self.cell_size * 0.5), pyray.BROWN) + elif cell_value == GridState.TRASH_BIN.value: + # Draw trash bin as a blue square + pyray.draw_rectangle(int(rect.x + self.cell_size * 0.1), int(rect.y + self.cell_size * 0.1), + int(self.cell_size * 0.8), int(self.cell_size * 0.8), pyray.BLUE) + elif cell_value == GridState.AGENT.value: + # Draw agent as a red circle + pyray.draw_circle(int(rect.x + self.cell_size / 2), int(rect.y + self.cell_size / 2), + self.cell_size * 0.4, pyray.RED) + + pyray.end_drawing() + + def is_episode_over(self): + # Check if there are any trash cells remaining in the grid + no_trash_left = not np.any(self.grid == GridState.TRASH.value) + + # Check if no agents are carrying trash + no_agent_carrying = all(carrying == 0 for carrying in self._agent_carrying.values()) + + # The episode is over if there's no trash left and no agents are carrying trash + return no_trash_left and no_agent_carrying + + def close(self): + pass + + def _move(self, position, action): + # Map actions to grid movements + x, y = position + if action == 0: # UP + return (x, y + 1) + elif action == 1: # DOWN + return (x, y - 1) + elif action == 2: # LEFT + return (x - 1, y) + elif action == 3: # RIGHT + return (x + 1, y) + else: + raise ValueError(f"TrashPickupOldEnv - Unexpected Move Action Received: {action}") + + def _random_positions(self, count, exclude=[]): + # Generate unique random positions on the grid + positions = [] + while len(positions) < count: + pos = (np.random.randint(0, self.grid_size), np.random.randint(0, self.grid_size)) + if pos not in positions and pos not in exclude: + positions.append(pos) + return positions + + def _get_observations(self): + # Provide observations for each agent + observations = {} + for agent in self.agents: + obs = { + 'agent_position': np.array(self._agent_positions[agent], dtype=np.int32), + 'carrying_trash': np.array(self._agent_carrying[agent], dtype=np.int32), + 'grid': self.grid.copy() + } + observations[agent] = obs + return observations + + def state(self): + # Return the global state (optional) + return self.grid.copy() + +def env_creator(): + return functools.partial(make) + +def make(): + env = TrashPickupOldEnv() + # env = aec_to_parallel_wrapper(env) + env = pufferlib.wrappers.PettingZooTruncatedWrapper(env) + return pufferlib.emulation.PettingZooPufferEnv(env=env) diff --git a/pufferlib/environments/trash_pickup/setup.py b/pufferlib/environments/trash_pickup/setup.py new file mode 100644 index 00000000..9526e046 --- /dev/null +++ b/pufferlib/environments/trash_pickup/setup.py @@ -0,0 +1,17 @@ +from setuptools import setup, Extension +from Cython.Build import cythonize +import numpy as np + +extensions = [ + Extension( + name="cy_trash_pickup", + sources=["cy_trash_pickup.pyx", "trash_pickup.c"], + include_dirs=[np.get_include(), "."], # Include NumPy headers + language="c", + ), +] + +setup( + name="cy_trash_pickup", + ext_modules=cythonize(extensions) +) diff --git a/pufferlib/environments/trash_pickup/trash_pickup.c b/pufferlib/environments/trash_pickup/trash_pickup.c new file mode 100644 index 00000000..415b7054 --- /dev/null +++ b/pufferlib/environments/trash_pickup/trash_pickup.c @@ -0,0 +1,239 @@ +#include "trash_pickup.h" + +// Helper function prototypes +static void place_random_items(CTrashPickupEnv* env, int count, int item_type); +static void move_agent(CTrashPickupEnv* env, int agent_idx, int action); + +// Initialize the environment with dynamic memory allocation +void initialize_env(CTrashPickupEnv* env, int grid_size, int num_agents, int num_trash, int num_bins, int max_steps) { + env->grid_size = grid_size; + env->num_agents = num_agents; + env->num_trash = num_trash; + env->num_bins = num_bins; + env->max_steps = max_steps; + env->current_step = 0; + + env->positive_reward = 0.5f / num_trash; // This makes it so that +1 is the total reward if all trash is picked up + env->negative_reward = 1.0f / (max_steps * num_agents); // This makes it so that -1 is the total reward if max steps is reached + env->total_episode_reward = 0.0f; + + // Allocate memory for grid + env->grid = (int**)malloc(grid_size * sizeof(int*)); + for (int i = 0; i < grid_size; i++) { + env->grid[i] = (int*)malloc(grid_size * sizeof(int)); + } + + // Allocate memory for agent positions and carrying states + env->agent_positions = (int(*)[2])malloc(num_agents * sizeof(int[2])); + env->agent_carrying = (int*)malloc(num_agents * sizeof(int)); + + // Allocate memory for PufferLib interface arrays + env->observations = (float*)malloc(num_agents * (3 + grid_size * grid_size) * sizeof(float)); + env->actions = (int*)malloc(num_agents * sizeof(int)); + env->rewards = (float*)malloc(num_agents * sizeof(float)); + env->dones = (unsigned char*)malloc(num_agents * sizeof(unsigned char)); + + reset_env(env); +} + +// Reset the environment +void reset_env(CTrashPickupEnv* env) { + env->current_step = 0; + env->total_episode_reward = 0.0f; + + // Clear grid + for (int x = 0; x < env->grid_size; x++) { + for (int y = 0; y < env->grid_size; y++) { + env->grid[x][y] = EMPTY; + } + } + + // Place trash + place_random_items(env, env->num_trash, TRASH); + + // Place bins + place_random_items(env, env->num_bins, TRASH_BIN); + + // Place agents + place_random_items(env, env->num_agents, AGENT); + for (int i = 0; i < env->num_agents; i++) { + env->agent_carrying[i] = 0; + } + + get_observations(env); +} + +// Check if the episode is over +bool is_episode_over(CTrashPickupEnv* env) { + // Check if all trash is collected and no agents are carrying trash + bool no_trash_left = true; + for (int x = 0; x < env->grid_size; x++) { + for (int y = 0; y < env->grid_size; y++) { + if (env->grid[x][y] == TRASH) { + no_trash_left = false; + break; + } + } + if (!no_trash_left) break; + } + + bool no_agent_carrying = true; + for (int i = 0; i < env->num_agents; i++) { + if (env->agent_carrying[i] != 0) { + no_agent_carrying = false; + break; + } + } + + return no_trash_left && no_agent_carrying; +} + +// Step the environment +void step_env(CTrashPickupEnv* env) { + // Reset rewards and dones for each agent at the start of the step + for (int i = 0; i < env->num_agents; i++) { + env->rewards[i] = 0.0f; + env->dones[i] = 0; + } + + // Process each agent's action + for (int i = 0; i < env->num_agents; i++) { + move_agent(env, i, env->actions[i]); + } + + // Update the step count and check if the episode is over + env->current_step++; + if (env->current_step >= env->max_steps || is_episode_over(env)) { + for (int i = 0; i < env->num_agents; i++) { + env->dones[i] = 1; + } + } + + // Update observations after each step + get_observations(env); +} + +// Get observations for each agent +void get_observations(CTrashPickupEnv* env) { + int obs_idx = 0; + for (int i = 0; i < env->num_agents; i++) { + // Agent's position + env->observations[obs_idx++] = env->agent_positions[i][0]; + env->observations[obs_idx++] = env->agent_positions[i][1]; + + // Whether the agent is carrying trash + env->observations[obs_idx++] = env->agent_carrying[i]; + + // Flattened grid representation + for (int x = 0; x < env->grid_size; x++) { + for (int y = 0; y < env->grid_size; y++) { + env->observations[obs_idx++] = (float)env->grid[x][y]; + } + } + } +} + +// Free dynamically allocated memory in the environment +void free_env(CTrashPickupEnv* env) { + // Free grid memory + for (int i = 0; i < env->grid_size; i++) { + free(env->grid[i]); + } + free(env->grid); + + // Free agent memory + free(env->agent_positions); + free(env->agent_carrying); + + // Free PufferLib interface arrays + free(env->observations); + free(env->actions); + free(env->rewards); + free(env->dones); +} + +// Helper functions + +static void place_random_items(CTrashPickupEnv* env, int count, int item_type) { + int placed = 0; + while (placed < count) { + int x = rand() % env->grid_size; + int y = rand() % env->grid_size; + + // Check if position is empty + if (env->grid[x][y] == EMPTY) { + env->grid[x][y] = item_type; + if (item_type == AGENT) { + int agent_idx = placed; + env->agent_positions[agent_idx][0] = x; + env->agent_positions[agent_idx][1] = y; + } + placed++; + } + } +} + +static void move_agent(CTrashPickupEnv* env, int agent_idx, int action) { + int x = env->agent_positions[agent_idx][0]; + int y = env->agent_positions[agent_idx][1]; + int carrying = env->agent_carrying[agent_idx]; + + int new_x = x; + int new_y = y; + + // Map actions to movements + if (action == 0) { // UP + new_y -= 1; + } else if (action == 1) { // DOWN + new_y += 1; + } else if (action == 2) { // LEFT + new_x -= 1; + } else if (action == 3) { // RIGHT + new_x += 1; + } + + // Check grid boundaries + if (new_x < 0 || new_x >= env->grid_size || new_y < 0 || new_y >= env->grid_size) { + new_x = x; + new_y = y; + } + + int cell_state = env->grid[new_x][new_y]; + + if (cell_state == EMPTY) { + // Move agent + env->agent_positions[agent_idx][0] = new_x; + env->agent_positions[agent_idx][1] = new_y; + } else if (cell_state == TRASH && carrying == 0) { + // Pick up trash + env->agent_carrying[agent_idx] = 1; + env->grid[new_x][new_y] = EMPTY; + env->agent_positions[agent_idx][0] = new_x; + env->agent_positions[agent_idx][1] = new_y; + env->rewards[agent_idx] += env->positive_reward; + env->total_episode_reward += env->positive_reward; + } else if (cell_state == TRASH_BIN) { + if (carrying == 1) { + // Deposit trash + env->agent_carrying[agent_idx] = 0; + env->rewards[agent_idx] += env->positive_reward; + env->total_episode_reward += env->positive_reward; + // Agent does not move onto the bin + } else { + // Attempt to push bin + // Check if the space after the bin is within bounds and empty + if (new_x >= 0 && new_x < env->grid_size && new_y >= 0 && new_y < env->grid_size && env->grid[new_x][new_y] == EMPTY) + { + // Move bin + env->grid[x][y] = EMPTY; + env->grid[new_x][new_y] = TRASH_BIN; + } + } + } else { + // Invalid move or occupied cell: stay in place + } + + // Apply negative reward to encourage time / step efficiency + env->rewards[agent_idx] -= env->negative_reward; + env->total_episode_reward -= env->negative_reward; +} diff --git a/pufferlib/environments/trash_pickup/trash_pickup.h b/pufferlib/environments/trash_pickup/trash_pickup.h new file mode 100644 index 00000000..74bb16e0 --- /dev/null +++ b/pufferlib/environments/trash_pickup/trash_pickup.h @@ -0,0 +1,50 @@ +#ifndef TRASH_PICKUP_H +#define TRASH_PICKUP_H + +#include +#include +#include + +typedef enum { + EMPTY = 0, + TRASH = 1, + TRASH_BIN = 2, + AGENT = 3 +} GridState; + +typedef struct { + int grid_size; + int num_agents; + int num_trash; + int num_bins; + int max_steps; + int current_step; + + float positive_reward; + float negative_reward; + float total_episode_reward; + + // Dynamic grid allocation + int** grid; + + // Agent positions and states + int (*agent_positions)[2]; + int* agent_carrying; + + // PufferLib interface arrays + float* observations; + int* actions; + float* rewards; + unsigned char* dones; + +} CTrashPickupEnv; + +// Function declarations +void initialize_env(CTrashPickupEnv* env, int grid_size, int num_agents, int num_trash, int num_bins, int max_steps); +void reset_env(CTrashPickupEnv* env); +void step_env(CTrashPickupEnv* env); +bool is_episode_over(CTrashPickupEnv* env); +void get_observations(CTrashPickupEnv* env); +void free_env(CTrashPickupEnv* env); + +#endif // TRASH_PICKUP_H From a69c7eae031128809cf83b0269ed38e02e643232 Mon Sep 17 00:00:00 2001 From: root Date: Wed, 6 Nov 2024 04:37:07 +0000 Subject: [PATCH 04/59] updated main script to handle more run_modes and other refactors --- examples/puffer_vec_ppo_trash_pickup.py | 478 +++++++++++------- .../environments/trash_pickup/__init__.py | 4 +- .../{environment.py => cy_environment.py} | 29 +- .../trash_pickup/cy_trash_pickup.pyx | 4 +- .../{old_environment.py => py_environment.py} | 26 +- 5 files changed, 347 insertions(+), 194 deletions(-) rename pufferlib/environments/trash_pickup/{environment.py => cy_environment.py} (67%) rename pufferlib/environments/trash_pickup/{old_environment.py => py_environment.py} (95%) diff --git a/examples/puffer_vec_ppo_trash_pickup.py b/examples/puffer_vec_ppo_trash_pickup.py index eaa29517..9ae6f7c5 100644 --- a/examples/puffer_vec_ppo_trash_pickup.py +++ b/examples/puffer_vec_ppo_trash_pickup.py @@ -41,19 +41,27 @@ class Args: """the wandb's project name""" wandb_entity: str = None """the entity (team) of wandb's project""" - capture_video: bool = True - """whether to capture videos of the agent performances (check out `videos` folder)""" + run_mode: str = "train" + """'train' to train a new model and save it, 'evaluate' to load an existing model and test performance, 'video' to load an existing model and create a video""" + model_save_path: str = './trash_pickup_saves/trash_pickup_model' + """Path to save model to this directory (file extension is automatically added, do NOT include it)""" + model_load_path: str = None + """Path to load in existing model to continue training, perform evaluate, or create a video""" + model_save_interval: float = 1 + """How (roughly) often to save the model in minutes""" + num_eval_episodes: int = 10 + """Number of evaluation episodes to run (only for run_mode == 'evaluation')""" # Algorithm specific arguments env_id: str = "TrashPickup" """the id of the environment""" - total_timesteps: int = 1_000_000_000 + total_timesteps: int = 250_000_000 """total timesteps of the experiments""" - learning_rate: float = 5e-5 + learning_rate: float = 5e-4 """the learning rate of the optimizer""" num_envs: int = 2 """the number of parallel game environments""" - num_steps: int = 1024 + num_steps: int = 512 """the number of steps to run in each environment per policy rollout""" anneal_lr: bool = True """Toggle learning rate annealing for policy and value networks""" @@ -80,13 +88,19 @@ class Args: target_kl: float = None """the target KL divergence threshold""" - # to be filled in runtime - batch_size: int = 0 - """the batch size (computed in runtime)""" - minibatch_size: int = 0 - """the mini-batch size (computed in runtime)""" - num_iterations: int = 0 - """the number of iterations (computed in runtime)""" + # Environment Specific Variables + num_agents: int = 3 + """The number of agents in the environment""" + grid_size: int = 10 + """The square size of the environment""" + num_trash: int = 15 + """The amount of 'trash' to be picked up by the agents in the environment""" + num_bins: int = 2 + """The number of 'trash bins' to put 'trash' picked up by the agents into in the environment""" + num_max_env_steps: int = 300 + """The maximum number of steps that can be taken in the environment before the episode automatically ends""" + + def layer_init(layer, std=np.sqrt(2), bias_const=0.0): @@ -100,13 +114,31 @@ def __init__(self, emulated): super().__init__() self.dtype = pufferlib.pytorch.nativize_dtype(emulated) + def get_conv_output_size(input_shape): + """Calculates the size of the output from the convolution layers.""" + dummy_input = torch.zeros(1, *input_shape) # Batch size of 1 with given input shape + output = self.conv2(self.conv1(dummy_input)) # Pass through conv layers + return int(np.prod(output.shape[1:])) # Flatten dimensions, except batch + # Define network for processing the grid with multiple channels (4 channels for 0, 1, 2, 3) + input_channels = 4 + + # Define the convolutional layers + self.conv1 = layer_init(nn.Conv2d(input_channels, 32, 3, stride=1)) + self.conv2 = layer_init(nn.Conv2d(32, 64, 3, stride=1)) + + # Calculate the output size of the conv layers + conv_output_size = get_conv_output_size((input_channels, args.grid_size, args.grid_size)) + + self.grid_net = nn.Sequential( - layer_init(nn.Conv2d(4, 32, 3, stride=1)), # Now assuming grid has 4 channels + self.conv1, nn.ReLU(), - layer_init(nn.Conv2d(32, 64, 3, stride=1)), + self.conv2, nn.ReLU(), nn.Flatten(), + layer_init(nn.Linear(conv_output_size, 32)), + nn.ReLU(), ) # Linear layers for agent position and carrying status @@ -121,9 +153,9 @@ def __init__(self, emulated): ) # Combine outputs - self.proj = nn.Linear(2368, 256) # (2368) hard-coded for now, fix later Adjusted projection layer size based on concatenated features - self.actor = layer_init(nn.Linear(256, 4), std=0.01) # 4 is hard-coded for now (represents number of actions) - self.critic = layer_init(nn.Linear(256, 1), std=1) + self.proj = nn.Linear(32 + 32 + 32, 256) # Adjusted projection layer size based on concatenated features + self.actor = layer_init(nn.Linear(256, 4), std=0.01) # 4 represents the number of possible actions + self.critic = layer_init(nn.Linear(256, 1), std=1) # 1 represents the value of the critic network (or sometimes called value network) def preprocess_grid(self, grid): # Separate grid into 4 channels for empty space, trash, bin, and agent @@ -169,9 +201,9 @@ def get_action_and_value(self, x, action=None): if __name__ == "__main__": args = tyro.cli(Args) - args.batch_size = int(args.num_envs * args.num_steps) - args.minibatch_size = int(args.batch_size // args.num_minibatches) - args.num_iterations = args.total_timesteps // args.batch_size + batch_size = int(args.num_envs * args.num_steps) + minibatch_size = int(batch_size // args.num_minibatches) + num_iterations = args.total_timesteps // batch_size run_name = f"{args.env_id}__{args.exp_name}__{args.seed}__{int(time.time())}" if args.track: import wandb @@ -185,6 +217,7 @@ def get_action_and_value(self, x, action=None): monitor_gym=True, save_code=True, ) + writer = SummaryWriter(f"runs/{run_name}") writer.add_text( "hyperparameters", @@ -200,18 +233,29 @@ def get_action_and_value(self, x, action=None): device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu") torch.set_printoptions(threshold=torch.inf) + num_agents = args.num_agents + + if args.run_mode == "video" and args.num_envs != 1: + print(f"run_mode set to video, but num_envs is {args.num_envs}, changing num_envs to just 1") + args.num_envs = 1 + # PufferLib vectorization envs = pufferlib.vector.make( - pufferlib.environments.trash_pickup.env_creator(), + pufferlib.environments.trash_pickup.env_creator( + grid_size=args.grid_size, + num_agents=num_agents, + num_trash=args.num_trash, + num_bins=args.num_bins, + max_steps=args.num_max_env_steps + ), backend=pufferlib.vector.Multiprocessing, num_envs=args.num_envs, - num_workers=args.num_envs, # // 2 + num_workers=args.num_envs, # num_workers == args.num_envs must be true else an error will occur ) assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported" # ALGO Logic: Storage setup - num_agents = 3 # hard-coded for now, adjust this later obs = torch.zeros((args.num_steps, args.num_envs, num_agents) + envs.single_observation_space.shape).to(device) actions = torch.zeros((args.num_steps, args.num_envs, num_agents) + envs.single_action_space.shape).to(device) logprobs = torch.zeros((args.num_steps, args.num_envs, num_agents)).to(device) @@ -219,164 +263,240 @@ def get_action_and_value(self, x, action=None): dones = torch.zeros((args.num_steps, args.num_envs, num_agents)).to(device) values = torch.zeros((args.num_steps, args.num_envs, num_agents)).to(device) - # dummy_obs = torch.zeros((args.num_steps, envs.single_observation_space.shape[0])).to(device) - - # Annoyance: AsyncVectorEnv does not have a driver env - agent = Agent(envs.driver_env.emulated).to(device) - optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5) - - # TRY NOT TO MODIFY: start the game - global_step = 0 - start_time = time.time() - next_obs, _ = envs.reset(seed=args.seed) # returns tensor of shape ([6, 120]), but we need it of shape ([3, 2, 120]) - next_done = torch.zeros((args.num_envs, num_agents)).to(device) - - for iteration in tqdm(range(1, args.num_iterations + 1)): - # Annealing the rate if instructed to do so. - if args.anneal_lr: - frac = 1.0 - (iteration - 1.0) / args.num_iterations - lrnow = frac * args.learning_rate - optimizer.param_groups[0]["lr"] = lrnow - - for step in range(0, args.num_steps): - global_step += args.num_envs * num_agents - obs[step] = torch.Tensor(next_obs.reshape(args.num_envs, num_agents, envs.single_observation_space.shape[0])).to(device) # error here - dones[step] = torch.Tensor(next_done.reshape(args.num_envs, num_agents)).to(device) - - # dummy = next_obs.reshape(args.num_envs, num_agents, envs.single_observation_space.shape[0]) - # dummy_obs[step] = torch.Tensor(dummy[0][0]).to(device) - - # ALGO LOGIC: action logic + model_path = args.model_load_path if args.model_load_path else args.model_save_path + if model_path and os.path.exists(model_path): + print(f"Loading model from {model_path}...") + agent = torch.load(model_path, map_location=device) + else: + print("Initializing new model...") + # Annoyance: AsyncVectorEnv does not have a driver env + agent = Agent(envs.driver_env.emulated).to(device) + + if args.run_mode == "train": + optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5) + + # TRY NOT TO MODIFY: start the game + global_step = 0 + start_time = time.time() + next_obs, _ = envs.reset(seed=args.seed) # returns tensor of shape ([6, 120]), but we need it of shape ([3, 2, 120]) + next_done = torch.zeros((args.num_envs, num_agents)).to(device) + + # List to keep track of saved models + saved_models = [] + last_save_time = time.time() + + for iteration in tqdm(range(1, num_iterations + 1)): + # Annealing the rate if instructed to do so. + if args.anneal_lr: + frac = 1.0 - (iteration - 1.0) / num_iterations + lrnow = frac * args.learning_rate + optimizer.param_groups[0]["lr"] = lrnow + + for step in range(0, args.num_steps): + global_step += args.num_envs * num_agents + obs[step] = torch.Tensor(next_obs.reshape(args.num_envs, num_agents, envs.single_observation_space.shape[0])).to(device) # error here + dones[step] = torch.Tensor(next_done.reshape(args.num_envs, num_agents)).to(device) + + # ALGO LOGIC: action logic + with torch.no_grad(): + action, logprob, _, value = agent.get_action_and_value(torch.Tensor(next_obs).to(device)) + values[step] = value.reshape(args.num_envs, num_agents) + actions[step] = action.reshape(args.num_envs, num_agents) + logprobs[step] = logprob.reshape(args.num_envs, num_agents) + + # TRY NOT TO MODIFY: execute the game and log data. + next_obs, reward, terminations, truncations, infos = envs.step(action.cpu().numpy()) + next_done = np.logical_or(terminations, truncations) + rewards[step] = torch.tensor(reward).to(device).view(args.num_envs, num_agents) + next_obs = torch.as_tensor(next_obs, device=device) + next_done = torch.as_tensor(next_done, dtype=torch.float32, device=device) + + writer.add_scalar(f"charts/mean-reward", rewards[step].mean(), global_step) + + episode_reward_list = [] + episode_length_list = [] + trash_remaining_list = [] + for info in infos: + if "final_info" in info: + episode_reward_list.append(info["final_info"]["episode_reward"]) + episode_length_list.append(info["final_info"]["episode_length"]) + trash_remaining_list.append(info["final_info"]["trash_remaining"]) + + if len(episode_reward_list) > 0: + writer.add_scalar("charts/episodic_reward", np.mean(episode_reward_list), global_step) + + if len(episode_length_list) > 0: + writer.add_scalar("charts/episodic_length", np.mean(episode_length_list), global_step) + + if len(trash_remaining_list) > 0: + writer.add_scalar("charts/total_trash_remaining", np.mean(trash_remaining_list), global_step) + + + # bootstrap value if not done with torch.no_grad(): - action, logprob, _, value = agent.get_action_and_value(torch.Tensor(next_obs).to(device)) - values[step] = value.reshape(args.num_envs, num_agents) - actions[step] = action.reshape(args.num_envs, num_agents) - logprobs[step] = logprob.reshape(args.num_envs, num_agents) - - # TRY NOT TO MODIFY: execute the game and log data. - next_obs, reward, terminations, truncations, infos = envs.step(action.cpu().numpy()) - next_done = np.logical_or(terminations, truncations) - rewards[step] = torch.tensor(reward).to(device).view(args.num_envs, num_agents) - next_obs = torch.as_tensor(next_obs, device=device) - next_done = torch.as_tensor(next_done, dtype=torch.float32, device=device) - - writer.add_scalar(f"charts/mean-reward", rewards[step].mean(), global_step) - - episode_reward_list = [] - episode_length_list = [] - trash_remaining_list = [] - for info in infos: - if "final_info" in info: - episode_reward_list.append(info["final_info"]["episode_reward"]) - episode_length_list.append(info["final_info"]["episode_length"]) - trash_remaining_list.append(info["final_info"]["trash_remaining"]) - - if len(episode_reward_list) > 0: - writer.add_scalar("charts/episodic_reward", np.mean(episode_reward_list), global_step) - - if len(episode_length_list) > 0: - writer.add_scalar("charts/episodic_length", np.mean(episode_length_list), global_step) - - if len(trash_remaining_list) > 0: - writer.add_scalar("charts/total_trash_remaining", np.mean(trash_remaining_list), global_step) - - - # bootstrap value if not done - with torch.no_grad(): - next_value = agent.get_value(next_obs).reshape(1, -1) - advantages = torch.zeros_like(rewards).to(device) - lastgaelam = 0 - for t in reversed(range(args.num_steps)): - if t == args.num_steps - 1: - nextnonterminal = 1.0 - next_done - nextvalues = next_value - else: - nextnonterminal = 1.0 - dones[t + 1].reshape(args.num_envs * num_agents) - nextvalues = values[t + 1].reshape(args.num_envs * num_agents) - delta = rewards[t].reshape(args.num_envs * num_agents) + args.gamma * nextvalues * nextnonterminal - values[t].reshape(args.num_envs * num_agents) - if not isinstance(lastgaelam, int): - lastgaelam = lastgaelam.reshape(args.num_envs * num_agents) - advantages[t] = lastgaelam = (delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam).reshape(args.num_envs, num_agents) - returns = advantages + values - - # flatten the batch - b_obs = obs.reshape((-1,) + envs.single_observation_space.shape) - b_logprobs = logprobs.reshape(-1) - b_actions = actions.reshape((-1,) + envs.single_action_space.shape) - b_advantages = advantages.reshape(-1) - b_returns = returns.reshape(-1) - b_values = values.reshape(-1) - - # Optimizing the policy and value network - b_inds = np.arange(args.batch_size) - clipfracs = [] - for epoch in range(args.update_epochs): - np.random.shuffle(b_inds) - for start in range(0, args.batch_size, args.minibatch_size): - end = start + args.minibatch_size - mb_inds = b_inds[start:end] - - _, newlogprob, entropy, newvalue = agent.get_action_and_value(b_obs[mb_inds], b_actions.long()[mb_inds]) - logratio = newlogprob - b_logprobs[mb_inds] - ratio = logratio.exp() - + next_value = agent.get_value(next_obs).reshape(1, -1) + advantages = torch.zeros_like(rewards).to(device) + lastgaelam = 0 + for t in reversed(range(args.num_steps)): + if t == args.num_steps - 1: + nextnonterminal = 1.0 - next_done + nextvalues = next_value + else: + nextnonterminal = 1.0 - dones[t + 1].reshape(args.num_envs * num_agents) + nextvalues = values[t + 1].reshape(args.num_envs * num_agents) + delta = rewards[t].reshape(args.num_envs * num_agents) + args.gamma * nextvalues * nextnonterminal - values[t].reshape(args.num_envs * num_agents) + if not isinstance(lastgaelam, int): + lastgaelam = lastgaelam.reshape(args.num_envs * num_agents) + advantages[t] = lastgaelam = (delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam).reshape(args.num_envs, num_agents) + returns = advantages + values + + # flatten the batch + b_obs = obs.reshape((-1,) + envs.single_observation_space.shape) + b_logprobs = logprobs.reshape(-1) + b_actions = actions.reshape((-1,) + envs.single_action_space.shape) + b_advantages = advantages.reshape(-1) + b_returns = returns.reshape(-1) + b_values = values.reshape(-1) + + # Optimizing the policy and value network + b_inds = np.arange(batch_size) + clipfracs = [] + for epoch in range(args.update_epochs): + np.random.shuffle(b_inds) + for start in range(0, batch_size, minibatch_size): + end = start + minibatch_size + mb_inds = b_inds[start:end] + + _, newlogprob, entropy, newvalue = agent.get_action_and_value(b_obs[mb_inds], b_actions.long()[mb_inds]) + logratio = newlogprob - b_logprobs[mb_inds] + ratio = logratio.exp() + + with torch.no_grad(): + # calculate approx_kl http://joschu.net/blog/kl-approx.html + old_approx_kl = (-logratio).mean() + approx_kl = ((ratio - 1) - logratio).mean() + clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean().item()] + + mb_advantages = b_advantages[mb_inds] + if args.norm_adv: + mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8) + + # Policy loss + pg_loss1 = -mb_advantages * ratio + pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - args.clip_coef, 1 + args.clip_coef) + pg_loss = torch.max(pg_loss1, pg_loss2).mean() + + # Value loss + newvalue = newvalue.view(-1) + if args.clip_vloss: + v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2 + v_clipped = b_values[mb_inds] + torch.clamp( + newvalue - b_values[mb_inds], + -args.clip_coef, + args.clip_coef, + ) + v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2 + v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped) + v_loss = 0.5 * v_loss_max.mean() + else: + v_loss = 0.5 * ((newvalue - b_returns[mb_inds]) ** 2).mean() + + entropy_loss = entropy.mean() + loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef + + optimizer.zero_grad() + loss.backward() + nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm) + optimizer.step() + + if args.target_kl is not None and approx_kl > args.target_kl: + break + + y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy() + var_y = np.var(y_true) + explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y + + # TRY NOT TO MODIFY: record rewards for plotting purposes + writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], global_step) + writer.add_scalar("losses/value_loss", v_loss.item(), global_step) + writer.add_scalar("losses/policy_loss", pg_loss.item(), global_step) + writer.add_scalar("losses/entropy", entropy_loss.item(), global_step) + writer.add_scalar("losses/old_approx_kl", old_approx_kl.item(), global_step) + writer.add_scalar("losses/approx_kl", approx_kl.item(), global_step) + writer.add_scalar("losses/clipfrac", np.mean(clipfracs), global_step) + writer.add_scalar("losses/explained_variance", explained_var, global_step) + # if iteration % 5 == 0: + # print(f"SPS: {int(global_step / (time.time() - start_time))} | Current Iteration: {iteration} out of {num_iterations}") + writer.add_scalar("charts/SPS", int(global_step / (time.time() - start_time)), global_step) + + # Save model periodically or on the final iteration + time_since_last_save = time.time() - last_save_time + if (time_since_last_save >= args.model_save_interval * 60 or iteration == num_iterations) and args.model_save_path: + # Define a unique filename for each saved model + model_filename = f"{args.model_save_path}_iter_{iteration}.pt" + torch.save(agent, model_filename) + last_save_time = time.time() + # Append the new model to the saved models list + saved_models.append(model_filename) + + # Remove oldest model if we have more than 3 saved + if len(saved_models) > 3: + oldest_model = saved_models.pop(0) + if os.path.exists(oldest_model): + os.remove(oldest_model) + elif args.run_mode == "evaluate": + print("Evaluating loaded model") + + episode_reward_list = [] + episode_length_list = [] + trash_remaining_list = [] + + for episode in range(args.num_eval_episodes): + obs, _ = envs.reset() + done = False + episode_reward = 0 + for step in tqdm(range(args.num_max_env_steps), desc='Evaluating...'): with torch.no_grad(): - # calculate approx_kl http://joschu.net/blog/kl-approx.html - old_approx_kl = (-logratio).mean() - approx_kl = ((ratio - 1) - logratio).mean() - clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean().item()] - - mb_advantages = b_advantages[mb_inds] - if args.norm_adv: - mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8) - - # Policy loss - pg_loss1 = -mb_advantages * ratio - pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - args.clip_coef, 1 + args.clip_coef) - pg_loss = torch.max(pg_loss1, pg_loss2).mean() - - # Value loss - newvalue = newvalue.view(-1) - if args.clip_vloss: - v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2 - v_clipped = b_values[mb_inds] + torch.clamp( - newvalue - b_values[mb_inds], - -args.clip_coef, - args.clip_coef, - ) - v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2 - v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped) - v_loss = 0.5 * v_loss_max.mean() - else: - v_loss = 0.5 * ((newvalue - b_returns[mb_inds]) ** 2).mean() - - entropy_loss = entropy.mean() - loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef - - optimizer.zero_grad() - loss.backward() - nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm) - optimizer.step() - - if args.target_kl is not None and approx_kl > args.target_kl: - break - - y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy() - var_y = np.var(y_true) - explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y - - # TRY NOT TO MODIFY: record rewards for plotting purposes - writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], global_step) - writer.add_scalar("losses/value_loss", v_loss.item(), global_step) - writer.add_scalar("losses/policy_loss", pg_loss.item(), global_step) - writer.add_scalar("losses/entropy", entropy_loss.item(), global_step) - writer.add_scalar("losses/old_approx_kl", old_approx_kl.item(), global_step) - writer.add_scalar("losses/approx_kl", approx_kl.item(), global_step) - writer.add_scalar("losses/clipfrac", np.mean(clipfracs), global_step) - writer.add_scalar("losses/explained_variance", explained_var, global_step) - # if iteration % 5 == 0: - # print(f"SPS: {int(global_step / (time.time() - start_time))} | Current Iteration: {iteration} out of {args.num_iterations}") - writer.add_scalar("charts/SPS", int(global_step / (time.time() - start_time)), global_step) + action, _, _, _ = agent.get_action_and_value(torch.Tensor(obs).to(device)) + obs, reward, terminations, truncations, infos = envs.step(action.cpu().numpy()) + done = terminations.any() or truncations.any() + episode_reward += reward.sum().item() + + writer.add_scalar(f"charts/mean-reward", rewards[step].mean(), step) + + for info in infos: + if "final_info" in info: + episode_reward_list.append(info["final_info"]["episode_reward"]) + episode_length_list.append(info["final_info"]["episode_length"]) + trash_remaining_list.append(info["final_info"]["trash_remaining"]) + + print(f"Average Episode Reward: {np.mean(episode_reward_list)}") + print(f"Average Episode Length: {np.mean(episode_length_list)}") + print(f"Average Trash Remaining: {np.mean(trash_remaining_list)}") + elif args.run_mode == "video": + print("Generating video for loaded model") + import imageio + + # Video recording loop + obs, _ = envs.reset() + frames = [] + done = False + while not done: + with torch.no_grad(): + action, _, _, _ = agent.get_action_and_value(torch.Tensor(obs).to(device)) + obs, _, terminations, truncations, _ = envs.step(action.cpu().numpy()) + frame = envs.render() + frames.append(frame) + done = terminations.any() or truncations.any() + + # Save video file + video_path = "agent_run.mp4" + with imageio.get_writer(video_path, fps=30) as video_writer: + for frame in frames: + video_writer.append_data(frame) + print(f"Video saved to {video_path}") + else: + print(f"Unhandled run_mode of: {args.run_mode}") envs.close() writer.close() diff --git a/pufferlib/environments/trash_pickup/__init__.py b/pufferlib/environments/trash_pickup/__init__.py index 51a69b3a..8b3a2a06 100644 --- a/pufferlib/environments/trash_pickup/__init__.py +++ b/pufferlib/environments/trash_pickup/__init__.py @@ -1,5 +1,5 @@ -# from .old_environment import env_creator, make -from .environment import env_creator, make +# from .cy_environment import env_creator, make # if you want to use the cython version, use this instead +from .py_environment import env_creator, make try: import torch diff --git a/pufferlib/environments/trash_pickup/environment.py b/pufferlib/environments/trash_pickup/cy_environment.py similarity index 67% rename from pufferlib/environments/trash_pickup/environment.py rename to pufferlib/environments/trash_pickup/cy_environment.py index 18e4711c..f7ad2ce5 100644 --- a/pufferlib/environments/trash_pickup/environment.py +++ b/pufferlib/environments/trash_pickup/cy_environment.py @@ -1,3 +1,9 @@ +''' +This is a cython version of the trash_pickup env. +It works but its actually slightly slower (4%) than just the normal python version. +Figured I would keep it around as reference for anyone who wants to try multi-agent with cython bindings. +''' + import functools import numpy as np import gymnasium as gym @@ -12,7 +18,7 @@ -class TrashPickupEnv(ParallelEnv): +class CyTrashPickupEnv(ParallelEnv): def __init__(self, grid_size=10, num_agents=3, num_trash=15, num_bins=2, max_steps=300): self.grid_size = grid_size self.num_trash = num_trash @@ -28,6 +34,7 @@ def __init__(self, grid_size=10, num_agents=3, num_trash=15, num_bins=2, max_ste self._num_agents = num_agents self.possible_agents = ["agent_" + str(i) for i in range(self._num_agents)] + self.agents = self.possible_agents[:] # Define the action space self.action_spaces = {agent: spaces.Discrete(4) for agent in self.possible_agents} @@ -42,17 +49,31 @@ def __init__(self, grid_size=10, num_agents=3, num_trash=15, num_bins=2, max_ste for agent in self.possible_agents } - - super().__init__() self.env = CyTrashPickupEnv(grid_size, num_agents, num_trash, num_bins, max_steps) + def _reshape_observations(self, observations): + """Reshape the grid component of each agent's observation.""" + for agent, obs in observations.items(): + obs['grid'] = np.array(obs['grid']).reshape(self.grid_size, self.grid_size) + return observations + def reset(self, seed=None): observations = self.env.reset() + observations = self._reshape_observations(observations) return observations def step(self, actions): observations, rewards, dones, infos = self.env.step(actions) + + # Convert rewards, dones, and infos lists to dictionaries with agent IDs as keys + agent_ids = [f"agent_{i}" for i in range(self.num_agents)] + rewards = {agent_id: rewards[i] for i, agent_id in enumerate(agent_ids)} + dones = {agent_id: dones[i] for i, agent_id in enumerate(agent_ids)} + infos = {agent_id: infos[i] for i, agent_id in enumerate(agent_ids)} + + observations = self._reshape_observations(observations) + return observations, rewards, dones, infos def render(self): @@ -66,7 +87,7 @@ def env_creator(): return functools.partial(make) def make(): - env = TrashPickupEnv() + env = CyTrashPickupEnv() # env = aec_to_parallel_wrapper(env) env = pufferlib.wrappers.PettingZooTruncatedWrapper(env) return pufferlib.emulation.PettingZooPufferEnv(env=env) \ No newline at end of file diff --git a/pufferlib/environments/trash_pickup/cy_trash_pickup.pyx b/pufferlib/environments/trash_pickup/cy_trash_pickup.pyx index d5f65619..2e075c6f 100644 --- a/pufferlib/environments/trash_pickup/cy_trash_pickup.pyx +++ b/pufferlib/environments/trash_pickup/cy_trash_pickup.pyx @@ -53,7 +53,7 @@ cdef class CyTrashPickupEnv: # Set actions cdef int i for i in range(self.num_agents): - self.env.actions[i] = actions[i] + self.env.actions[i] = actions[f'agent_{i}'] # Step the environment step_env(self.env) @@ -78,7 +78,7 @@ cdef class CyTrashPickupEnv: for j in range(idx + 3, idx + self.obs_size): grid_data.append(self.env.observations[j]) obs['grid'] = grid_data # You may need to reshape this into a 2D array - observations[f"agent-{i}"] = obs + observations[f"agent_{i}"] = obs return observations def __dealloc__(self): diff --git a/pufferlib/environments/trash_pickup/old_environment.py b/pufferlib/environments/trash_pickup/py_environment.py similarity index 95% rename from pufferlib/environments/trash_pickup/old_environment.py rename to pufferlib/environments/trash_pickup/py_environment.py index 9efd8747..26eb0024 100644 --- a/pufferlib/environments/trash_pickup/old_environment.py +++ b/pufferlib/environments/trash_pickup/py_environment.py @@ -18,7 +18,7 @@ class GridState(Enum): TRASH_BIN = 2 AGENT = 3 -class TrashPickupOldEnv(ParallelEnv): +class PyTrashPickupEnv(ParallelEnv): metadata = {'render_modes': ['human'], 'name': 'TrashPickupOld'} def __init__(self, grid_size=10, num_agents=3, num_trash=15, num_bins=2, max_steps=300): @@ -274,11 +274,23 @@ def state(self): # Return the global state (optional) return self.grid.copy() -def env_creator(): - return functools.partial(make) - -def make(): - env = TrashPickupOldEnv() - # env = aec_to_parallel_wrapper(env) +def env_creator(grid_size=10, num_agents=3, num_trash=15, num_bins=2, max_steps=300): + return functools.partial(make, + grid_size=grid_size, + num_agents=num_agents, + num_trash=num_trash, + num_bins=num_bins, + max_steps=max_steps + ) + +def make(grid_size=10, num_agents=3, num_trash=15, num_bins=2, max_steps=300): + env = PyTrashPickupEnv( + grid_size=grid_size, + num_agents=num_agents, + num_trash=num_trash, + num_bins=num_bins, + max_steps=max_steps + ) + env = pufferlib.wrappers.PettingZooTruncatedWrapper(env) return pufferlib.emulation.PettingZooPufferEnv(env=env) From 5a807745d5e7bc8eb3c9781d64981551ebe0d78b Mon Sep 17 00:00:00 2001 From: root Date: Wed, 6 Nov 2024 04:55:38 +0000 Subject: [PATCH 05/59] fixed minor bugs with saving models --- examples/puffer_vec_ppo_trash_pickup.py | 22 ++++++++++++++++------ 1 file changed, 16 insertions(+), 6 deletions(-) diff --git a/examples/puffer_vec_ppo_trash_pickup.py b/examples/puffer_vec_ppo_trash_pickup.py index 9ae6f7c5..9055a32d 100644 --- a/examples/puffer_vec_ppo_trash_pickup.py +++ b/examples/puffer_vec_ppo_trash_pickup.py @@ -43,8 +43,10 @@ class Args: """the entity (team) of wandb's project""" run_mode: str = "train" """'train' to train a new model and save it, 'evaluate' to load an existing model and test performance, 'video' to load an existing model and create a video""" - model_save_path: str = './trash_pickup_saves/trash_pickup_model' - """Path to save model to this directory (file extension is automatically added, do NOT include it)""" + model_save_dir: str = 'examples/trash_pickup_saves/' + """Path to save the model to this directory""" + model_save_filename: str = "trash_pickup_model" + """Filename to save the model as (file extension is automatically added, do NOT include it)""" model_load_path: str = None """Path to load in existing model to continue training, perform evaluate, or create a video""" model_save_interval: float = 1 @@ -59,7 +61,7 @@ class Args: """total timesteps of the experiments""" learning_rate: float = 5e-4 """the learning rate of the optimizer""" - num_envs: int = 2 + num_envs: int = 16 """the number of parallel game environments""" num_steps: int = 512 """the number of steps to run in each environment per policy rollout""" @@ -201,6 +203,12 @@ def get_action_and_value(self, x, action=None): if __name__ == "__main__": args = tyro.cli(Args) + + if not os.path.isdir(args.model_save_dir): + print(f"Model Save Directory [{args.model_save_dir}] does not exist, exitting...") + print(f"Current absolute path is: {os.path.abspath('./')}") + exit(0) + batch_size = int(args.num_envs * args.num_steps) minibatch_size = int(batch_size // args.num_minibatches) num_iterations = args.total_timesteps // batch_size @@ -263,7 +271,9 @@ def get_action_and_value(self, x, action=None): dones = torch.zeros((args.num_steps, args.num_envs, num_agents)).to(device) values = torch.zeros((args.num_steps, args.num_envs, num_agents)).to(device) - model_path = args.model_load_path if args.model_load_path else args.model_save_path + model_save_path = os.path.join(args.model_save_dir, args.model_save_filename) + + model_path = args.model_load_path if args.model_load_path else model_save_path if model_path and os.path.exists(model_path): print(f"Loading model from {model_path}...") agent = torch.load(model_path, map_location=device) @@ -431,9 +441,9 @@ def get_action_and_value(self, x, action=None): # Save model periodically or on the final iteration time_since_last_save = time.time() - last_save_time - if (time_since_last_save >= args.model_save_interval * 60 or iteration == num_iterations) and args.model_save_path: + if (time_since_last_save >= args.model_save_interval * 60 or iteration == num_iterations) and model_save_path: # Define a unique filename for each saved model - model_filename = f"{args.model_save_path}_iter_{iteration}.pt" + model_filename = f"{model_save_path}_iter_{iteration}.pt" torch.save(agent, model_filename) last_save_time = time.time() # Append the new model to the saved models list From 4037e7a5f4811225fafe240c646966e67b8141c0 Mon Sep 17 00:00:00 2001 From: root Date: Thu, 7 Nov 2024 04:28:32 +0000 Subject: [PATCH 06/59] Added documentation, but video mode still doesn't work... --- examples/puffer_vec_ppo_trash_pickup.py | 106 ++++++++++++------ .../trash_pickup_model_iter_13338.pt | Bin 0 -> 489822 bytes .../trash_pickup_model_iter_13348.pt | Bin 0 -> 489822 bytes .../trash_pickup_model_iter_13358.pt | Bin 0 -> 489822 bytes pufferlib/environments/trash_pickup/README.md | 55 +++++++++ .../trash_pickup/py_environment.py | 72 +++++++----- 6 files changed, 170 insertions(+), 63 deletions(-) create mode 100644 examples/trash_pickup_saves/trash_pickup_model_iter_13338.pt create mode 100644 examples/trash_pickup_saves/trash_pickup_model_iter_13348.pt create mode 100644 examples/trash_pickup_saves/trash_pickup_model_iter_13358.pt create mode 100644 pufferlib/environments/trash_pickup/README.md diff --git a/examples/puffer_vec_ppo_trash_pickup.py b/examples/puffer_vec_ppo_trash_pickup.py index 9055a32d..8f686968 100644 --- a/examples/puffer_vec_ppo_trash_pickup.py +++ b/examples/puffer_vec_ppo_trash_pickup.py @@ -1,12 +1,12 @@ -# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/ppo/#ppo_ataripy +# Standard libraries for file operations, randomization, time management, and data handling import os import random import time from dataclasses import dataclass from copy import deepcopy +# Libraries for progress tracking, environments, and RL model components from tqdm import tqdm - import gymnasium as gym import numpy as np import torch @@ -16,9 +16,8 @@ from torch.distributions.categorical import Categorical from torch.utils.tensorboard import SummaryWriter -import gym as old_gym -import shimmy - +# PufferLib modules for environment emulation and agent training in a multi-agent setting +import pufferlib import pufferlib.emulation import pufferlib.pytorch import pufferlib.vector @@ -27,6 +26,7 @@ @dataclass class Args: + # Experiment configuration details, seed values, and directory paths exp_name: str = os.path.basename(__file__)[: -len(".py")] """the name of this experiment""" seed: int = 1 @@ -41,20 +41,20 @@ class Args: """the wandb's project name""" wandb_entity: str = None """the entity (team) of wandb's project""" - run_mode: str = "train" + run_mode: str = "video" """'train' to train a new model and save it, 'evaluate' to load an existing model and test performance, 'video' to load an existing model and create a video""" - model_save_dir: str = 'examples/trash_pickup_saves/' + model_save_dir: str = None """Path to save the model to this directory""" model_save_filename: str = "trash_pickup_model" """Filename to save the model as (file extension is automatically added, do NOT include it)""" - model_load_path: str = None + model_load_path: str = 'puffertank/pufferlib/examples/trash_pickup_saves/trash_pickup_model_iter_13358.pt' """Path to load in existing model to continue training, perform evaluate, or create a video""" model_save_interval: float = 1 """How (roughly) often to save the model in minutes""" num_eval_episodes: int = 10 - """Number of evaluation episodes to run (only for run_mode == 'evaluation')""" + """Number of evaluation episodes to run (only for run_mode == 'evaluate')""" - # Algorithm specific arguments + # PPO algorithm-specific hyperparameters like learning rate, number of timesteps, etc. env_id: str = "TrashPickup" """the id of the environment""" total_timesteps: int = 250_000_000 @@ -63,7 +63,7 @@ class Args: """the learning rate of the optimizer""" num_envs: int = 16 """the number of parallel game environments""" - num_steps: int = 512 + num_steps: int = 1024 """the number of steps to run in each environment per policy rollout""" anneal_lr: bool = True """Toggle learning rate annealing for policy and value networks""" @@ -90,7 +90,7 @@ class Args: target_kl: float = None """the target KL divergence threshold""" - # Environment Specific Variables + # Environment-specific parameters, such as grid size, agent count, and max steps per episode num_agents: int = 3 """The number of agents in the environment""" grid_size: int = 10 @@ -106,6 +106,7 @@ class Args: def layer_init(layer, std=np.sqrt(2), bias_const=0.0): + # Initialize the layer with orthogonal weights and set bias to a constant torch.nn.init.orthogonal_(layer.weight, std) torch.nn.init.constant_(layer.bias, bias_const) return layer @@ -117,22 +118,23 @@ def __init__(self, emulated): self.dtype = pufferlib.pytorch.nativize_dtype(emulated) def get_conv_output_size(input_shape): - """Calculates the size of the output from the convolution layers.""" + """Helper function to calculate the flattened size of output from convolution layers. + This size will be used as the input for the linear layer following the convolutional layers.""" dummy_input = torch.zeros(1, *input_shape) # Batch size of 1 with given input shape output = self.conv2(self.conv1(dummy_input)) # Pass through conv layers return int(np.prod(output.shape[1:])) # Flatten dimensions, except batch - # Define network for processing the grid with multiple channels (4 channels for 0, 1, 2, 3) + # Define the neural network for grid processing with 4 input channels (empty, trash, bin, agent) input_channels = 4 # Define the convolutional layers self.conv1 = layer_init(nn.Conv2d(input_channels, 32, 3, stride=1)) self.conv2 = layer_init(nn.Conv2d(32, 64, 3, stride=1)) - # Calculate the output size of the conv layers + # Compute the output size of the convolutional layers dynamically conv_output_size = get_conv_output_size((input_channels, args.grid_size, args.grid_size)) - + # Sequential grid network including the calculated linear layer self.grid_net = nn.Sequential( self.conv1, nn.ReLU(), @@ -154,7 +156,7 @@ def get_conv_output_size(input_shape): nn.ReLU(), ) - # Combine outputs + # Projection layer to combine features from different sources self.proj = nn.Linear(32 + 32 + 32, 256) # Adjusted projection layer size based on concatenated features self.actor = layer_init(nn.Linear(256, 4), std=0.01) # 4 represents the number of possible actions self.critic = layer_init(nn.Linear(256, 1), std=1) # 1 represents the value of the critic network (or sometimes called value network) @@ -178,7 +180,7 @@ def hidden(self, x): grid = self.preprocess_grid(x['grid']) grid_features = self.grid_net(grid) - position = x['agent_position'].float() / 10 # divide by 10 (grid-size) hard-coded for now... + position = x['agent_position'].float() / args.grid_size position_features = self.position_net(position) carrying = x['carrying_trash'].float() @@ -204,10 +206,16 @@ def get_action_and_value(self, x, action=None): if __name__ == "__main__": args = tyro.cli(Args) - if not os.path.isdir(args.model_save_dir): + # Verify that the directory for saving models exists, otherwise exit + if args.model_save_dir is not None and os.path.isdir(args.model_save_dir): print(f"Model Save Directory [{args.model_save_dir}] does not exist, exitting...") print(f"Current absolute path is: {os.path.abspath('./')}") exit(0) + elif args.run_mode == 'train' and args.model_save_dir is None: + print(f"WARNING: Training but save location not specified. Continuing running to store training data, but model will not be saved") + elif (args.run_mode == 'evaluate' or args.run_mode == 'video') and args.model_load_path is None: + print(f"Can not run program with run_mode '{args.run_mode}' and args.model_load_path is None.") + exit(0) batch_size = int(args.num_envs * args.num_steps) minibatch_size = int(batch_size // args.num_minibatches) @@ -271,7 +279,8 @@ def get_action_and_value(self, x, action=None): dones = torch.zeros((args.num_steps, args.num_envs, num_agents)).to(device) values = torch.zeros((args.num_steps, args.num_envs, num_agents)).to(device) - model_save_path = os.path.join(args.model_save_dir, args.model_save_filename) + if args.model_save_dir is not None: + model_save_path = os.path.join(args.model_save_dir, args.model_save_filename) model_path = args.model_load_path if args.model_load_path else model_save_path if model_path and os.path.exists(model_path): @@ -283,38 +292,42 @@ def get_action_and_value(self, x, action=None): agent = Agent(envs.driver_env.emulated).to(device) if args.run_mode == "train": + # Training mode: initialize optimizer and training variables optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5) - # TRY NOT TO MODIFY: start the game + # Start training loop global_step = 0 start_time = time.time() next_obs, _ = envs.reset(seed=args.seed) # returns tensor of shape ([6, 120]), but we need it of shape ([3, 2, 120]) next_done = torch.zeros((args.num_envs, num_agents)).to(device) - # List to keep track of saved models + # Initialize list to keep track of saved models (keeping only the latest 3 models) saved_models = [] last_save_time = time.time() + # Begin training iteration loop for iteration in tqdm(range(1, num_iterations + 1)): - # Annealing the rate if instructed to do so. + # Annealing the learning rate if instructed to do so. if args.anneal_lr: frac = 1.0 - (iteration - 1.0) / num_iterations lrnow = frac * args.learning_rate optimizer.param_groups[0]["lr"] = lrnow + # Step loop for environment interaction for step in range(0, args.num_steps): + # Update global step count and store current observations and dones global_step += args.num_envs * num_agents obs[step] = torch.Tensor(next_obs.reshape(args.num_envs, num_agents, envs.single_observation_space.shape[0])).to(device) # error here dones[step] = torch.Tensor(next_done.reshape(args.num_envs, num_agents)).to(device) - # ALGO LOGIC: action logic + # Get actions and values from agentic with torch.no_grad(): action, logprob, _, value = agent.get_action_and_value(torch.Tensor(next_obs).to(device)) values[step] = value.reshape(args.num_envs, num_agents) actions[step] = action.reshape(args.num_envs, num_agents) logprobs[step] = logprob.reshape(args.num_envs, num_agents) - # TRY NOT TO MODIFY: execute the game and log data. + # Execute the game step and collect new observations next_obs, reward, terminations, truncations, infos = envs.step(action.cpu().numpy()) next_done = np.logical_or(terminations, truncations) rewards[step] = torch.tensor(reward).to(device).view(args.num_envs, num_agents) @@ -439,13 +452,15 @@ def get_action_and_value(self, x, action=None): # print(f"SPS: {int(global_step / (time.time() - start_time))} | Current Iteration: {iteration} out of {num_iterations}") writer.add_scalar("charts/SPS", int(global_step / (time.time() - start_time)), global_step) - # Save model periodically or on the final iteration + # (Optional) Save model periodically + # Check if enough time has passed or if we're at the final iteration time_since_last_save = time.time() - last_save_time if (time_since_last_save >= args.model_save_interval * 60 or iteration == num_iterations) and model_save_path: # Define a unique filename for each saved model model_filename = f"{model_save_path}_iter_{iteration}.pt" torch.save(agent, model_filename) last_save_time = time.time() + # Append the new model to the saved models list saved_models.append(model_filename) @@ -454,18 +469,22 @@ def get_action_and_value(self, x, action=None): oldest_model = saved_models.pop(0) if os.path.exists(oldest_model): os.remove(oldest_model) + elif args.run_mode == "evaluate": + # Evaluation mode: run episodes and log performance print("Evaluating loaded model") - episode_reward_list = [] - episode_length_list = [] - trash_remaining_list = [] + # Track results across episodes for final metrics + episode_reward_list, episode_length_list, trash_remaining_list = [], [], [] - for episode in range(args.num_eval_episodes): + # Run the environment for the specified number of evaluation episodes + for episode in tqdm(range(args.num_eval_episodes), desc='Evaluating Episodes...'): obs, _ = envs.reset() done = False episode_reward = 0 - for step in tqdm(range(args.num_max_env_steps), desc='Evaluating...'): + + # Play out one episode, logging rewards and episode information + for step in range(args.num_max_env_steps): with torch.no_grad(): action, _, _, _ = agent.get_action_and_value(torch.Tensor(obs).to(device)) obs, reward, terminations, truncations, infos = envs.step(action.cpu().numpy()) @@ -480,12 +499,15 @@ def get_action_and_value(self, x, action=None): episode_length_list.append(info["final_info"]["episode_length"]) trash_remaining_list.append(info["final_info"]["trash_remaining"]) + # Summarize evaluation results print(f"Average Episode Reward: {np.mean(episode_reward_list)}") print(f"Average Episode Length: {np.mean(episode_length_list)}") print(f"Average Trash Remaining: {np.mean(trash_remaining_list)}") + elif args.run_mode == "video": print("Generating video for loaded model") import imageio + import raylibpy as pyray # Library for rendering the environment # Video recording loop obs, _ = envs.reset() @@ -495,16 +517,26 @@ def get_action_and_value(self, x, action=None): with torch.no_grad(): action, _, _, _ = agent.get_action_and_value(torch.Tensor(obs).to(device)) obs, _, terminations, truncations, _ = envs.step(action.cpu().numpy()) - frame = envs.render() + + # Render the environment + envs.driver_env.render() # Render the frame + + pyray.take_screenshot("temp_frame.png") # Save the screenshot to a temporary file + frame = imageio.imread("temp_frame.png") frames.append(frame) + done = terminations.any() or truncations.any() - # Save video file - video_path = "agent_run.mp4" - with imageio.get_writer(video_path, fps=30) as video_writer: - for frame in frames: - video_writer.append_data(frame) + # Save frames to video + video_path = 'trash_pickup_run.mp4' + imageio.mimsave(video_path, frames, fps=30) print(f"Video saved to {video_path}") + + # NOTE: If you get this error - TypeError: write_frames() got an unexpected keyword argument 'audio_path' + # Try the following command and then rerun: pip install --upgrade imageio imageio-ffmpeg + + # Delete the temporary file + os.remove("temp_frame.png") else: print(f"Unhandled run_mode of: {args.run_mode}") diff --git 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