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run_metaworld_sac_mt.py
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import metaworld
# mushroomrl
from mushroom_rl.core import Logger
# deeplearning frameworks
import torch.optim as optim
import torch.nn.functional as F
# continual proto-value functions
from moore.core import VecCore
from moore.algorithms.actor_critic import MTSAC
from moore.environments.metaworld_env import make_env
from moore.environments import SubprocVecEnv
from moore.utils.dataset import get_stats
from moore.utils.argparser import argparser
import moore.utils.networks_sac as Network
# data handling
import numpy as np
# visualization
from tqdm import trange
import wandb
# Utils
import pickle
import os
# The function is used to run a single experiment
def run_experiment(args, save_dir, exp_id = 0, seed = None):
import matplotlib
matplotlib.use('Agg')
np.random.seed()
single_logger = Logger(f"seed_{exp_id if seed is None else seed}", results_dir=save_dir, log_console=True)
save_dir = single_logger.path
n_epochs = args.n_epochs
n_steps = args.n_steps
n_episodes_test = args.n_episodes_test
# MDP
exp_type = args.exp_type
# env_name = args.env_name
horizon = args.horizon
gamma = args.gamma
gamma_eval = args.gamma_eval
benchmark = getattr(metaworld, exp_type)()
mdp = SubprocVecEnv(
[make_env(env_name=env_name,
env_cls=env_cls,
train_tasks = benchmark.train_tasks,
horizon=horizon,
gamma=gamma,
normalize_reward=args.normalize_reward,
sample_task_per_episode=args.sample_task_per_episode)
for env_name, env_cls in benchmark.train_classes.items()])
n_contexts = mdp.num_envs
# Settings
initial_replay_size = args.initial_replay_size #
max_replay_size = int(args.max_replay_size) #
batch_size = args.batch_size #
train_frequency = args.train_frequency
tau = args.tau #
warmup_transitions = args.warmup_transitions #
log_std_min = args.log_std_min
log_std_max = args.log_std_max
append_context_actor = "Single" in args.actor_network
append_context_mu_actor = "Single" in args.actor_mu_network
append_context_sigma_actor = "Single" in args.actor_sigma_network
append_context_critic = "Single" in args.critic_network
# Settings
if args.shared_mu_sigma:
actor_network = getattr(Network, args.actor_network)#
actor_n_features = args.actor_n_features#
else:
actor_mu_network = getattr(Network, args.actor_mu_network)#
actor_sigma_network = getattr(Network, args.actor_sigma_network) #
actor_mu_n_features = args.actor_mu_n_features#
actor_sigma_n_features = args.actor_sigma_n_features#
critic_network = getattr(Network, args.critic_network)#
critic_n_features = args.critic_n_features#
lr_alpha = args.lr_alpha #
lr_actor = args.lr_actor #
lr_critic = args.lr_critic #
target_entropy = args.target_entropy #
actor_params = None
actor_mu_params = None
actor_sigma_params = None
# Approximator
if args.shared_mu_sigma:
actor_input_shape = mdp.observation_space.shape
if append_context_actor:
single_logger.info("Append context to Actor's input!")
actor_input_shape = (actor_input_shape[0] + n_contexts,)
actor_params = dict(network=actor_network,
n_features=actor_n_features,
input_shape=actor_input_shape,
output_shape=mdp.action_space.shape,
shared_mu_sigma = args.shared_mu_sigma,
use_cuda=args.use_cuda,
n_contexts = n_contexts,
activation = args.activation,
orthogonal = args.orthogonal,
n_experts = args.n_experts,
agg_activation = args.agg_activation,
)#
else:
mu_actor_input_shape = mdp.observation_space.shape
if append_context_mu_actor:
single_logger.info("Append context to Mu Actor's input!")
mu_actor_input_shape = (mu_actor_input_shape[0] + n_contexts,)
actor_mu_params = dict(network=actor_mu_network,
n_features=actor_mu_n_features,
input_shape=mu_actor_input_shape,
output_shape=mdp.action_space.shape,
use_cuda=args.use_cuda,
n_contexts = n_contexts,
activation = args.activation,
orthogonal = args.orthogonal,
n_experts = args.n_experts,
agg_activation = args.agg_activation,)#
sigma_actor_input_shape = mdp.observation_space.shape
if append_context_sigma_actor:
single_logger.info("Append context to Sigma Actor's input!")
sigma_actor_input_shape = (sigma_actor_input_shape[0] + n_contexts,)
actor_sigma_params = dict(network=actor_sigma_network,
n_features=actor_sigma_n_features,
input_shape=sigma_actor_input_shape,
output_shape=mdp.action_space.shape,
use_cuda=args.use_cuda,
n_contexts = n_contexts,
activation = args.activation,
orthogonal = args.orthogonal,
n_experts = args.n_experts,
agg_activation = args.agg_activation,)#
actor_optimizer = {'class': optim.Adam,
'params': {'lr': lr_actor, 'betas': (0.9, 0.999)}}#
critic_input_shape = (mdp.observation_space.shape[0] + mdp.action_space.shape[0],)
if append_context_critic:
single_logger.info("Append context to Critic's input!")
critic_input_shape = (critic_input_shape[0]+n_contexts,)
critic_params = dict(network=critic_network,
optimizer={'class': optim.Adam,
'params': {'lr': lr_critic, 'betas': (0.9, 0.999)}},
loss=F.mse_loss,
n_features=critic_n_features,
input_shape=critic_input_shape,
output_shape=(1,),
use_cuda=args.use_cuda,
n_contexts = n_contexts,
activation = args.activation,
orthogonal = args.orthogonal,
n_experts = args.n_experts,
agg_activation = args.agg_activation,
)
if args.debug:
initial_replay_size = 150
batch_size = 8
n_epochs = 2
n_steps = 150
n_steps_test = 100
n_episodes_test = 1
args.wandb = False
warmup_transitions = 150
if args.wandb:
wandb.init(name = "seed_"+str(exp_id if seed is None else seed), project = "MOORE", group = f"metaworld_{args.env_name}" if args.env_name is not None else f"metaworld_{args.exp_type}", job_type=args.exp_name, entity=args.wandb_entity, config=vars(args))
# create SAC agent
agent = MTSAC(mdp_info=mdp.info,
batch_size=batch_size, initial_replay_size=initial_replay_size,
max_replay_size=max_replay_size,
warmup_transitions=warmup_transitions, tau=tau, lr_alpha=lr_alpha,
actor_params = actor_params, actor_mu_params=actor_mu_params, actor_sigma_params=actor_sigma_params,
actor_optimizer=actor_optimizer, critic_params=critic_params,
target_entropy=target_entropy, critic_fit_params=None,
log_std_min=log_std_min, log_std_max=log_std_max, shared_mu_sigma=args.shared_mu_sigma,
n_contexts=n_contexts)
os.makedirs(save_dir, exist_ok=True)
os.makedirs(os.path.join(save_dir, "actor"), exist_ok=True)
os.makedirs(os.path.join(save_dir, "critic"), exist_ok=True)
os.makedirs(os.path.join(save_dir, "agent"), exist_ok=True)
# load agent
if args.load_agent:
agent = agent.load(args.load_agent)
else:
# load the critic
if args.load_critic:
agent.set_critic_weights(args.load_critic)
# load the policy/ actor
if args.load_actor:
agent.policy.set_weights(np.load(args.load_actor))
# set logger
agent.set_logger(single_logger)
# log models summary
agent.models_summary()
# Algorithm
core = VecCore(agent, mdp)
# metrics
env_names = mdp.get_attr("env_name")
metrics = {env_name_i: {} for env_name_i in env_names}
for key, value in metrics.items():
value.update({"MinReturn": []})
value.update({"MaxReturn": []})
value.update({"AverageReturn": []})
value.update({"AverageDiscountedReturn": []})
value.update({"SuccessRate": []})
value.update({"LogAlpha": []})
metrics.update({"all_metaworld": {"SuccessRate": []}})
if args.start_epoch == 0:
# Intialize the replay memory
core.eval = False
core.learn(n_steps=initial_replay_size, n_steps_per_fit=initial_replay_size, render=args.render_train)
# random policy evaluation
current_success_rate_avg = 0.0
for c, key in enumerate(env_names):
core.eval = True
core.current_idx = c
dataset, dataset_info = core.evaluate(n_episodes=n_episodes_test, render=args.render_eval if exp_id == 0 else False, get_env_info=True)
min_J, max_J, mean_J, mean_discounted_J, success_rate = get_stats(dataset, gamma, gamma_eval, dataset_info=dataset_info)
log_alpha = agent.get_log_alpha(c)
metrics[key]["MinReturn"].append(min_J)
metrics[key]["MaxReturn"].append(max_J)
metrics[key]["AverageReturn"].append(mean_J)
metrics[key]["AverageDiscountedReturn"].append(mean_discounted_J)
metrics[key]["SuccessRate"].append(success_rate)
metrics[key]["LogAlpha"].append(log_alpha)
current_success_rate_avg+=success_rate
single_logger.epoch_info(0, C = key,
min_J=min_J,
max_J = max_J,
mean_J = mean_J,
mean_discounted_J = mean_discounted_J,
success_rate = success_rate,
log_alpha = log_alpha)
if args.wandb:
wandb.log({f'{key}/MinReturn': min_J,
f'{key}/MaxReturn': max_J,
f'{key}/AverageReturn':mean_J,
f'{key}/AverageDiscountedReturn':mean_discounted_J,
f'{key}/SuccessRate':success_rate,
f'{key}/LogAlpha':log_alpha}, step = 0, commit=False)
metrics["all_metaworld"]["SuccessRate"].append(current_success_rate_avg / n_contexts)
if args.wandb:
wandb.log({"all_metaworld/SuccessRate": current_success_rate_avg / n_contexts}, step = 0, commit=True)
for n in trange(args.start_epoch, n_epochs):
# train
core.eval = False
core.learn(n_steps=n_steps, n_steps_per_fit=train_frequency, render=args.render_train, quiet=False)
# eval
core.eval = True
current_success_rate_avg = 0
for c, key in enumerate(env_names):
core.current_idx = c
dataset, dataset_info = core.evaluate(n_episodes=n_episodes_test, render=(args.render_eval if n%args.render_interval == 0 and exp_id == 0 else False), get_env_info=True)
min_J, max_J, mean_J, mean_discounted_J, success_rate = get_stats(dataset, gamma, gamma_eval, dataset_info=dataset_info)
log_alpha = agent.get_log_alpha(c)
metrics[key]["MinReturn"].append(min_J)
metrics[key]["MaxReturn"].append(max_J)
metrics[key]["AverageReturn"].append(mean_J)
metrics[key]["AverageDiscountedReturn"].append(mean_discounted_J)
metrics[key]["SuccessRate"].append(success_rate)
metrics[key]["LogAlpha"].append(log_alpha)
current_success_rate_avg+=success_rate
single_logger.epoch_info(n+1, C = key,
min_J=min_J,
max_J = max_J,
mean_J = mean_J,
mean_discounted_J = mean_discounted_J,
success_rate = success_rate,
log_alpha = log_alpha)
if args.wandb:
wandb.log({f'{key}/MinReturn': min_J,
f'{key}/MaxReturn': max_J,
f'{key}/AverageReturn':mean_J,
f'{key}/AverageDiscountedReturn':mean_discounted_J,
f'{key}/SuccessRate':success_rate,
f'{key}/LogAlpha':log_alpha}, step = n+1, commit=False)
metrics["all_metaworld"]["SuccessRate"].append(current_success_rate_avg / n_contexts)
if args.wandb:
wandb.log({"all_metaworld/SuccessRate": current_success_rate_avg / n_contexts}, step = n+1, commit=True)
if (n+1) % args.rl_checkpoint_interval == 0:
# save the learned policy/ actor so far
actor_weights = agent.policy.get_weights()
np.save(os.path.join(save_dir, f"actor/actor_weights_{n+1}.npy"), actor_weights)
# save the critic so far
critic_weights = agent.get_critic_weights()
for key, value in critic_weights.items():
np.save(os.path.join(save_dir, f"critic/{key}_{n+1}.npy"), value)
# save the whole agent
agent.save(os.path.join(save_dir, f"agent/agent_{n+1}"), full_save=True)
if args.wandb:
wandb.finish()
# save the learned policy/ actor
actor_weights = agent.policy.get_weights()
np.save(os.path.join(save_dir, "actor/actor_weights.npy"), actor_weights)
# save the critic
critic_weights = agent.get_critic_weights()
for key, value in critic_weights.items():
np.save(os.path.join(save_dir, f"critic/{key}.npy"), value)
# save the whole agent
agent.save(os.path.join(save_dir, f"agent/agent_final"), full_save=True)
return metrics
if __name__ == '__main__':
# arguments
args = argparser()
if args.seed is not None:
assert len(args.seed) == args.n_exp
alg_name = "mixture_orthogonal_experts" if args.orthogonal else "mixture_experts"
# logging
results_dir = os.path.join(args.results_dir, args.exp_type, alg_name)
logger = Logger(args.exp_name, results_dir=results_dir, log_console=True, use_timestamp=args.use_timestamp)
logger.strong_line()
logger.info('Experiment Algorithm: ' + MTSAC.__name__)
save_dir = logger.path
with open(os.path.join(save_dir, 'args.pkl'), 'wb') as f:
pickle.dump(args, f)
logger.info(vars(args))
out = run_experiment(args, save_dir, seed=args.seed[0])
for key, value in out.items():
if key == "all_metaworld":
np.save(os.path.join(save_dir, f'all_SuccessRate.npy'), value["SuccessRate"])
else:
for metric_key, metric_value in value.items():
np.save(os.path.join(save_dir, f'{key}_{metric_key}.npy'), metric_value)