import argparse import os import gymnasium as gym import numpy as np import torch from torch.utils.tensorboard import SummaryWriter from tianshou.data import Collector, VectorReplayBuffer from tianshou.env import DummyVectorEnv from tianshou.policy import ImitationPolicy, SACPolicy from tianshou.policy.base import BasePolicy from tianshou.trainer import OffpolicyTrainer from tianshou.utils import TensorboardLogger from tianshou.utils.net.common import Net from tianshou.utils.net.continuous import Actor, ActorProb, Critic from tianshou.utils.space_info import SpaceInfo try: import envpool except ImportError: envpool = None def get_args() -> argparse.Namespace: parser = argparse.ArgumentParser() parser.add_argument("--task", type=str, default="Pendulum-v1") parser.add_argument("--reward-threshold", type=float, default=None) parser.add_argument("--seed", type=int, default=1) parser.add_argument("--buffer-size", type=int, default=20000) parser.add_argument("--actor-lr", type=float, default=1e-3) parser.add_argument("--critic-lr", type=float, default=1e-3) parser.add_argument("--il-lr", type=float, default=1e-3) parser.add_argument("--gamma", type=float, default=0.99) parser.add_argument("--tau", type=float, default=0.005) parser.add_argument("--alpha", type=float, default=0.2) parser.add_argument("--auto-alpha", type=int, default=1) parser.add_argument("--alpha-lr", type=float, default=3e-4) parser.add_argument("--epoch", type=int, default=10) parser.add_argument("--step-per-epoch", type=int, default=24000) parser.add_argument("--il-step-per-epoch", type=int, default=500) parser.add_argument("--step-per-collect", type=int, default=10) parser.add_argument("--update-per-step", type=float, default=0.1) parser.add_argument("--batch-size", type=int, default=128) parser.add_argument("--hidden-sizes", type=int, nargs="*", default=[128, 128]) parser.add_argument("--imitation-hidden-sizes", type=int, nargs="*", default=[128, 128]) parser.add_argument("--training-num", type=int, default=10) parser.add_argument("--test-num", type=int, default=100) parser.add_argument("--logdir", type=str, default="log") parser.add_argument("--render", type=float, default=0.0) parser.add_argument("--n-step", type=int, default=3) parser.add_argument( "--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu", ) return parser.parse_known_args()[0] def test_sac_with_il(args: argparse.Namespace = get_args()) -> None: # if you want to use python vector env, please refer to other test scripts # train_envs = env = envpool.make_gymnasium(args.task, num_envs=args.training_num, seed=args.seed) # test_envs = envpool.make_gymnasium(args.task, num_envs=args.test_num, seed=args.seed) env = gym.make(args.task) train_envs = DummyVectorEnv([lambda: gym.make(args.task) for _ in range(args.training_num)]) test_envs = DummyVectorEnv([lambda: gym.make(args.task) for _ in range(args.test_num)]) space_info = SpaceInfo.from_env(env) args.state_shape = space_info.observation_info.obs_shape args.action_shape = space_info.action_info.action_shape args.max_action = space_info.action_info.max_action if args.reward_threshold is None: default_reward_threshold = {"Pendulum-v0": -250, "Pendulum-v1": -250} args.reward_threshold = default_reward_threshold.get( args.task, env.spec.reward_threshold if env.spec else None, ) # you can also use tianshou.env.SubprocVectorEnv # seed np.random.seed(args.seed) torch.manual_seed(args.seed) train_envs.seed(args.seed) test_envs.seed(args.seed + args.training_num) # model net = Net(state_shape=args.state_shape, hidden_sizes=args.hidden_sizes, device=args.device) actor = ActorProb(net, args.action_shape, device=args.device, unbounded=True).to(args.device) actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr) net_c1 = Net( state_shape=args.state_shape, action_shape=args.action_shape, hidden_sizes=args.hidden_sizes, concat=True, device=args.device, ) critic1 = Critic(net_c1, device=args.device).to(args.device) critic1_optim = torch.optim.Adam(critic1.parameters(), lr=args.critic_lr) net_c2 = Net( state_shape=args.state_shape, action_shape=args.action_shape, hidden_sizes=args.hidden_sizes, concat=True, device=args.device, ) critic2 = Critic(net_c2, device=args.device).to(args.device) critic2_optim = torch.optim.Adam(critic2.parameters(), lr=args.critic_lr) action_dim = space_info.action_info.action_dim if args.auto_alpha: target_entropy = -action_dim log_alpha = torch.zeros(1, requires_grad=True, device=args.device) alpha_optim = torch.optim.Adam([log_alpha], lr=args.alpha_lr) args.alpha = (target_entropy, log_alpha, alpha_optim) policy: SACPolicy = SACPolicy( actor=actor, actor_optim=actor_optim, critic=critic1, critic_optim=critic1_optim, critic2=critic2, critic2_optim=critic2_optim, tau=args.tau, gamma=args.gamma, alpha=args.alpha, estimation_step=args.n_step, action_space=env.action_space, ) # collector train_collector = Collector( policy, train_envs, VectorReplayBuffer(args.buffer_size, len(train_envs)), exploration_noise=True, ) test_collector = Collector(policy, test_envs) # train_collector.collect(n_step=args.buffer_size) # log log_path = os.path.join(args.logdir, args.task, "sac") writer = SummaryWriter(log_path) logger = TensorboardLogger(writer) def save_best_fn(policy: BasePolicy) -> None: torch.save(policy.state_dict(), os.path.join(log_path, "policy.pth")) def stop_fn(mean_rewards: float) -> bool: return mean_rewards >= args.reward_threshold # trainer result = OffpolicyTrainer( policy=policy, train_collector=train_collector, test_collector=test_collector, max_epoch=args.epoch, step_per_epoch=args.step_per_epoch, step_per_collect=args.step_per_collect, episode_per_test=args.test_num, batch_size=args.batch_size, update_per_step=args.update_per_step, stop_fn=stop_fn, save_best_fn=save_best_fn, logger=logger, ).run() assert stop_fn(result.best_reward) # here we define an imitation collector with a trivial policy if args.task.startswith("Pendulum"): args.reward_threshold -= 50 # lower the goal il_net = Net( args.state_shape, hidden_sizes=args.imitation_hidden_sizes, device=args.device, ) il_actor = Actor( il_net, args.action_shape, max_action=args.max_action, device=args.device, ).to(args.device) optim = torch.optim.Adam(il_actor.parameters(), lr=args.il_lr) il_policy: ImitationPolicy = ImitationPolicy( actor=il_actor, optim=optim, action_space=env.action_space, action_scaling=True, action_bound_method="clip", ) il_test_env = gym.make(args.task) il_test_env.reset(seed=args.seed + args.training_num + args.test_num) il_test_collector = Collector( il_policy, # envpool.make_gymnasium(args.task, num_envs=args.test_num, seed=args.seed), il_test_env, ) train_collector.reset() result = OffpolicyTrainer( policy=il_policy, train_collector=train_collector, test_collector=il_test_collector, max_epoch=args.epoch, step_per_epoch=args.il_step_per_epoch, step_per_collect=args.step_per_collect, episode_per_test=args.test_num, batch_size=args.batch_size, stop_fn=stop_fn, save_best_fn=save_best_fn, logger=logger, ).run() assert stop_fn(result.best_reward) if __name__ == "__main__": test_sac_with_il()