import argparse import os import pprint import gymnasium as gym import numpy as np import torch from gymnasium.spaces import Box from torch.utils.tensorboard import SummaryWriter from tianshou.data import Collector, VectorReplayBuffer from tianshou.env import DummyVectorEnv from tianshou.policy import ICMPolicy, PPOPolicy from tianshou.policy.base import BasePolicy from tianshou.policy.modelfree.ppo import PPOTrainingStats from tianshou.trainer import OnpolicyTrainer from tianshou.utils import TensorboardLogger from tianshou.utils.net.common import MLP, ActorCritic, Net from tianshou.utils.net.discrete import Actor, Critic, IntrinsicCuriosityModule from tianshou.utils.space_info import SpaceInfo def get_args() -> argparse.Namespace: parser = argparse.ArgumentParser() parser.add_argument("--task", type=str, default="CartPole-v0") parser.add_argument("--reward-threshold", type=float, default=None) parser.add_argument("--seed", type=int, default=1626) parser.add_argument("--buffer-size", type=int, default=20000) parser.add_argument("--lr", type=float, default=3e-4) parser.add_argument("--gamma", type=float, default=0.99) parser.add_argument("--epoch", type=int, default=10) parser.add_argument("--step-per-epoch", type=int, default=50000) parser.add_argument("--step-per-collect", type=int, default=2000) parser.add_argument("--repeat-per-collect", type=int, default=10) parser.add_argument("--batch-size", type=int, default=64) parser.add_argument("--hidden-sizes", type=int, nargs="*", default=[64, 64]) parser.add_argument("--training-num", type=int, default=20) 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( "--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu", ) # ppo special parser.add_argument("--vf-coef", type=float, default=0.5) parser.add_argument("--ent-coef", type=float, default=0.0) parser.add_argument("--eps-clip", type=float, default=0.2) parser.add_argument("--max-grad-norm", type=float, default=0.5) parser.add_argument("--gae-lambda", type=float, default=0.95) parser.add_argument("--rew-norm", type=int, default=0) parser.add_argument("--norm-adv", type=int, default=0) parser.add_argument("--recompute-adv", type=int, default=0) parser.add_argument("--dual-clip", type=float, default=None) parser.add_argument("--value-clip", type=int, default=0) parser.add_argument( "--lr-scale", type=float, default=1.0, help="use intrinsic curiosity module with this lr scale", ) parser.add_argument( "--reward-scale", type=float, default=0.01, help="scaling factor for intrinsic curiosity reward", ) parser.add_argument( "--forward-loss-weight", type=float, default=0.2, help="weight for the forward model loss in ICM", ) return parser.parse_known_args()[0] def test_ppo(args: argparse.Namespace = get_args()) -> None: env = gym.make(args.task) space_info = SpaceInfo.from_env(env) args.state_shape = space_info.observation_info.obs_shape args.action_shape = space_info.action_info.action_shape if args.reward_threshold is None: default_reward_threshold = {"CartPole-v0": 195} args.reward_threshold = default_reward_threshold.get( args.task, env.spec.reward_threshold if env.spec else None, ) # train_envs = gym.make(args.task) # you can also use tianshou.env.SubprocVectorEnv train_envs = DummyVectorEnv([lambda: gym.make(args.task) for _ in range(args.training_num)]) # test_envs = gym.make(args.task) test_envs = DummyVectorEnv([lambda: gym.make(args.task) for _ in range(args.test_num)]) # seed np.random.seed(args.seed) torch.manual_seed(args.seed) train_envs.seed(args.seed) test_envs.seed(args.seed) # model net = Net(args.state_shape, hidden_sizes=args.hidden_sizes, device=args.device) actor = Actor(net, args.action_shape, device=args.device).to(args.device) critic = Critic(net, device=args.device).to(args.device) actor_critic = ActorCritic(actor, critic) # orthogonal initialization for m in actor_critic.modules(): if isinstance(m, torch.nn.Linear): torch.nn.init.orthogonal_(m.weight) torch.nn.init.zeros_(m.bias) optim = torch.optim.Adam(actor_critic.parameters(), lr=args.lr) dist = torch.distributions.Categorical policy: PPOPolicy[PPOTrainingStats] = PPOPolicy( actor=actor, critic=critic, optim=optim, dist_fn=dist, action_scaling=isinstance(env.action_space, Box), discount_factor=args.gamma, max_grad_norm=args.max_grad_norm, eps_clip=args.eps_clip, vf_coef=args.vf_coef, ent_coef=args.ent_coef, gae_lambda=args.gae_lambda, reward_normalization=args.rew_norm, dual_clip=args.dual_clip, value_clip=args.value_clip, action_space=env.action_space, deterministic_eval=True, advantage_normalization=args.norm_adv, recompute_advantage=args.recompute_adv, ) feature_dim = args.hidden_sizes[-1] feature_net = MLP( space_info.observation_info.obs_dim, output_dim=feature_dim, hidden_sizes=args.hidden_sizes[:-1], device=args.device, ) action_dim = space_info.action_info.action_dim icm_net = IntrinsicCuriosityModule( feature_net, feature_dim, action_dim, hidden_sizes=args.hidden_sizes[-1:], device=args.device, ).to(args.device) icm_optim = torch.optim.Adam(icm_net.parameters(), lr=args.lr) policy = ICMPolicy( policy=policy, model=icm_net, optim=icm_optim, action_space=env.action_space, lr_scale=args.lr_scale, reward_scale=args.reward_scale, forward_loss_weight=args.forward_loss_weight, ) # collector train_collector = Collector( policy, train_envs, VectorReplayBuffer(args.buffer_size, len(train_envs)), ) test_collector = Collector(policy, test_envs) # log log_path = os.path.join(args.logdir, args.task, "ppo_icm") 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 = OnpolicyTrainer( policy=policy, train_collector=train_collector, test_collector=test_collector, max_epoch=args.epoch, step_per_epoch=args.step_per_epoch, repeat_per_collect=args.repeat_per_collect, episode_per_test=args.test_num, batch_size=args.batch_size, step_per_collect=args.step_per_collect, stop_fn=stop_fn, save_best_fn=save_best_fn, logger=logger, ).run() assert stop_fn(result.best_reward) if __name__ == "__main__": pprint.pprint(result) # Let's watch its performance! env = gym.make(args.task) policy.eval() collector = Collector(policy, env) collector_stats = collector.collect(n_episode=1, render=args.render) print(collector_stats) if __name__ == "__main__": test_ppo()