eb0215cf76
Improves typing in examples and tests, towards mypy passing there. Introduces the SpaceInfo utility
210 lines
8.0 KiB
Python
210 lines
8.0 KiB
Python
import argparse
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import os
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import pprint
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import gymnasium as gym
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import numpy as np
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import torch
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from torch.distributions import Distribution, Independent, Normal
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from torch.utils.tensorboard import SummaryWriter
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from tianshou.data import Collector, VectorReplayBuffer
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from tianshou.env import DummyVectorEnv
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from tianshou.policy import PPOPolicy
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from tianshou.policy.base import BasePolicy
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from tianshou.policy.modelfree.ppo import PPOTrainingStats
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from tianshou.trainer import OnpolicyTrainer
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from tianshou.utils import TensorboardLogger
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from tianshou.utils.net.common import ActorCritic, Net
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from tianshou.utils.net.continuous import ActorProb, Critic
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from tianshou.utils.space_info import SpaceInfo
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def get_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser()
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parser.add_argument("--task", type=str, default="Pendulum-v1")
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parser.add_argument("--reward-threshold", type=float, default=None)
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parser.add_argument("--seed", type=int, default=1)
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parser.add_argument("--buffer-size", type=int, default=20000)
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parser.add_argument("--lr", type=float, default=1e-3)
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parser.add_argument("--gamma", type=float, default=0.95)
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parser.add_argument("--epoch", type=int, default=5)
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parser.add_argument("--step-per-epoch", type=int, default=150000)
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parser.add_argument("--episode-per-collect", type=int, default=16)
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parser.add_argument("--repeat-per-collect", type=int, default=2)
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parser.add_argument("--batch-size", type=int, default=128)
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parser.add_argument("--hidden-sizes", type=int, nargs="*", default=[64, 64])
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parser.add_argument("--training-num", type=int, default=16)
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parser.add_argument("--test-num", type=int, default=100)
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parser.add_argument("--logdir", type=str, default="log")
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parser.add_argument("--render", type=float, default=0.0)
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parser.add_argument(
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"--device",
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type=str,
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default="cuda" if torch.cuda.is_available() else "cpu",
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)
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# ppo special
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parser.add_argument("--vf-coef", type=float, default=0.25)
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parser.add_argument("--ent-coef", type=float, default=0.0)
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parser.add_argument("--eps-clip", type=float, default=0.2)
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parser.add_argument("--max-grad-norm", type=float, default=0.5)
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parser.add_argument("--gae-lambda", type=float, default=0.95)
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parser.add_argument("--rew-norm", type=int, default=1)
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parser.add_argument("--dual-clip", type=float, default=None)
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parser.add_argument("--value-clip", type=int, default=1)
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parser.add_argument("--norm-adv", type=int, default=1)
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parser.add_argument("--recompute-adv", type=int, default=0)
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parser.add_argument("--resume", action="store_true")
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parser.add_argument("--save-interval", type=int, default=4)
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return parser.parse_known_args()[0]
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def test_ppo(args: argparse.Namespace = get_args()) -> None:
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env = gym.make(args.task)
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space_info = SpaceInfo.from_env(env)
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args.state_shape = space_info.observation_info.obs_shape
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args.action_shape = space_info.action_info.action_shape
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args.max_action = space_info.action_info.max_action
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if args.reward_threshold is None:
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default_reward_threshold = {"Pendulum-v0": -250, "Pendulum-v1": -250}
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args.reward_threshold = default_reward_threshold.get(
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args.task,
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env.spec.reward_threshold if env.spec else None,
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)
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# you can also use tianshou.env.SubprocVectorEnv
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# train_envs = gym.make(args.task)
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train_envs = DummyVectorEnv([lambda: gym.make(args.task) for _ in range(args.training_num)])
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# test_envs = gym.make(args.task)
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test_envs = DummyVectorEnv([lambda: gym.make(args.task) for _ in range(args.test_num)])
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# seed
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np.random.seed(args.seed)
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torch.manual_seed(args.seed)
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train_envs.seed(args.seed)
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test_envs.seed(args.seed)
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# model
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net = Net(args.state_shape, hidden_sizes=args.hidden_sizes, device=args.device)
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actor = ActorProb(net, args.action_shape, unbounded=True, device=args.device).to(args.device)
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critic = Critic(
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Net(args.state_shape, hidden_sizes=args.hidden_sizes, device=args.device),
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device=args.device,
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).to(args.device)
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actor_critic = ActorCritic(actor, critic)
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# orthogonal initialization
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for m in actor_critic.modules():
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if isinstance(m, torch.nn.Linear):
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torch.nn.init.orthogonal_(m.weight)
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torch.nn.init.zeros_(m.bias)
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optim = torch.optim.Adam(actor_critic.parameters(), lr=args.lr)
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# replace DiagGuassian with Independent(Normal) which is equivalent
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# pass *logits to be consistent with policy.forward
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def dist(*logits: torch.Tensor) -> Distribution:
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return Independent(Normal(*logits), 1)
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policy: PPOPolicy[PPOTrainingStats] = PPOPolicy(
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actor=actor,
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critic=critic,
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optim=optim,
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dist_fn=dist,
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discount_factor=args.gamma,
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max_grad_norm=args.max_grad_norm,
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eps_clip=args.eps_clip,
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vf_coef=args.vf_coef,
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ent_coef=args.ent_coef,
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reward_normalization=args.rew_norm,
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advantage_normalization=args.norm_adv,
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recompute_advantage=args.recompute_adv,
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dual_clip=args.dual_clip,
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value_clip=args.value_clip,
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gae_lambda=args.gae_lambda,
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action_space=env.action_space,
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)
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# collector
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train_collector = Collector(
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policy,
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train_envs,
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VectorReplayBuffer(args.buffer_size, len(train_envs)),
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)
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test_collector = Collector(policy, test_envs)
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# log
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log_path = os.path.join(args.logdir, args.task, "ppo")
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writer = SummaryWriter(log_path)
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logger = TensorboardLogger(writer, save_interval=args.save_interval)
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def save_best_fn(policy: BasePolicy) -> None:
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torch.save(policy.state_dict(), os.path.join(log_path, "policy.pth"))
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def stop_fn(mean_rewards: float) -> bool:
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return mean_rewards >= args.reward_threshold
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def save_checkpoint_fn(epoch: int, env_step: int, gradient_step: int) -> str:
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# see also: https://pytorch.org/tutorials/beginner/saving_loading_models.html
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ckpt_path = os.path.join(log_path, "checkpoint.pth")
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# Example: saving by epoch num
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# ckpt_path = os.path.join(log_path, f"checkpoint_{epoch}.pth")
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torch.save(
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{
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"model": policy.state_dict(),
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"optim": optim.state_dict(),
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},
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ckpt_path,
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)
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return ckpt_path
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if args.resume:
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# load from existing checkpoint
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print(f"Loading agent under {log_path}")
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ckpt_path = os.path.join(log_path, "checkpoint.pth")
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if os.path.exists(ckpt_path):
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checkpoint = torch.load(ckpt_path, map_location=args.device)
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policy.load_state_dict(checkpoint["model"])
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optim.load_state_dict(checkpoint["optim"])
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print("Successfully restore policy and optim.")
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else:
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print("Fail to restore policy and optim.")
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# trainer
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trainer = OnpolicyTrainer(
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policy=policy,
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train_collector=train_collector,
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test_collector=test_collector,
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max_epoch=args.epoch,
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step_per_epoch=args.step_per_epoch,
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repeat_per_collect=args.repeat_per_collect,
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episode_per_test=args.test_num,
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batch_size=args.batch_size,
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episode_per_collect=args.episode_per_collect,
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stop_fn=stop_fn,
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save_best_fn=save_best_fn,
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logger=logger,
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resume_from_log=args.resume,
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save_checkpoint_fn=save_checkpoint_fn,
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)
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for epoch_stat in trainer:
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print(f"Epoch: {epoch_stat.epoch}")
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print(epoch_stat)
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# print(info)
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assert stop_fn(epoch_stat.info_stat.best_reward)
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if __name__ == "__main__":
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pprint.pprint(epoch_stat)
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# Let's watch its performance!
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env = gym.make(args.task)
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policy.eval()
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collector = Collector(policy, env)
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collector_stats = collector.collect(n_episode=1, render=args.render)
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print(collector_stats)
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def test_ppo_resume(args: argparse.Namespace = get_args()) -> None:
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args.resume = True
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test_ppo(args)
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if __name__ == "__main__":
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test_ppo()
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