ca69e79b4a
* Remove flag `eval_mode` from Collector.collect
* Replace flag `is_eval` in BasePolicy with `is_within_training_step` (negating usages)
and set it appropriately in BaseTrainer
164 lines
6.1 KiB
Python
164 lines
6.1 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.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.exploration import OUNoise
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from tianshou.policy import SACPolicy
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from tianshou.policy.base import BasePolicy
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from tianshou.trainer import OffpolicyTrainer
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from tianshou.utils import TensorboardLogger
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from tianshou.utils.net.common import 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="MountainCarContinuous-v0")
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parser.add_argument("--seed", type=int, default=1626)
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parser.add_argument("--buffer-size", type=int, default=50000)
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parser.add_argument("--actor-lr", type=float, default=3e-4)
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parser.add_argument("--critic-lr", type=float, default=3e-4)
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parser.add_argument("--alpha-lr", type=float, default=3e-4)
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parser.add_argument("--noise_std", type=float, default=1.2)
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parser.add_argument("--gamma", type=float, default=0.99)
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parser.add_argument("--tau", type=float, default=0.005)
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parser.add_argument("--auto_alpha", type=int, default=1)
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parser.add_argument("--alpha", type=float, default=0.2)
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parser.add_argument("--epoch", type=int, default=20)
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parser.add_argument("--step-per-epoch", type=int, default=12000)
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parser.add_argument("--step-per-collect", type=int, default=5)
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parser.add_argument("--update-per-step", type=float, default=0.2)
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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=[128, 128])
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parser.add_argument("--training-num", type=int, default=5)
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parser.add_argument("--test-num", type=int, default=10)
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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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return parser.parse_args()
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def test_sac(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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# 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(state_shape=args.state_shape, hidden_sizes=args.hidden_sizes, device=args.device)
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actor = ActorProb(net, args.action_shape, device=args.device, unbounded=True).to(args.device)
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actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr)
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net_c1 = Net(
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state_shape=args.state_shape,
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action_shape=args.action_shape,
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hidden_sizes=args.hidden_sizes,
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concat=True,
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device=args.device,
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)
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critic1 = Critic(net_c1, device=args.device).to(args.device)
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critic1_optim = torch.optim.Adam(critic1.parameters(), lr=args.critic_lr)
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net_c2 = Net(
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state_shape=args.state_shape,
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action_shape=args.action_shape,
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hidden_sizes=args.hidden_sizes,
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concat=True,
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device=args.device,
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)
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critic2 = Critic(net_c2, device=args.device).to(args.device)
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critic2_optim = torch.optim.Adam(critic2.parameters(), lr=args.critic_lr)
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action_dim = space_info.action_info.action_dim
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if args.auto_alpha:
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target_entropy = -action_dim
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log_alpha = torch.zeros(1, requires_grad=True, device=args.device)
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alpha_optim = torch.optim.Adam([log_alpha], lr=args.alpha_lr)
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args.alpha = (target_entropy, log_alpha, alpha_optim)
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policy: SACPolicy = SACPolicy(
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actor=actor,
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actor_optim=actor_optim,
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critic=critic1,
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critic_optim=critic1_optim,
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critic2=critic2,
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critic2_optim=critic2_optim,
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tau=args.tau,
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gamma=args.gamma,
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alpha=args.alpha,
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exploration_noise=OUNoise(0.0, args.noise_std),
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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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exploration_noise=True,
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)
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test_collector = Collector(policy, test_envs)
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# train_collector.collect(n_step=args.buffer_size)
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# log
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log_path = os.path.join(args.logdir, args.task, "sac")
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writer = SummaryWriter(log_path)
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logger = TensorboardLogger(writer)
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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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if env.spec:
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if not env.spec.reward_threshold:
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return False
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else:
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return mean_rewards >= env.spec.reward_threshold
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return False
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# trainer
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result = OffpolicyTrainer(
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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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step_per_collect=args.step_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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update_per_step=args.update_per_step,
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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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).run()
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assert stop_fn(result.best_reward)
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if __name__ == "__main__":
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pprint.pprint(result)
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# Let's watch its performance!
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test_envs.seed(args.seed)
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test_collector.reset()
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collector_stats = test_collector.collect(n_episode=args.test_num, render=args.render)
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print(collector_stats)
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if __name__ == "__main__":
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test_sac()
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