eb0215cf76
Improves typing in examples and tests, towards mypy passing there. Introduces the SpaceInfo utility
187 lines
6.9 KiB
Python
187 lines
6.9 KiB
Python
import argparse
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import os
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import pickle
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import pprint
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from typing import cast
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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.policy import BasePolicy, DiscreteBCQPolicy
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from tianshou.trainer import OfflineTrainer
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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.discrete import Actor
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from tianshou.utils.space_info import SpaceInfo
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if __name__ == "__main__":
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from gather_cartpole_data import expert_file_name, gather_data
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else: # pytest
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from test.offline.gather_cartpole_data import expert_file_name, gather_data
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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="CartPole-v0")
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parser.add_argument("--reward-threshold", type=float, default=None)
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parser.add_argument("--seed", type=int, default=1626)
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parser.add_argument("--eps-test", type=float, default=0.001)
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parser.add_argument("--lr", type=float, default=3e-4)
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parser.add_argument("--gamma", type=float, default=0.99)
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parser.add_argument("--n-step", type=int, default=3)
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parser.add_argument("--target-update-freq", type=int, default=320)
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parser.add_argument("--unlikely-action-threshold", type=float, default=0.6)
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parser.add_argument("--imitation-logits-penalty", type=float, default=0.01)
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parser.add_argument("--epoch", type=int, default=5)
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parser.add_argument("--update-per-epoch", type=int, default=2000)
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parser.add_argument("--batch-size", type=int, default=64)
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parser.add_argument("--hidden-sizes", type=int, nargs="*", default=[64, 64])
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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("--load-buffer-name", type=str, default=expert_file_name())
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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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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_discrete_bcq(args: argparse.Namespace = get_args()) -> None:
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# envs
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env = gym.make(args.task)
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env.action_space = cast(gym.spaces.Discrete, env.action_space)
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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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if args.reward_threshold is None:
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default_reward_threshold = {"CartPole-v0": 185}
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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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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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test_envs.seed(args.seed)
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# model
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net = Net(args.state_shape, args.hidden_sizes[0], device=args.device)
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policy_net = Actor(
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net,
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args.action_shape,
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hidden_sizes=args.hidden_sizes,
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device=args.device,
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).to(args.device)
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imitation_net = Actor(
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net,
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args.action_shape,
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hidden_sizes=args.hidden_sizes,
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device=args.device,
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).to(args.device)
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actor_critic = ActorCritic(policy_net, imitation_net)
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optim = torch.optim.Adam(actor_critic.parameters(), lr=args.lr)
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policy: DiscreteBCQPolicy = DiscreteBCQPolicy(
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model=policy_net,
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imitator=imitation_net,
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optim=optim,
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action_space=env.action_space,
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discount_factor=args.gamma,
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estimation_step=args.n_step,
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target_update_freq=args.target_update_freq,
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eval_eps=args.eps_test,
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unlikely_action_threshold=args.unlikely_action_threshold,
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imitation_logits_penalty=args.imitation_logits_penalty,
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)
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# buffer
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if os.path.exists(args.load_buffer_name) and os.path.isfile(args.load_buffer_name):
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if args.load_buffer_name.endswith(".hdf5"):
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buffer = VectorReplayBuffer.load_hdf5(args.load_buffer_name)
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else:
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with open(args.load_buffer_name, "rb") as f:
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buffer = pickle.load(f)
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else:
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buffer = gather_data()
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# collector
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test_collector = Collector(policy, test_envs, exploration_noise=True)
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log_path = os.path.join(args.logdir, args.task, "discrete_bcq")
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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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result = OfflineTrainer(
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policy=policy,
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buffer=buffer,
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test_collector=test_collector,
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max_epoch=args.epoch,
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step_per_epoch=args.update_per_epoch,
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episode_per_test=args.test_num,
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batch_size=args.batch_size,
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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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).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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env = gym.make(args.task)
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policy.eval()
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policy.set_eps(args.eps_test)
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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_discrete_bcq_resume(args: argparse.Namespace = get_args()) -> None:
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args.resume = True
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test_discrete_bcq(args)
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if __name__ == "__main__":
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test_discrete_bcq(get_args())
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