2cc34fb72b
Closes #914 Additional changes: - Deprecate python below 11 - Remove 3rd party and throughput tests. This simplifies install and test pipeline - Remove gym compatibility and shimmy - Format with 3.11 conventions. In particular, add `zip(..., strict=True/False)` where possible Since the additional tests and gym were complicating the CI pipeline (flaky and dist-dependent), it didn't make sense to work on fixing the current tests in this PR to then just delete them in the next one. So this PR changes the build and removes these tests at the same time.
135 lines
4.8 KiB
Python
135 lines
4.8 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 gymnasium.spaces import Box
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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 PGPolicy
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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 Net
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def get_args():
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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=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=10)
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parser.add_argument("--step-per-epoch", type=int, default=40000)
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parser.add_argument("--episode-per-collect", type=int, default=8)
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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=64)
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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=8)
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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("--rew-norm", type=int, default=1)
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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_known_args()[0]
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def test_pg(args=get_args()):
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env = gym.make(args.task)
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args.state_shape = env.observation_space.shape or env.observation_space.n
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args.action_shape = env.action_space.shape or env.action_space.n
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if args.reward_threshold is None:
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default_reward_threshold = {"CartPole-v0": 195}
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args.reward_threshold = default_reward_threshold.get(args.task, env.spec.reward_threshold)
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# train_envs = gym.make(args.task)
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# you can also use tianshou.env.SubprocVectorEnv
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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(
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args.state_shape,
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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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softmax=True,
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).to(args.device)
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optim = torch.optim.Adam(net.parameters(), lr=args.lr)
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dist = torch.distributions.Categorical
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policy = PGPolicy(
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net,
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optim,
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dist,
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args.gamma,
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action_scaling=isinstance(env.action_space, Box),
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reward_normalization=args.rew_norm,
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action_space=env.action_space,
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)
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for m in net.modules():
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if isinstance(m, torch.nn.Linear):
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# orthogonal initialization
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torch.nn.init.orthogonal_(m.weight, gain=np.sqrt(2))
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torch.nn.init.zeros_(m.bias)
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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, "pg")
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writer = SummaryWriter(log_path)
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logger = TensorboardLogger(writer)
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def save_best_fn(policy):
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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):
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return mean_rewards >= args.reward_threshold
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# trainer
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result = 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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).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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collector = Collector(policy, env)
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result = collector.collect(n_episode=1, render=args.render)
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rews, lens = result["rews"], result["lens"]
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print(f"Final reward: {rews.mean()}, length: {lens.mean()}")
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if __name__ == "__main__":
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test_pg()
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