600f4bbd55
Preparation for #914 and #920 Changes formatting to ruff and black. Remove python 3.8 ## Additional Changes - Removed flake8 dependencies - Adjusted pre-commit. Now CI and Make use pre-commit, reducing the duplication of linting calls - Removed check-docstyle option (ruff is doing that) - Merged format and lint. In CI the format-lint step fails if any changes are done, so it fulfills the lint functionality. --------- Co-authored-by: Jiayi Weng <jiayi@openai.com>
196 lines
7.1 KiB
Python
196 lines
7.1 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 gym.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 ICMPolicy, PPOPolicy
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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 MLP, ActorCritic, Net
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from tianshou.utils.net.discrete import Actor, Critic, IntrinsicCuriosityModule
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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=1626)
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parser.add_argument("--buffer-size", type=int, default=20000)
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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("--epoch", type=int, default=10)
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parser.add_argument("--step-per-epoch", type=int, default=50000)
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parser.add_argument("--step-per-collect", type=int, default=2000)
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parser.add_argument("--repeat-per-collect", type=int, default=10)
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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=20)
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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=0.5)
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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=0)
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parser.add_argument("--norm-adv", type=int, default=0)
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parser.add_argument("--recompute-adv", type=int, default=0)
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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=0)
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parser.add_argument(
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"--lr-scale",
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type=float,
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default=1.0,
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help="use intrinsic curiosity module with this lr scale",
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)
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parser.add_argument(
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"--reward-scale",
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type=float,
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default=0.01,
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help="scaling factor for intrinsic curiosity reward",
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)
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parser.add_argument(
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"--forward-loss-weight",
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type=float,
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default=0.2,
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help="weight for the forward model loss in ICM",
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)
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return parser.parse_known_args()[0]
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def test_ppo(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(args.state_shape, hidden_sizes=args.hidden_sizes, device=args.device)
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actor = Actor(net, args.action_shape, device=args.device).to(args.device)
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critic = Critic(net, device=args.device).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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dist = torch.distributions.Categorical
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policy = PPOPolicy(
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actor,
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critic,
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optim,
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dist,
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action_scaling=isinstance(env.action_space, Box),
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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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gae_lambda=args.gae_lambda,
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reward_normalization=args.rew_norm,
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dual_clip=args.dual_clip,
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value_clip=args.value_clip,
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action_space=env.action_space,
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deterministic_eval=True,
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advantage_normalization=args.norm_adv,
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recompute_advantage=args.recompute_adv,
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)
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feature_dim = args.hidden_sizes[-1]
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feature_net = MLP(
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np.prod(args.state_shape),
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output_dim=feature_dim,
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hidden_sizes=args.hidden_sizes[:-1],
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device=args.device,
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)
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action_dim = np.prod(args.action_shape)
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icm_net = IntrinsicCuriosityModule(
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feature_net,
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feature_dim,
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action_dim,
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hidden_sizes=args.hidden_sizes[-1:],
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device=args.device,
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).to(args.device)
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icm_optim = torch.optim.Adam(icm_net.parameters(), lr=args.lr)
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policy = ICMPolicy(
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policy,
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icm_net,
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icm_optim,
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args.lr_scale,
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args.reward_scale,
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args.forward_loss_weight,
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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_icm")
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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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step_per_collect=args.step_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_ppo()
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