5fe9aea798
Addresses part of #1015 ### Dependencies - move jsonargparse and docstring-parser to dependencies to run hl examples without dev - create mujoco-py extra for legacy mujoco envs - updated atari extra - removed atari-py and gym dependencies - added ALE-py, autorom, and shimmy - created robotics extra for HER-DDPG ### Mac specific - only install envpool when not on mac - mujoco-py not working on macOS newer than Monterey (https://github.com/openai/mujoco-py/issues/777) - D4RL also fails due to dependency on mujoco-py (https://github.com/Farama-Foundation/D4RL/issues/232) ### Other - reduced training-num/test-num in example files to a number ≤ 20 (examples with 100 led to too many open files) - rendering for Mujoco envs needs to be fixed on gymnasium side (https://github.com/Farama-Foundation/Gymnasium/issues/749) --------- Co-authored-by: Maximilian Huettenrauch <m.huettenrauch@appliedai.de> Co-authored-by: Michael Panchenko <35432522+MischaPanch@users.noreply.github.com>
214 lines
7.4 KiB
Python
214 lines
7.4 KiB
Python
import argparse
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import os
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import pprint
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from typing import Any
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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.core import WrapperActType, WrapperObsType
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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 SubprocVectorEnv
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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="BipedalWalkerHardcore-v3")
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parser.add_argument("--seed", type=int, default=0)
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parser.add_argument("--buffer-size", type=int, default=1000000)
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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=1e-3)
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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("--alpha", type=float, default=0.1)
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parser.add_argument("--auto-alpha", type=int, default=1)
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parser.add_argument("--alpha-lr", type=float, default=3e-4)
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parser.add_argument("--epoch", type=int, default=100)
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parser.add_argument("--step-per-epoch", type=int, default=100000)
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parser.add_argument("--step-per-collect", type=int, default=10)
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parser.add_argument("--update-per-step", type=float, default=0.1)
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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=10)
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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("--n-step", type=int, default=4)
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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-path", type=str, default=None)
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return parser.parse_args()
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class Wrapper(gym.Wrapper):
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"""Env wrapper for reward scale, action repeat and removing done penalty."""
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def __init__(
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self,
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env: gym.Env,
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action_repeat: int = 3,
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reward_scale: int = 5,
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rm_done: bool = True,
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) -> None:
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super().__init__(env)
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self.action_repeat = action_repeat
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self.reward_scale = reward_scale
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self.rm_done = rm_done
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def step(
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self,
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action: WrapperActType,
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) -> tuple[WrapperObsType, float, bool, bool, dict[str, Any]]:
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rew_sum = 0.0
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for _ in range(self.action_repeat):
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obs, rew, terminated, truncated, info = self.env.step(action)
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done = terminated | truncated
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# remove done reward penalty
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if not done or not self.rm_done:
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rew_sum = rew_sum + float(rew)
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if done:
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break
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# scale reward
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return obs, self.reward_scale * rew_sum, terminated, truncated, info
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def test_sac_bipedal(args: argparse.Namespace = get_args()) -> None:
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env = Wrapper(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 = SubprocVectorEnv(
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[lambda: Wrapper(gym.make(args.task)) for _ in range(args.training_num)],
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)
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# test_envs = gym.make(args.task)
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test_envs = SubprocVectorEnv(
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[
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lambda: Wrapper(gym.make(args.task), reward_scale=1, rm_done=False)
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for _ in range(args.test_num)
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],
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)
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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_a = Net(args.state_shape, hidden_sizes=args.hidden_sizes, device=args.device)
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actor = ActorProb(net_a, 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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args.state_shape,
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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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args.state_shape,
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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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estimation_step=args.n_step,
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action_space=env.action_space,
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)
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# load a previous policy
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if args.resume_path:
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policy.load_state_dict(torch.load(args.resume_path))
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print("Loaded agent from: ", args.resume_path)
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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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test_in_train=False,
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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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if __name__ == "__main__":
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pprint.pprint(result)
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# Let's watch its performance!
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policy.eval()
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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_bipedal()
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