6c6c872523
Changes: - Disclaimer in README - Replaced all occurences of Gym with Gymnasium - Removed code that is now dead since we no longer need to support the old step API - Updated type hints to only allow new step API - Increased required version of envpool to support Gymnasium - Increased required version of PettingZoo to support Gymnasium - Updated `PettingZooEnv` to only use the new step API, removed hack to also support old API - I had to add some `# type: ignore` comments, due to new type hinting in Gymnasium. I'm not that familiar with type hinting but I believe that the issue is on the Gymnasium side and we are looking into it. - Had to update `MyTestEnv` to support `options` kwarg - Skip NNI tests because they still use OpenAI Gym - Also allow `PettingZooEnv` in vector environment - Updated doc page about ReplayBuffer to also talk about terminated and truncated flags. Still need to do: - Update the Jupyter notebooks in docs - Check the entire code base for more dead code (from compatibility stuff) - Check the reset functions of all environments/wrappers in code base to make sure they use the `options` kwarg - Someone might want to check test_env_finite.py - Is it okay to allow `PettingZooEnv` in vector environments? Might need to update docs?
278 lines
9.7 KiB
Python
278 lines
9.7 KiB
Python
#!/usr/bin/env python3
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import argparse
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import datetime
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import os
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import pprint
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import d4rl
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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 import nn
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from torch.distributions import Independent, Normal
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from torch.optim.lr_scheduler import LambdaLR
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from torch.utils.tensorboard import SummaryWriter
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from tianshou.data import Batch, Collector, ReplayBuffer, VectorReplayBuffer
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from tianshou.env import SubprocVectorEnv
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from tianshou.policy import GAILPolicy
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from tianshou.trainer import onpolicy_trainer
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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.continuous import ActorProb, Critic
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class NoRewardEnv(gym.RewardWrapper):
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"""sets the reward to 0.
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:param gym.Env env: the environment to wrap.
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"""
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def __init__(self, env):
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super().__init__(env)
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def reward(self, reward):
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"""Set reward to 0."""
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return np.zeros_like(reward)
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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='HalfCheetah-v2')
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parser.add_argument('--seed', type=int, default=0)
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parser.add_argument(
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'--expert-data-task', type=str, default='halfcheetah-expert-v2'
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)
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parser.add_argument('--buffer-size', type=int, default=4096)
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parser.add_argument('--hidden-sizes', type=int, nargs='*', default=[64, 64])
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parser.add_argument('--lr', type=float, default=3e-4)
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parser.add_argument('--disc-lr', type=float, default=2.5e-5)
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parser.add_argument('--gamma', type=float, default=0.99)
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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=30000)
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parser.add_argument('--step-per-collect', type=int, default=2048)
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parser.add_argument('--repeat-per-collect', type=int, default=10)
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parser.add_argument('--disc-update-num', type=int, default=2)
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parser.add_argument('--batch-size', type=int, default=64)
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parser.add_argument('--training-num', type=int, default=64)
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parser.add_argument('--test-num', type=int, default=10)
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# ppo special
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parser.add_argument('--rew-norm', type=int, default=True)
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# In theory, `vf-coef` will not make any difference if using Adam optimizer.
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parser.add_argument('--vf-coef', type=float, default=0.25)
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parser.add_argument('--ent-coef', type=float, default=0.001)
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parser.add_argument('--gae-lambda', type=float, default=0.95)
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parser.add_argument('--bound-action-method', type=str, default="clip")
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parser.add_argument('--lr-decay', type=int, default=True)
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parser.add_argument('--max-grad-norm', type=float, default=0.5)
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parser.add_argument('--eps-clip', type=float, default=0.2)
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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('--norm-adv', type=int, default=0)
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parser.add_argument('--recompute-adv', type=int, default=1)
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parser.add_argument('--logdir', type=str, default='log')
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parser.add_argument('--render', type=float, default=0.)
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parser.add_argument(
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'--device', type=str, 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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parser.add_argument(
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'--watch',
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default=False,
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action='store_true',
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help='watch the play of pre-trained policy only'
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)
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return parser.parse_args()
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def test_gail(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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args.max_action = env.action_space.high[0]
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print("Observations shape:", args.state_shape)
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print("Actions shape:", args.action_shape)
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print("Action range:", np.min(env.action_space.low), np.max(env.action_space.high))
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# train_envs = gym.make(args.task)
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train_envs = SubprocVectorEnv(
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[lambda: NoRewardEnv(gym.make(args.task)) for _ in range(args.training_num)],
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norm_obs=True
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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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[lambda: gym.make(args.task) for _ in range(args.test_num)],
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norm_obs=True,
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obs_rms=train_envs.obs_rms,
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update_obs_rms=False
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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(
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args.state_shape,
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hidden_sizes=args.hidden_sizes,
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activation=nn.Tanh,
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device=args.device
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)
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actor = ActorProb(
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net_a,
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args.action_shape,
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max_action=args.max_action,
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unbounded=True,
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device=args.device
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).to(args.device)
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net_c = Net(
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args.state_shape,
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hidden_sizes=args.hidden_sizes,
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activation=nn.Tanh,
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device=args.device
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)
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critic = Critic(net_c, device=args.device).to(args.device)
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torch.nn.init.constant_(actor.sigma_param, -0.5)
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for m in list(actor.modules()) + list(critic.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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# do last policy layer scaling, this will make initial actions have (close to)
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# 0 mean and std, and will help boost performances,
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# see https://arxiv.org/abs/2006.05990, Fig.24 for details
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for m in actor.mu.modules():
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if isinstance(m, torch.nn.Linear):
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torch.nn.init.zeros_(m.bias)
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m.weight.data.copy_(0.01 * m.weight.data)
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optim = torch.optim.Adam(ActorCritic(actor, critic).parameters(), lr=args.lr)
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# discriminator
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net_d = Net(
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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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activation=nn.Tanh,
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device=args.device,
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concat=True
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)
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disc_net = Critic(net_d, device=args.device).to(args.device)
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for m in disc_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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disc_optim = torch.optim.Adam(disc_net.parameters(), lr=args.disc_lr)
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lr_scheduler = None
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if args.lr_decay:
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# decay learning rate to 0 linearly
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max_update_num = np.ceil(
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args.step_per_epoch / args.step_per_collect
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) * args.epoch
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lr_scheduler = LambdaLR(
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optim, lr_lambda=lambda epoch: 1 - epoch / max_update_num
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)
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def dist(*logits):
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return Independent(Normal(*logits), 1)
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# expert replay buffer
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dataset = d4rl.qlearning_dataset(gym.make(args.expert_data_task))
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dataset_size = dataset['rewards'].size
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print("dataset_size", dataset_size)
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expert_buffer = ReplayBuffer(dataset_size)
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for i in range(dataset_size):
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expert_buffer.add(
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Batch(
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obs=dataset['observations'][i],
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act=dataset['actions'][i],
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rew=dataset['rewards'][i],
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done=dataset['terminals'][i],
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obs_next=dataset['next_observations'][i],
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)
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)
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print("dataset loaded")
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policy = GAILPolicy(
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actor,
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critic,
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optim,
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dist,
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expert_buffer,
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disc_net,
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disc_optim,
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disc_update_num=args.disc_update_num,
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discount_factor=args.gamma,
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gae_lambda=args.gae_lambda,
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max_grad_norm=args.max_grad_norm,
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vf_coef=args.vf_coef,
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ent_coef=args.ent_coef,
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reward_normalization=args.rew_norm,
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action_scaling=True,
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action_bound_method=args.bound_action_method,
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lr_scheduler=lr_scheduler,
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action_space=env.action_space,
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eps_clip=args.eps_clip,
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value_clip=args.value_clip,
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dual_clip=args.dual_clip,
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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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# 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, map_location=args.device))
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print("Loaded agent from: ", args.resume_path)
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# collector
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if args.training_num > 1:
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buffer = VectorReplayBuffer(args.buffer_size, len(train_envs))
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else:
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buffer = ReplayBuffer(args.buffer_size)
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train_collector = Collector(policy, train_envs, buffer, exploration_noise=True)
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test_collector = Collector(policy, test_envs)
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# log
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t0 = datetime.datetime.now().strftime("%m%d_%H%M%S")
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log_file = f'seed_{args.seed}_{t0}-{args.task.replace("-", "_")}_gail'
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log_path = os.path.join(args.logdir, args.task, 'gail', log_file)
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writer = SummaryWriter(log_path)
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writer.add_text("args", str(args))
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logger = TensorboardLogger(writer, update_interval=100, train_interval=100)
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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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if not args.watch:
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# trainer
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result = onpolicy_trainer(
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policy,
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train_collector,
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test_collector,
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args.epoch,
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args.step_per_epoch,
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args.repeat_per_collect,
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args.test_num,
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args.batch_size,
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step_per_collect=args.step_per_collect,
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save_best_fn=save_best_fn,
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logger=logger,
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test_in_train=False
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)
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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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result = test_collector.collect(n_episode=args.test_num, render=args.render)
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print(f'Final reward: {result["rews"].mean()}, length: {result["lens"].mean()}')
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if __name__ == '__main__':
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test_gail()
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