bf0d632108
Closes #917 ### Internal Improvements - Better variable names related to model outputs (logits, dist input etc.). #1032 - Improved typing for actors and critics, using Tianshou classes like `Actor`, `ActorProb`, etc., instead of just `nn.Module`. #1032 - Added interfaces for most `Actor` and `Critic` classes to enforce the presence of `forward` methods. #1032 - Simplified `PGPolicy` forward by unifying the `dist_fn` interface (see associated breaking change). #1032 - Use `.mode` of distribution instead of relying on knowledge of the distribution type. #1032 ### Breaking Changes - Changed interface of `dist_fn` in `PGPolicy` and all subclasses to take a single argument in both continuous and discrete cases. #1032 --------- Co-authored-by: Arnau Jimenez <arnau.jimenez@zeiss.com> Co-authored-by: Michael Panchenko <m.panchenko@appliedai.de>
271 lines
10 KiB
Python
271 lines
10 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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from typing import SupportsFloat
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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 Distribution, 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, VectorEnvNormObs
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from tianshou.policy import GAILPolicy
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from tianshou.policy.base import BasePolicy
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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 ActorCritic, 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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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: gym.Env) -> None:
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super().__init__(env)
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def reward(self, reward: SupportsFloat) -> np.ndarray:
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"""Set reward to 0."""
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return np.zeros_like(reward)
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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="HalfCheetah-v2")
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parser.add_argument("--seed", type=int, default=0)
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parser.add_argument("--expert-data-task", type=str, default="halfcheetah-expert-v2")
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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=0.0)
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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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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: argparse.Namespace = get_args()) -> None:
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env = 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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print("Observations shape:", args.state_shape)
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print("Actions shape:", args.action_shape)
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print("Action range:", args.min_action, args.max_action)
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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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)
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train_envs = VectorEnvNormObs(train_envs)
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# test_envs = gym.make(args.task)
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test_envs = SubprocVectorEnv([lambda: gym.make(args.task) for _ in range(args.test_num)])
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test_envs = VectorEnvNormObs(test_envs, update_obs_rms=False)
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test_envs.set_obs_rms(train_envs.get_obs_rms())
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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(net_a, args.action_shape, unbounded=True, device=args.device).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(args.step_per_epoch / args.step_per_collect) * args.epoch
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lr_scheduler = LambdaLR(optim, lr_lambda=lambda epoch: 1 - epoch / max_update_num)
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def dist(loc_scale: tuple[torch.Tensor, torch.Tensor]) -> Distribution:
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loc, scale = loc_scale
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return Independent(Normal(loc, scale), 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 = GAILPolicy(
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actor=actor,
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critic=critic,
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optim=optim,
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dist_fn=dist,
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expert_buffer=expert_buffer,
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disc_net=disc_net,
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disc_optim=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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buffer: ReplayBuffer
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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: BasePolicy) -> None:
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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 = 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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save_best_fn=save_best_fn,
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logger=logger,
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test_in_train=False,
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).run()
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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_gail()
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