Tianshou/test/offline/test_gail.py

import argparse
import os
import pickle
import pprint

import gymnasium as gym
import numpy as np
import torch
from torch.distributions import Distribution, Independent, Normal
from torch.utils.tensorboard import SummaryWriter

from tianshou.data import Collector, VectorReplayBuffer
from tianshou.env import DummyVectorEnv
from tianshou.policy import BasePolicy, GAILPolicy
from tianshou.trainer import OnpolicyTrainer
from tianshou.utils import TensorboardLogger
from tianshou.utils.net.common import ActorCritic, Net
from tianshou.utils.net.continuous import ActorProb, Critic
from tianshou.utils.space_info import SpaceInfo

if __name__ == "__main__":
    from gather_pendulum_data import expert_file_name, gather_data
else:  # pytest
    from test.offline.gather_pendulum_data import expert_file_name, gather_data


def get_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser()
    parser.add_argument("--task", type=str, default="Pendulum-v1")
    parser.add_argument("--reward-threshold", type=float, default=None)
    parser.add_argument("--seed", type=int, default=1)
    parser.add_argument("--buffer-size", type=int, default=20000)
    parser.add_argument("--lr", type=float, default=1e-3)
    parser.add_argument("--disc-lr", type=float, default=5e-4)
    parser.add_argument("--gamma", type=float, default=0.95)
    parser.add_argument("--epoch", type=int, default=5)
    parser.add_argument("--step-per-epoch", type=int, default=150000)
    parser.add_argument("--episode-per-collect", type=int, default=16)
    parser.add_argument("--repeat-per-collect", type=int, default=2)
    parser.add_argument("--disc-update-num", type=int, default=2)
    parser.add_argument("--batch-size", type=int, default=128)
    parser.add_argument("--hidden-sizes", type=int, nargs="*", default=[64, 64])
    parser.add_argument("--training-num", type=int, default=16)
    parser.add_argument("--test-num", type=int, default=100)
    parser.add_argument("--logdir", type=str, default="log")
    parser.add_argument("--render", type=float, default=0.0)
    parser.add_argument(
        "--device",
        type=str,
        default="cuda" if torch.cuda.is_available() else "cpu",
    )
    # ppo special
    parser.add_argument("--vf-coef", type=float, default=0.25)
    parser.add_argument("--ent-coef", type=float, default=0.0)
    parser.add_argument("--eps-clip", type=float, default=0.2)
    parser.add_argument("--max-grad-norm", type=float, default=0.5)
    parser.add_argument("--gae-lambda", type=float, default=0.95)
    parser.add_argument("--rew-norm", type=int, default=1)
    parser.add_argument("--dual-clip", type=float, default=None)
    parser.add_argument("--value-clip", type=int, default=1)
    parser.add_argument("--norm-adv", type=int, default=1)
    parser.add_argument("--recompute-adv", type=int, default=0)
    parser.add_argument("--resume", action="store_true")
    parser.add_argument("--save-interval", type=int, default=4)
    parser.add_argument("--load-buffer-name", type=str, default=expert_file_name())
    return parser.parse_known_args()[0]


def test_gail(args: argparse.Namespace = get_args()) -> None:
    if os.path.exists(args.load_buffer_name) and os.path.isfile(args.load_buffer_name):
        if args.load_buffer_name.endswith(".hdf5"):
            buffer = VectorReplayBuffer.load_hdf5(args.load_buffer_name)
        else:
            with open(args.load_buffer_name, "rb") as f:
                buffer = pickle.load(f)
    else:
        buffer = gather_data()
    env = gym.make(args.task)
    if args.reward_threshold is None:
        default_reward_threshold = {"Pendulum-v0": -1100, "Pendulum-v1": -1100}
        args.reward_threshold = default_reward_threshold.get(
            args.task,
            env.spec.reward_threshold if env.spec else None,
        )
    space_info = SpaceInfo.from_env(env)
    args.state_shape = space_info.observation_info.obs_shape
    args.action_shape = space_info.action_info.action_shape
    args.max_action = space_info.action_info.max_action
    # you can also use tianshou.env.SubprocVectorEnv
    # train_envs = gym.make(args.task)
    train_envs = DummyVectorEnv([lambda: gym.make(args.task) for _ in range(args.training_num)])
    # test_envs = gym.make(args.task)
    test_envs = DummyVectorEnv([lambda: gym.make(args.task) for _ in range(args.test_num)])
    # seed
    np.random.seed(args.seed)
    torch.manual_seed(args.seed)
    train_envs.seed(args.seed)
    test_envs.seed(args.seed)
    # model
    net = Net(args.state_shape, hidden_sizes=args.hidden_sizes, device=args.device)
    actor = ActorProb(net, args.action_shape, max_action=args.max_action, device=args.device).to(
        args.device,
    )
    critic = Critic(
        Net(args.state_shape, hidden_sizes=args.hidden_sizes, device=args.device),
        device=args.device,
    ).to(args.device)
    actor_critic = ActorCritic(actor, critic)
    # orthogonal initialization
    for m in actor_critic.modules():
        if isinstance(m, torch.nn.Linear):
            torch.nn.init.orthogonal_(m.weight)
            torch.nn.init.zeros_(m.bias)
    optim = torch.optim.Adam(actor_critic.parameters(), lr=args.lr)
    # discriminator
    disc_net = Critic(
        Net(
            args.state_shape,
            action_shape=args.action_shape,
            hidden_sizes=args.hidden_sizes,
            activation=torch.nn.Tanh,
            device=args.device,
            concat=True,
        ),
        device=args.device,
    ).to(args.device)
    for m in disc_net.modules():
        if isinstance(m, torch.nn.Linear):
            # orthogonal initialization
            torch.nn.init.orthogonal_(m.weight, gain=np.sqrt(2))
            torch.nn.init.zeros_(m.bias)
    disc_optim = torch.optim.Adam(disc_net.parameters(), lr=args.disc_lr)

    # replace DiagGuassian with Independent(Normal) which is equivalent
    # pass *logits to be consistent with policy.forward
    def dist(loc_scale: tuple[torch.Tensor, torch.Tensor]) -> Distribution:
        loc, scale = loc_scale
        return Independent(Normal(loc, scale), 1)

    policy: BasePolicy = GAILPolicy(
        actor=actor,
        critic=critic,
        optim=optim,
        dist_fn=dist,
        expert_buffer=buffer,
        disc_net=disc_net,
        disc_optim=disc_optim,
        disc_update_num=args.disc_update_num,
        discount_factor=args.gamma,
        max_grad_norm=args.max_grad_norm,
        eps_clip=args.eps_clip,
        vf_coef=args.vf_coef,
        ent_coef=args.ent_coef,
        reward_normalization=args.rew_norm,
        advantage_normalization=args.norm_adv,
        recompute_advantage=args.recompute_adv,
        dual_clip=args.dual_clip,
        value_clip=args.value_clip,
        gae_lambda=args.gae_lambda,
        action_space=env.action_space,
    )
    # collector
    train_collector = Collector(
        policy,
        train_envs,
        VectorReplayBuffer(args.buffer_size, len(train_envs)),
    )
    test_collector = Collector(policy, test_envs)
    # log
    log_path = os.path.join(args.logdir, args.task, "gail")
    writer = SummaryWriter(log_path)
    logger = TensorboardLogger(writer, save_interval=args.save_interval)

    def save_best_fn(policy: BasePolicy) -> None:
        torch.save(policy.state_dict(), os.path.join(log_path, "policy.pth"))

    def stop_fn(mean_rewards: float) -> bool:
        return mean_rewards >= args.reward_threshold

    def save_checkpoint_fn(epoch: int, env_step: int, gradient_step: int) -> str:
        # see also: https://pytorch.org/tutorials/beginner/saving_loading_models.html
        ckpt_path = os.path.join(log_path, "checkpoint.pth")
        # Example: saving by epoch num
        # ckpt_path = os.path.join(log_path, f"checkpoint_{epoch}.pth")
        torch.save(
            {
                "model": policy.state_dict(),
                "optim": optim.state_dict(),
            },
            ckpt_path,
        )
        return ckpt_path

    if args.resume:
        # load from existing checkpoint
        print(f"Loading agent under {log_path}")
        ckpt_path = os.path.join(log_path, "checkpoint.pth")
        if os.path.exists(ckpt_path):
            checkpoint = torch.load(ckpt_path, map_location=args.device)
            policy.load_state_dict(checkpoint["model"])
            optim.load_state_dict(checkpoint["optim"])
            print("Successfully restore policy and optim.")
        else:
            print("Fail to restore policy and optim.")

    # trainer
    result = OnpolicyTrainer(
        policy=policy,
        train_collector=train_collector,
        test_collector=test_collector,
        max_epoch=args.epoch,
        step_per_epoch=args.step_per_epoch,
        repeat_per_collect=args.repeat_per_collect,
        episode_per_test=args.test_num,
        batch_size=args.batch_size,
        episode_per_collect=args.episode_per_collect,
        stop_fn=stop_fn,
        save_best_fn=save_best_fn,
        logger=logger,
        resume_from_log=args.resume,
        save_checkpoint_fn=save_checkpoint_fn,
    ).run()
    assert stop_fn(result.best_reward)

    if __name__ == "__main__":
        pprint.pprint(result)
        # Let's watch its performance!
        env = gym.make(args.task)
        policy.eval()
        collector = Collector(policy, env)
        collector_stats = collector.collect(n_episode=1, render=args.render)
        print(collector_stats)


if __name__ == "__main__":
    test_gail()