Tianshou/test/offline/test_td3_bc.py

import argparse
import datetime
import os
import pickle
import pprint

import gymnasium as gym
import numpy as np
import torch
from torch.utils.tensorboard import SummaryWriter

from tianshou.data import Collector, VectorReplayBuffer
from tianshou.env import DummyVectorEnv
from tianshou.exploration import GaussianNoise
from tianshou.policy import TD3BCPolicy
from tianshou.trainer import OfflineTrainer
from tianshou.utils import TensorboardLogger
from tianshou.utils.net.common import Net
from tianshou.utils.net.continuous import Actor, Critic

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():
    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=0)
    parser.add_argument("--hidden-sizes", type=int, nargs="*", default=[64, 64])
    parser.add_argument("--actor-lr", type=float, default=1e-3)
    parser.add_argument("--critic-lr", type=float, default=1e-3)
    parser.add_argument("--epoch", type=int, default=5)
    parser.add_argument("--step-per-epoch", type=int, default=500)
    parser.add_argument("--n-step", type=int, default=3)
    parser.add_argument("--batch-size", type=int, default=64)
    parser.add_argument("--alpha", type=float, default=2.5)
    parser.add_argument("--exploration-noise", type=float, default=0.1)
    parser.add_argument("--policy-noise", type=float, default=0.2)
    parser.add_argument("--noise-clip", type=float, default=0.5)
    parser.add_argument("--update-actor-freq", type=int, default=2)
    parser.add_argument("--tau", type=float, default=0.005)
    parser.add_argument("--gamma", type=float, default=0.99)

    parser.add_argument("--eval-freq", type=int, default=1)
    parser.add_argument("--test-num", type=int, default=10)
    parser.add_argument("--logdir", type=str, default="log")
    parser.add_argument("--render", type=float, default=1 / 35)
    parser.add_argument(
        "--device",
        type=str,
        default="cuda" if torch.cuda.is_available() else "cpu",
    )
    parser.add_argument("--resume-path", type=str, default=None)
    parser.add_argument(
        "--watch",
        default=False,
        action="store_true",
        help="watch the play of pre-trained policy only",
    )
    parser.add_argument("--load-buffer-name", type=str, default=expert_file_name())
    return parser.parse_known_args()[0]


def test_td3_bc(args=get_args()):
    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)
    args.state_shape = env.observation_space.shape or env.observation_space.n
    args.action_shape = env.action_space.shape or env.action_space.n
    args.max_action = env.action_space.high[0]  # float
    if args.reward_threshold is None:
        # too low?
        default_reward_threshold = {"Pendulum-v0": -1200, "Pendulum-v1": -1200}
        args.reward_threshold = default_reward_threshold.get(args.task, env.spec.reward_threshold)

    args.state_dim = args.state_shape[0]
    args.action_dim = args.action_shape[0]
    # 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)
    test_envs.seed(args.seed)

    # model
    # actor network
    net_a = Net(
        args.state_shape,
        hidden_sizes=args.hidden_sizes,
        device=args.device,
    )
    actor = Actor(
        net_a,
        action_shape=args.action_shape,
        max_action=args.max_action,
        device=args.device,
    ).to(args.device)
    actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr)

    # critic network
    net_c1 = Net(
        args.state_shape,
        args.action_shape,
        hidden_sizes=args.hidden_sizes,
        concat=True,
        device=args.device,
    )
    net_c2 = Net(
        args.state_shape,
        args.action_shape,
        hidden_sizes=args.hidden_sizes,
        concat=True,
        device=args.device,
    )
    critic1 = Critic(net_c1, device=args.device).to(args.device)
    critic1_optim = torch.optim.Adam(critic1.parameters(), lr=args.critic_lr)
    critic2 = Critic(net_c2, device=args.device).to(args.device)
    critic2_optim = torch.optim.Adam(critic2.parameters(), lr=args.critic_lr)

    policy = TD3BCPolicy(
        actor=actor,
        actor_optim=actor_optim,
        critic=critic1,
        critic_optim=critic1_optim,
        critic2=critic2,
        critic2_optim=critic2_optim,
        tau=args.tau,
        gamma=args.gamma,
        exploration_noise=GaussianNoise(sigma=args.exploration_noise),
        policy_noise=args.policy_noise,
        update_actor_freq=args.update_actor_freq,
        noise_clip=args.noise_clip,
        alpha=args.alpha,
        estimation_step=args.n_step,
        action_space=env.action_space,
    )

    # load a previous policy
    if args.resume_path:
        policy.load_state_dict(torch.load(args.resume_path, map_location=args.device))
        print("Loaded agent from: ", args.resume_path)

    # collector
    # buffer has been gathered
    # train_collector = Collector(policy, train_envs, buffer, exploration_noise=True)
    test_collector = Collector(policy, test_envs)
    # log
    t0 = datetime.datetime.now().strftime("%m%d_%H%M%S")
    log_file = f'seed_{args.seed}_{t0}-{args.task.replace("-", "_")}_td3_bc'
    log_path = os.path.join(args.logdir, args.task, "td3_bc", log_file)
    writer = SummaryWriter(log_path)
    writer.add_text("args", str(args))
    logger = TensorboardLogger(writer)

    def save_best_fn(policy):
        torch.save(policy.state_dict(), os.path.join(log_path, "policy.pth"))

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

    # trainer
    trainer = OfflineTrainer(
        policy=policy,
        buffer=buffer,
        test_collector=test_collector,
        max_epoch=args.epoch,
        step_per_epoch=args.step_per_epoch,
        episode_per_test=args.test_num,
        batch_size=args.batch_size,
        save_best_fn=save_best_fn,
        stop_fn=stop_fn,
        logger=logger,
    )

    for epoch, epoch_stat, info in trainer:
        print(f"Epoch: {epoch}")
        print(epoch_stat)
        print(info)

    assert stop_fn(info["best_reward"])

    # Let's watch its performance!
    if __name__ == "__main__":
        pprint.pprint(info)
        env = gym.make(args.task)
        policy.eval()
        collector = Collector(policy, env)
        collector_result = collector.collect(n_episode=1, render=args.render)
        rews, lens = collector_result["rews"], collector_result["lens"]
        print(f"Final reward: {rews.mean()}, length: {lens.mean()}")


if __name__ == "__main__":
    test_td3_bc()