Tianshou/test/discrete/test_c51.py

import argparse
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, PrioritizedVectorReplayBuffer, VectorReplayBuffer
from tianshou.env import DummyVectorEnv
from tianshou.policy import C51Policy
from tianshou.trainer import offpolicy_trainer
from tianshou.utils import TensorboardLogger
from tianshou.utils.net.common import Net


def get_args():
    parser = argparse.ArgumentParser()
    parser.add_argument('--task', type=str, default='CartPole-v0')
    parser.add_argument('--reward-threshold', type=float, default=None)
    parser.add_argument('--seed', type=int, default=1626)
    parser.add_argument('--eps-test', type=float, default=0.05)
    parser.add_argument('--eps-train', type=float, default=0.1)
    parser.add_argument('--buffer-size', type=int, default=20000)
    parser.add_argument('--lr', type=float, default=1e-3)
    parser.add_argument('--gamma', type=float, default=0.9)
    parser.add_argument('--num-atoms', type=int, default=51)
    parser.add_argument('--v-min', type=float, default=-10.)
    parser.add_argument('--v-max', type=float, default=10.)
    parser.add_argument('--n-step', type=int, default=3)
    parser.add_argument('--target-update-freq', type=int, default=320)
    parser.add_argument('--epoch', type=int, default=10)
    parser.add_argument('--step-per-epoch', type=int, default=8000)
    parser.add_argument('--step-per-collect', type=int, default=8)
    parser.add_argument('--update-per-step', type=float, default=0.125)
    parser.add_argument('--batch-size', type=int, default=64)
    parser.add_argument(
        '--hidden-sizes', type=int, nargs='*', default=[128, 128, 128, 128]
    )
    parser.add_argument('--training-num', type=int, default=8)
    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.)
    parser.add_argument('--prioritized-replay', action="store_true", default=False)
    parser.add_argument('--alpha', type=float, default=0.6)
    parser.add_argument('--beta', type=float, default=0.4)
    parser.add_argument('--resume', action="store_true")
    parser.add_argument(
        '--device', type=str, default='cuda' if torch.cuda.is_available() else 'cpu'
    )
    parser.add_argument("--save-interval", type=int, default=4)
    args = parser.parse_known_args()[0]
    return args


def test_c51(args=get_args()):
    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
    if args.reward_threshold is None:
        default_reward_threshold = {"CartPole-v0": 195}
        args.reward_threshold = default_reward_threshold.get(
            args.task, env.spec.reward_threshold
        )
    # train_envs = gym.make(args.task)
    # you can also use tianshou.env.SubprocVectorEnv
    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,
        args.action_shape,
        hidden_sizes=args.hidden_sizes,
        device=args.device,
        softmax=True,
        num_atoms=args.num_atoms,
    )
    optim = torch.optim.Adam(net.parameters(), lr=args.lr)
    policy = C51Policy(
        net,
        optim,
        args.gamma,
        args.num_atoms,
        args.v_min,
        args.v_max,
        args.n_step,
        target_update_freq=args.target_update_freq,
    ).to(args.device)
    # buffer
    if args.prioritized_replay:
        buf = PrioritizedVectorReplayBuffer(
            args.buffer_size,
            buffer_num=len(train_envs),
            alpha=args.alpha,
            beta=args.beta,
        )
    else:
        buf = VectorReplayBuffer(args.buffer_size, buffer_num=len(train_envs))
    # collector
    train_collector = Collector(policy, train_envs, buf, exploration_noise=True)
    test_collector = Collector(policy, test_envs, exploration_noise=True)
    # policy.set_eps(1)
    train_collector.collect(n_step=args.batch_size * args.training_num)
    # log
    log_path = os.path.join(args.logdir, args.task, "c51")
    writer = SummaryWriter(log_path)
    logger = TensorboardLogger(writer, save_interval=args.save_interval)

    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

    def train_fn(epoch, env_step):
        # eps annnealing, just a demo
        if env_step <= 10000:
            policy.set_eps(args.eps_train)
        elif env_step <= 50000:
            eps = args.eps_train - (env_step - 10000) / \
                40000 * (0.9 * args.eps_train)
            policy.set_eps(eps)
        else:
            policy.set_eps(0.1 * args.eps_train)

    def test_fn(epoch, env_step):
        policy.set_eps(args.eps_test)

    def save_checkpoint_fn(epoch, env_step, gradient_step):
        # 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
        )
        buffer_path = os.path.join(log_path, "train_buffer.pkl")
        pickle.dump(train_collector.buffer, open(buffer_path, "wb"))
        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"])
            policy.optim.load_state_dict(checkpoint["optim"])
            print("Successfully restore policy and optim.")
        else:
            print("Fail to restore policy and optim.")
        buffer_path = os.path.join(log_path, "train_buffer.pkl")
        if os.path.exists(buffer_path):
            train_collector.buffer = pickle.load(open(buffer_path, "rb"))
            print("Successfully restore buffer.")
        else:
            print("Fail to restore buffer.")

    # trainer
    result = offpolicy_trainer(
        policy,
        train_collector,
        test_collector,
        args.epoch,
        args.step_per_epoch,
        args.step_per_collect,
        args.test_num,
        args.batch_size,
        update_per_step=args.update_per_step,
        train_fn=train_fn,
        test_fn=test_fn,
        stop_fn=stop_fn,
        save_best_fn=save_best_fn,
        logger=logger,
        resume_from_log=args.resume,
        save_checkpoint_fn=save_checkpoint_fn,
    )
    assert stop_fn(result["best_reward"])

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


def test_c51_resume(args=get_args()):
    args.resume = True
    test_c51(args)


def test_pc51(args=get_args()):
    args.prioritized_replay = True
    args.gamma = .95
    args.seed = 1
    test_c51(args)


if __name__ == "__main__":
    test_c51(get_args())