Tianshou/test/multiagent/tic_tac_toe.py

import argparse
import os
from copy import deepcopy
from typing import Optional, Tuple

import numpy as np
import torch
from tic_tac_toe_env import TicTacToeEnv
from torch.utils.tensorboard import SummaryWriter

from tianshou.data import Collector, VectorReplayBuffer
from tianshou.env import DummyVectorEnv
from tianshou.policy import (
    BasePolicy,
    DQNPolicy,
    MultiAgentPolicyManager,
    RandomPolicy,
)
from tianshou.trainer import offpolicy_trainer
from tianshou.utils import TensorboardLogger
from tianshou.utils.net.common import Net


def get_parser() -> argparse.ArgumentParser:
    parser = argparse.ArgumentParser()
    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, help='a smaller gamma favors earlier win'
    )
    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=20)
    parser.add_argument('--step-per-epoch', type=int, default=5000)
    parser.add_argument('--step-per-collect', type=int, default=10)
    parser.add_argument('--update-per-step', type=float, default=0.1)
    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=10)
    parser.add_argument('--test-num', type=int, default=100)
    parser.add_argument('--logdir', type=str, default='log')
    parser.add_argument('--render', type=float, default=0.1)
    parser.add_argument('--board-size', type=int, default=6)
    parser.add_argument('--win-size', type=int, default=4)
    parser.add_argument(
        '--win-rate', type=float, default=0.9, help='the expected winning rate'
    )
    parser.add_argument(
        '--watch',
        default=False,
        action='store_true',
        help='no training, '
        'watch the play of pre-trained models'
    )
    parser.add_argument(
        '--agent-id',
        type=int,
        default=2,
        help='the learned agent plays as the'
        ' agent_id-th player. Choices are 1 and 2.'
    )
    parser.add_argument(
        '--resume-path',
        type=str,
        default='',
        help='the path of agent pth file '
        'for resuming from a pre-trained agent'
    )
    parser.add_argument(
        '--opponent-path',
        type=str,
        default='',
        help='the path of opponent agent pth file '
        'for resuming from a pre-trained agent'
    )
    parser.add_argument(
        '--device', type=str, default='cuda' if torch.cuda.is_available() else 'cpu'
    )
    return parser


def get_args() -> argparse.Namespace:
    parser = get_parser()
    return parser.parse_known_args()[0]


def get_agents(
    args: argparse.Namespace = get_args(),
    agent_learn: Optional[BasePolicy] = None,
    agent_opponent: Optional[BasePolicy] = None,
    optim: Optional[torch.optim.Optimizer] = None,
) -> Tuple[BasePolicy, torch.optim.Optimizer]:
    env = TicTacToeEnv(args.board_size, args.win_size)
    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 agent_learn is None:
        # model
        net = Net(
            args.state_shape,
            args.action_shape,
            hidden_sizes=args.hidden_sizes,
            device=args.device
        ).to(args.device)
        if optim is None:
            optim = torch.optim.Adam(net.parameters(), lr=args.lr)
        agent_learn = DQNPolicy(
            net,
            optim,
            args.gamma,
            args.n_step,
            target_update_freq=args.target_update_freq
        )
        if args.resume_path:
            agent_learn.load_state_dict(torch.load(args.resume_path))

    if agent_opponent is None:
        if args.opponent_path:
            agent_opponent = deepcopy(agent_learn)
            agent_opponent.load_state_dict(torch.load(args.opponent_path))
        else:
            agent_opponent = RandomPolicy()

    if args.agent_id == 1:
        agents = [agent_learn, agent_opponent]
    else:
        agents = [agent_opponent, agent_learn]
    policy = MultiAgentPolicyManager(agents)
    return policy, optim


def train_agent(
    args: argparse.Namespace = get_args(),
    agent_learn: Optional[BasePolicy] = None,
    agent_opponent: Optional[BasePolicy] = None,
    optim: Optional[torch.optim.Optimizer] = None,
) -> Tuple[dict, BasePolicy]:

    def env_func():
        return TicTacToeEnv(args.board_size, args.win_size)

    train_envs = DummyVectorEnv([env_func for _ in range(args.training_num)])
    test_envs = DummyVectorEnv([env_func 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)

    policy, optim = get_agents(
        args, agent_learn=agent_learn, agent_opponent=agent_opponent, optim=optim
    )

    # collector
    train_collector = Collector(
        policy,
        train_envs,
        VectorReplayBuffer(args.buffer_size, len(train_envs)),
        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, 'tic_tac_toe', 'dqn')
    writer = SummaryWriter(log_path)
    writer.add_text("args", str(args))
    logger = TensorboardLogger(writer)

    def save_fn(policy):
        if hasattr(args, 'model_save_path'):
            model_save_path = args.model_save_path
        else:
            model_save_path = os.path.join(
                args.logdir, 'tic_tac_toe', 'dqn', 'policy.pth'
            )
        torch.save(policy.policies[args.agent_id - 1].state_dict(), model_save_path)

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

    def train_fn(epoch, env_step):
        policy.policies[args.agent_id - 1].set_eps(args.eps_train)

    def test_fn(epoch, env_step):
        policy.policies[args.agent_id - 1].set_eps(args.eps_test)

    def reward_metric(rews):
        return rews[:, args.agent_id - 1]

    # 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,
        train_fn=train_fn,
        test_fn=test_fn,
        stop_fn=stop_fn,
        save_fn=save_fn,
        update_per_step=args.update_per_step,
        logger=logger,
        test_in_train=False,
        reward_metric=reward_metric
    )

    return result, policy.policies[args.agent_id - 1]


def watch(
    args: argparse.Namespace = get_args(),
    agent_learn: Optional[BasePolicy] = None,
    agent_opponent: Optional[BasePolicy] = None,
) -> None:
    env = TicTacToeEnv(args.board_size, args.win_size)
    policy, optim = get_agents(
        args, agent_learn=agent_learn, agent_opponent=agent_opponent
    )
    policy.eval()
    policy.policies[args.agent_id - 1].set_eps(args.eps_test)
    collector = Collector(policy, env, exploration_noise=True)
    result = collector.collect(n_episode=1, render=args.render)
    rews, lens = result["rews"], result["lens"]
    print(f"Final reward: {rews[:, args.agent_id - 1].mean()}, length: {lens.mean()}")