How to use the tianshou.policy.DQNPolicy function in tianshou

[lambda: gym.make(args.task) for _ in range(args.training_num)],
        reset_after_done=True)
    # test_envs = gym.make(args.task)
    test_envs = SubprocVectorEnv(
        [lambda: gym.make(args.task) for _ in range(args.test_num)],
        reset_after_done=False)
    # 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.layer_num, args.state_shape, args.action_shape, args.device)
    net = net.to(args.device)
    optim = torch.optim.Adam(net.parameters(), lr=args.lr)
    policy = DQNPolicy(net, optim, args.gamma, args.n_step)
    # collector
    train_collector = Collector(
        policy, train_envs, ReplayBuffer(args.buffer_size))
    test_collector = Collector(policy, test_envs, stat_size=args.test_num)
    train_collector.collect(n_step=args.batch_size)
    # log
    writer = SummaryWriter(args.logdir)

    def stop_fn(x):
        return x >= env.spec.reward_threshold

    def train_fn(x):
        policy.sync_weight()
        policy.set_eps(args.eps_train)

    def test_fn(x):

# you can also use tianshou.env.SubprocVectorEnv
    train_envs = VectorEnv(
        [lambda: gym.make(args.task) for _ in range(args.training_num)])
    # test_envs = gym.make(args.task)
    test_envs = VectorEnv(
        [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.layer_num, args.state_shape,
              args.action_shape, args.device).to(args.device)
    optim = torch.optim.Adam(net.parameters(), lr=args.lr)
    policy = DQNPolicy(
        net, optim, args.gamma, args.n_step,
        target_update_freq=args.target_update_freq)
    # collector
    train_collector = Collector(
        policy, train_envs, ReplayBuffer(args.buffer_size))
    test_collector = Collector(policy, test_envs)
    # policy.set_eps(1)
    train_collector.collect(n_step=args.batch_size)
    # log
    log_path = os.path.join(args.logdir, args.task, 'dqn')
    writer = SummaryWriter(log_path)

    def save_fn(policy):
        torch.save(policy.state_dict(), os.path.join(log_path, 'policy.pth'))

    def stop_fn(x):

# you can also use tianshou.env.SubprocVectorEnv
    train_envs = VectorEnv(
        [lambda: gym.make(args.task)for _ in range(args.training_num)])
    # test_envs = gym.make(args.task)
    test_envs = VectorEnv(
        [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 = Recurrent(args.layer_num, args.state_shape,
                    args.action_shape, args.device).to(args.device)
    optim = torch.optim.Adam(net.parameters(), lr=args.lr)
    policy = DQNPolicy(
        net, optim, args.gamma, args.n_step,
        target_update_freq=args.target_update_freq)
    # collector
    train_collector = Collector(
        policy, train_envs, ReplayBuffer(
            args.buffer_size, stack_num=args.stack_num, ignore_obs_next=True))
    # the stack_num is for RNN training: sample framestack obs
    test_collector = Collector(policy, test_envs)
    # policy.set_eps(1)
    train_collector.collect(n_step=args.batch_size)
    # log
    log_path = os.path.join(args.logdir, args.task, 'drqn')
    writer = SummaryWriter(log_path)

    def save_fn(policy):
        torch.save(policy.state_dict(), os.path.join(log_path, 'policy.pth'))

# you can also use tianshou.env.SubprocVectorEnv
    train_envs = VectorEnv(
        [lambda: gym.make(args.task) for _ in range(args.training_num)])
    # test_envs = gym.make(args.task)
    test_envs = VectorEnv(
        [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.layer_num, args.state_shape,
              args.action_shape, args.device).to(args.device)
    optim = torch.optim.Adam(net.parameters(), lr=args.lr)
    policy = DQNPolicy(
        net, optim, args.gamma, args.n_step,
        target_update_freq=args.target_update_freq)
    # collector
    if args.prioritized_replay > 0:
        buf = PrioritizedReplayBuffer(
            args.buffer_size, alpha=args.alpha,
            beta=args.alpha, repeat_sample=True)
    else:
        buf = ReplayBuffer(args.buffer_size)
    train_collector = Collector(
        policy, train_envs, buf)
    test_collector = Collector(policy, test_envs)
    # policy.set_eps(1)
    train_collector.collect(n_step=args.batch_size)
    # log
    log_path = os.path.join(args.logdir, args.task, 'dqn')

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.layer_num, args.state_shape, args.action_shape,
                  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]

# test_envs = gym.make(args.task)
    test_envs = SubprocVectorEnv([
        lambda: create_atari_environment(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 = DQN(
        args.state_shape[0], args.state_shape[1],
        args.action_shape, args.device)
    net = net.to(args.device)
    optim = torch.optim.Adam(net.parameters(), lr=args.lr)
    policy = DQNPolicy(
        net, optim, args.gamma, args.n_step,
        target_update_freq=args.target_update_freq)
    # collector
    train_collector = Collector(
        policy, train_envs, ReplayBuffer(args.buffer_size),
        preprocess_fn=preprocess_fn)
    test_collector = Collector(policy, test_envs, preprocess_fn=preprocess_fn)
    # policy.set_eps(1)
    train_collector.collect(n_step=args.batch_size * 4)
    print(len(train_collector.buffer))
    # log
    writer = SummaryWriter(args.logdir + '/' + 'dqn')

    def stop_fn(x):
        if env.env.spec.reward_threshold:
            return x >= env.spec.reward_threshold