from easydict import EasyDict

# ==============================================================
# begin of the most frequently changed config specified by the user
# ==============================================================
collector_env_num = 32
n_episode = 32
evaluator_env_num = 5
num_simulations = 50
update_per_collect = 50
reanalyze_ratio = 0.
batch_size = 256
max_env_step = int(1e6)

board_size = 6  # default_size is 15
bot_action_type = 'v0'  # options={'v0', 'v1'}
prob_random_action_in_bot = 0.5
# ==============================================================
# end of the most frequently changed config specified by the user
# ==============================================================

cfg = dict(
    main_config=dict(
        exp_name='Gomoku-play-with-bot-MuZero',
        seed=0,
        env=dict(
            env_id='Gomoku-play-with-bot',
            battle_mode='play_with_bot_mode',
            render_mode='image_savefile_mode',
            replay_format='mp4',
            board_size=board_size,
            bot_action_type=bot_action_type,
            prob_random_action_in_bot=prob_random_action_in_bot,
            channel_last=True,
            collector_env_num=collector_env_num,
            evaluator_env_num=evaluator_env_num,
            n_evaluator_episode=evaluator_env_num,
            manager=dict(shared_memory=False, ),
        ),
        policy=dict(
            model=dict(
                observation_shape=(3, board_size, board_size),
                action_space_size=int(board_size * board_size),
                image_channel=3,
                num_res_blocks=1,
                num_channels=32,
                support_scale=10,
                reward_support_size=21,
                value_support_size=21,
            ),
            cuda=True,
            env_type='board_games',
            action_type='varied_action_space',
            game_segment_length=int(board_size * board_size / 2),  # for battle_mode='play_with_bot_mode'
            update_per_collect=update_per_collect,
            batch_size=batch_size,
            optim_type='Adam',
            piecewise_decay_lr_scheduler=False,
            learning_rate=0.003,
            grad_clip_value=0.5,
            num_simulations=num_simulations,
            reanalyze_ratio=reanalyze_ratio,
            # NOTE：In board_games, we set large td_steps to make sure the value target is the final outcome.
            td_steps=int(board_size * board_size / 2),  # for battle_mode='play_with_bot_mode'
            # NOTE：In board_games, we set discount_factor=1.
            discount_factor=1,
            n_episode=n_episode,
            eval_freq=int(2e3),
            replay_buffer_size=int(1e5),
            collector_env_num=collector_env_num,
            evaluator_env_num=evaluator_env_num,
        ),
        wandb_logger=dict(
            gradient_logger=False, video_logger=False, plot_logger=False, action_logger=False, return_logger=False
        ),
    ),
    create_config = dict(
        env=dict(
            type='gomoku',
            import_names=['zoo.board_games.gomoku.envs.gomoku_env'],
        ),
        env_manager=dict(type='subprocess'),
        policy=dict(
            type='muzero',
            import_names=['lzero.policy.muzero'],
        ),
    )
)

cfg = EasyDict(cfg)
