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| from easydict import EasyDict | |
| # ============================================================== | |
| # begin of the most frequently changed config specified by the user | |
| # ============================================================== | |
| env_name = 'game_2048' | |
| action_space_size = 4 | |
| collector_env_num = 8 | |
| n_episode = 8 | |
| evaluator_env_num = 3 | |
| num_simulations = 100 | |
| update_per_collect = 200 | |
| batch_size = 512 | |
| max_env_step = int(5e6) | |
| reanalyze_ratio = 0. | |
| num_of_possible_chance_tile = 2 | |
| chance_space_size = 16 * num_of_possible_chance_tile | |
| # ============================================================== | |
| # end of the most frequently changed config specified by the user | |
| # ============================================================== | |
| atari_muzero_config = dict( | |
| exp_name=f'data_mz_ctree/game_2048_npct-{num_of_possible_chance_tile}_muzero_ns{num_simulations}_upc{update_per_collect}_rr{reanalyze_ratio}_bs{batch_size}_sslw2_seed0', | |
| env=dict( | |
| stop_value=int(1e6), | |
| env_name=env_name, | |
| obs_shape=(16, 4, 4), | |
| obs_type='dict_encoded_board', | |
| raw_reward_type='raw', # 'merged_tiles_plus_log_max_tile_num' | |
| reward_normalize=False, | |
| reward_norm_scale=100, | |
| max_tile=int(2 ** 16), # 2**11=2048, 2**16=65536 | |
| num_of_possible_chance_tile=num_of_possible_chance_tile, | |
| 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=(16, 4, 4), | |
| action_space_size=action_space_size, | |
| image_channel=16, | |
| # NOTE: whether to use the self_supervised_learning_loss. default is False | |
| self_supervised_learning_loss=True, | |
| ), | |
| mcts_ctree=True, | |
| gumbel_algo=False, | |
| cuda=True, | |
| game_segment_length=200, | |
| update_per_collect=update_per_collect, | |
| batch_size=batch_size, | |
| td_steps=10, | |
| discount_factor=0.999, | |
| manual_temperature_decay=True, | |
| threshold_training_steps_for_final_temperature=int(1e5), | |
| optim_type='Adam', | |
| lr_piecewise_constant_decay=False, | |
| learning_rate=3e-3, | |
| # (float) Weight decay for training policy network. | |
| weight_decay=1e-4, | |
| num_simulations=num_simulations, | |
| reanalyze_ratio=reanalyze_ratio, | |
| ssl_loss_weight=2, # default is 0 | |
| n_episode=n_episode, | |
| eval_freq=int(2e3), | |
| replay_buffer_size=int(1e6), # the size/capacity of replay_buffer, in the terms of transitions. | |
| collector_env_num=collector_env_num, | |
| evaluator_env_num=evaluator_env_num, | |
| ), | |
| ) | |
| atari_muzero_config = EasyDict(atari_muzero_config) | |
| main_config = atari_muzero_config | |
| atari_muzero_create_config = dict( | |
| env=dict( | |
| type='game_2048', | |
| import_names=['zoo.game_2048.envs.game_2048_env'], | |
| ), | |
| env_manager=dict(type='subprocess'), | |
| policy=dict( | |
| type='muzero', | |
| import_names=['lzero.policy.muzero'], | |
| ), | |
| ) | |
| atari_muzero_create_config = EasyDict(atari_muzero_create_config) | |
| create_config = atari_muzero_create_config | |
| if __name__ == "__main__": | |
| from lzero.entry import train_muzero | |
| train_muzero([main_config, create_config], seed=0, max_env_step=max_env_step) | |