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| from easydict import EasyDict | |
| # options={'PongNoFrameskip-v4', 'QbertNoFrameskip-v4', 'MsPacmanNoFrameskip-v4', 'SpaceInvadersNoFrameskip-v4', 'BreakoutNoFrameskip-v4', ...} | |
| env_name = 'PongNoFrameskip-v4' | |
| if env_name == 'PongNoFrameskip-v4': | |
| action_space_size = 6 | |
| elif env_name == 'QbertNoFrameskip-v4': | |
| action_space_size = 6 | |
| elif env_name == 'MsPacmanNoFrameskip-v4': | |
| action_space_size = 9 | |
| elif env_name == 'SpaceInvadersNoFrameskip-v4': | |
| action_space_size = 6 | |
| elif env_name == 'BreakoutNoFrameskip-v4': | |
| action_space_size = 4 | |
| # ============================================================== | |
| # begin of the most frequently changed config specified by the user | |
| # ============================================================== | |
| # collector_env_num = 8 | |
| # n_episode = 8 | |
| # evaluator_env_num = 3 | |
| # num_simulations = 50 | |
| # update_per_collect = 1000 | |
| # batch_size = 256 | |
| # max_env_step = int(1e6) | |
| # reanalyze_ratio = 0. | |
| # chance_space_size = 4 | |
| # debug config | |
| collector_env_num = 1 | |
| n_episode = 1 | |
| evaluator_env_num = 1 | |
| num_simulations = 5 | |
| update_per_collect = 10 | |
| batch_size = 2 | |
| max_env_step = int(1e6) | |
| reanalyze_ratio = 0. | |
| chance_space_size = 4 | |
| # ============================================================== | |
| # end of the most frequently changed config specified by the user | |
| # ============================================================== | |
| atari_stochastic_muzero_config = dict( | |
| exp_name= | |
| f'data_stochastic_mz_ctree/{env_name[:-14]}_stochastic_muzero_ns{num_simulations}_upc{update_per_collect}_rr{reanalyze_ratio}_chance{chance_space_size}_seed0', | |
| env=dict( | |
| stop_value=int(1e6), | |
| env_name=env_name, | |
| obs_shape=(4, 96, 96), | |
| 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=(4, 96, 96), | |
| frame_stack_num=4, | |
| action_space_size=action_space_size, | |
| chance_space_size=chance_space_size, | |
| downsample=True, | |
| self_supervised_learning_loss=True, # default is False | |
| discrete_action_encoding_type='one_hot', | |
| norm_type='BN', | |
| ), | |
| cuda=True, | |
| gumbel_algo=False, | |
| mcts_ctree=True, | |
| env_type='not_board_games', | |
| game_segment_length=400, | |
| use_augmentation=True, | |
| update_per_collect=update_per_collect, | |
| batch_size=batch_size, | |
| optim_type='Adam', | |
| lr_piecewise_constant_decay=False, | |
| learning_rate=3e-3, | |
| 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_stochastic_muzero_config = EasyDict(atari_stochastic_muzero_config) | |
| main_config = atari_stochastic_muzero_config | |
| atari_stochastic_muzero_create_config = dict( | |
| env=dict( | |
| type='atari_lightzero', | |
| import_names=['zoo.atari.envs.atari_lightzero_env'], | |
| ), | |
| env_manager=dict(type='subprocess'), | |
| policy=dict( | |
| type='stochastic_muzero', | |
| import_names=['lzero.policy.stochastic_muzero'], | |
| ), | |
| ) | |
| atari_stochastic_muzero_create_config = EasyDict(atari_stochastic_muzero_create_config) | |
| create_config = atari_stochastic_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) | |