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from easydict import EasyDict |
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env_name = 'game_2048' |
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action_space_size = 4 |
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use_ture_chance_label_in_chance_encoder = True |
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collector_env_num = 8 |
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n_episode = 8 |
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evaluator_env_num = 3 |
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num_simulations = 100 |
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update_per_collect = 200 |
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batch_size = 512 |
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max_env_step = int(1e9) |
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reanalyze_ratio = 0. |
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num_of_possible_chance_tile = 2 |
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chance_space_size = 16 * num_of_possible_chance_tile |
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game_2048_stochastic_muzero_config = dict( |
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exp_name=f'data_stochastic_mz_ctree/game_2048_npct-{num_of_possible_chance_tile}_stochastic_muzero_ns{num_simulations}_upc{update_per_collect}_rr{reanalyze_ratio}_bs{batch_size}_chance-{use_ture_chance_label_in_chance_encoder}_sslw2_seed0', |
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env=dict( |
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stop_value=int(1e6), |
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env_name=env_name, |
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obs_shape=(16, 4, 4), |
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obs_type='dict_encoded_board', |
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num_of_possible_chance_tile=num_of_possible_chance_tile, |
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collector_env_num=collector_env_num, |
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evaluator_env_num=evaluator_env_num, |
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n_evaluator_episode=evaluator_env_num, |
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manager=dict(shared_memory=False, ), |
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), |
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policy=dict( |
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model=dict( |
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observation_shape=(16, 4, 4), |
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action_space_size=action_space_size, |
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chance_space_size=chance_space_size, |
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image_channel=16, |
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self_supervised_learning_loss=True, |
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discrete_action_encoding_type='one_hot', |
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norm_type='BN', |
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), |
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use_ture_chance_label_in_chance_encoder=use_ture_chance_label_in_chance_encoder, |
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mcts_ctree=True, |
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cuda=True, |
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game_segment_length=200, |
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update_per_collect=update_per_collect, |
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batch_size=batch_size, |
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td_steps=10, |
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discount_factor=0.999, |
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manual_temperature_decay=True, |
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optim_type='Adam', |
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lr_piecewise_constant_decay=False, |
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learning_rate=0.003, |
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weight_decay=1e-4, |
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num_simulations=num_simulations, |
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reanalyze_ratio=reanalyze_ratio, |
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ssl_loss_weight=2, |
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n_episode=n_episode, |
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eval_freq=int(2e3), |
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replay_buffer_size=int(1e6), |
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collector_env_num=collector_env_num, |
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evaluator_env_num=evaluator_env_num, |
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), |
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) |
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game_2048_stochastic_muzero_config = EasyDict(game_2048_stochastic_muzero_config) |
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main_config = game_2048_stochastic_muzero_config |
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game_2048_stochastic_muzero_create_config = dict( |
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env=dict( |
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type='game_2048', |
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import_names=['zoo.game_2048.envs.game_2048_env'], |
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), |
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env_manager=dict(type='subprocess'), |
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policy=dict( |
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type='stochastic_muzero', |
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import_names=['lzero.policy.stochastic_muzero'], |
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), |
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) |
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game_2048_stochastic_muzero_create_config = EasyDict(game_2048_stochastic_muzero_create_config) |
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create_config = game_2048_stochastic_muzero_create_config |
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if __name__ == "__main__": |
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from lzero.entry import train_muzero |
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train_muzero([main_config, create_config], seed=0, max_env_step=max_env_step) |
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