from copy import deepcopy from easydict import EasyDict spaceinvaders_ppg_config = dict( exp_name='spaceinvaders_ppg_seed0', env=dict( collector_env_num=16, evaluator_env_num=8, n_evaluator_episode=8, stop_value=10000000000, env_id='SpaceInvadersNoFrameskip-v4', #'ALE/SpaceInvaders-v5' is available. But special setting is needed after gym make. frame_stack=4, manager=dict(shared_memory=False, ) ), policy=dict( cuda=True, model=dict( obs_shape=[4, 84, 84], action_shape=6, encoder_hidden_size_list=[32, 64, 64, 128], actor_head_hidden_size=128, critic_head_hidden_size=128, critic_head_layer_num=2, ), learn=dict( update_per_collect=24, batch_size=128, # (bool) Whether to normalize advantage. Default to False. adv_norm=False, learning_rate=0.0001, # (float) loss weight of the value network, the weight of policy network is set to 1 value_weight=0.5, # (float) loss weight of the entropy regularization, the weight of policy network is set to 1 entropy_weight=0.03, clip_ratio=0.1, epochs_aux=6, beta_weight=1, aux_freq=100 ), collect=dict( # (int) collect n_sample data, train model n_iteration times n_sample=1024, # (float) the trade-off factor lambda to balance 1step td and mc gae_lambda=0.95, discount_factor=0.99, ), eval=dict(evaluator=dict(eval_freq=1000, )), other=dict( replay_buffer=dict( multi_buffer=True, policy=dict( replay_buffer_size=100000, max_use=3, ), value=dict( replay_buffer_size=100000, max_use=10, ), ), ), ), ) spaceinvaders_ppg_config = EasyDict(spaceinvaders_ppg_config) main_config = spaceinvaders_ppg_config spaceinvaders_ppg_create_config = dict( env=dict( type='atari', import_names=['dizoo.atari.envs.atari_env'], ), env_manager=dict(type='subprocess'), policy=dict(type='ppg_offpolicy'), ) spaceinvaders_ppg_create_config = EasyDict(spaceinvaders_ppg_create_config) create_config = EasyDict(spaceinvaders_ppg_create_config) if __name__ == '__main__': from dizoo.atari.entry.atari_ppg_main import main # PPG needs to use specific entry, you can run `dizoo/atari/entry/atari_ppg_main.py` main(main_config)