from easydict import EasyDict pong_ppg_config = dict( exp_name='pong_ppg_seed0', env=dict( collector_env_num=8, evaluator_env_num=8, n_evaluator_episode=8, stop_value=20, env_id='PongNoFrameskip-v4', #'ALE/Pong-v5' is available. But special setting is needed after gym make. frame_stack=4, ), policy=dict( cuda=True, model=dict( obs_shape=[4, 84, 84], action_shape=6, encoder_hidden_size_list=[64, 64, 128], critic_head_hidden_size=128, actor_head_hidden_size=128, ), 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=5, ), ), ), ), ) main_config = EasyDict(pong_ppg_config) pong_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'), ) create_config = EasyDict(pong_ppg_create_config) if __name__ == "__main__": import os import warnings from dizoo.atari.entry.atari_ppg_main import main from dizoo.atari.entry.atari_ppg_main import __file__ as _origin_py_file origin_py_file_rel = os.path.relpath(_origin_py_file, os.path.abspath(os.path.curdir)) warnings.warn(UserWarning(f"This config file can be executed by {repr(origin_py_file_rel)}")) main(main_config)