from easydict import EasyDict pong_acer_config = dict( 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, priority=False, model=dict( obs_shape=[4, 84, 84], action_shape=6, encoder_hidden_size_list=[128, 128, 512], critic_head_hidden_size=512, critic_head_layer_num=2, actor_head_hidden_size=512, actor_head_layer_num=2, ), unroll_len=64, learn=dict( # (int) collect n_sample data, train model update_per_collect times # here we follow impala serial pipeline update_per_collect=10, # (int) the number of data for a train iteration batch_size=64, # grad_clip_type='clip_norm', # clip_value=10, learning_rate_actor=0.0001, learning_rate_critic=0.0003, # (float) loss weight of the entropy regularization, the weight of policy network is set to 1 entropy_weight=0.01, # (float) discount factor for future reward, defaults int [0, 1] discount_factor=0.9, # (float) additional discounting parameter trust_region=True, # (float) clip ratio of importance weights c_clip_ratio=10, ), collect=dict( # (int) collect n_sample data, train model n_iteration times n_sample=64, # (float) discount factor for future reward, defaults int [0, 1] discount_factor=0.9, collector=dict(collect_print_freq=1000, ), ), eval=dict(evaluator=dict(eval_freq=5000, )), other=dict(replay_buffer=dict(replay_buffer_size=3000, ), ), ), ) main_config = EasyDict(pong_acer_config) pong_acer_create_config = dict( env=dict( type='atari', import_names=['dizoo.atari.envs.atari_env'], ), env_manager=dict(type='subprocess'), policy=dict(type='acer'), ) create_config = EasyDict(pong_acer_create_config) if __name__ == '__main__': # or you can enter `ding -m serial -c pong_acer_config.py -s 0` from ding.entry import serial_pipeline serial_pipeline((main_config, create_config), seed=0)