from easydict import EasyDict pong_sqil_config = dict( exp_name='pong_sqil_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, priority=True, model=dict( obs_shape=[4, 84, 84], action_shape=6, encoder_hidden_size_list=[128, 128, 512], ), nstep=3, discount_factor=0.97, # discount_factor: 0.97-0.99 learn=dict(update_per_collect=10, batch_size=32, learning_rate=0.0001, target_update_freq=500, alpha=0.1), # alpha: 0.08-0.12 collect=dict( n_sample=96, # Users should add their own model path here. Model path should lead to a model. # Absolute path is recommended. # In DI-engine, it is ``exp_name/ckpt/ckpt_best.pth.tar``. model_path='model_path_placeholder', ), other=dict( eps=dict( type='exp', start=1., end=0.05, decay=250000, ), replay_buffer=dict(replay_buffer_size=100000, ), ), ), ) pong_sqil_config = EasyDict(pong_sqil_config) main_config = pong_sqil_config pong_sqil_create_config = dict( env=dict( type='atari', import_names=['dizoo.atari.envs.atari_env'], ), env_manager=dict(type='subprocess'), policy=dict(type='sql'), ) pong_sqil_create_config = EasyDict(pong_sqil_create_config) create_config = pong_sqil_create_config if __name__ == '__main__': # or you can enter `ding -m serial_sqil -c pong_sqil_config.py -s 0` # then input the config you used to generate your expert model in the path mentioned above # e.g. pong_dqn_config.py from ding.entry import serial_pipeline_sqil from dizoo.atari.config.serial.pong import pong_dqn_config, pong_dqn_create_config expert_main_config = pong_dqn_config expert_create_config = pong_dqn_create_config serial_pipeline_sqil((main_config, create_config), (expert_main_config, expert_create_config), seed=0)