from easydict import EasyDict pong_cql_config = dict( exp_name='pong_cql_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=False, model=dict( obs_shape=[4, 84, 84], action_shape=6, encoder_hidden_size_list=[128, 128, 512], num_quantiles=200, ), nstep=1, discount_factor=0.99, learn=dict( train_epoch=30000, batch_size=32, learning_rate=0.00005, target_update_freq=2000, min_q_weight=10.0, ), collect=dict( n_sample=100, data_type='hdf5', # Users should add their own data path here. Data path should lead to a file to store data or load the stored data. # Absolute path is recommended. # In DI-engine, it is usually located in ``exp_name`` directory data_path='./default_experiment/expert.pkl', ), eval=dict(evaluator=dict(eval_freq=4000, )), other=dict( eps=dict( type='exp', start=1., end=0.05, decay=250000, ), replay_buffer=dict(replay_buffer_size=100000, ), ), ), ) pong_cql_config = EasyDict(pong_cql_config) main_config = pong_cql_config pong_cql_create_config = dict( env=dict( type='atari', import_names=['dizoo.atari.envs.atari_env'], ), env_manager=dict(type='subprocess'), policy=dict(type='cql_discrete'), ) pong_cql_create_config = EasyDict(pong_cql_create_config) create_config = pong_cql_create_config if __name__ == '__main__': # or you can enter `ding -m serial_offline -c pong_cql_config.py -s 0` from ding.entry import serial_pipeline_offline serial_pipeline_offline((main_config, create_config), seed=0)