from easydict import EasyDict qbert_sqil_config = dict( exp_name='qbert_sqil_seed0', env=dict( collector_env_num=8, evaluator_env_num=8, n_evaluator_episode=8, stop_value=30000, env_id='QbertNoFrameskip-v4', #'ALE/Qbert-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=100, # 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' ), eval=dict(evaluator=dict(eval_freq=4000, )), other=dict( eps=dict( type='exp', start=1., end=0.05, decay=1000000, ), replay_buffer=dict(replay_buffer_size=400000, ), ), ), ) qbert_sqil_config = EasyDict(qbert_sqil_config) main_config = qbert_sqil_config qbert_sqil_create_config = dict( env=dict( type='atari', import_names=['dizoo.atari.envs.atari_env'], ), env_manager=dict(type='subprocess'), policy=dict(type='dqn'), ) qbert_sqil_create_config = EasyDict(qbert_sqil_create_config) create_config = qbert_sqil_create_config if __name__ == '__main__': # or you can enter `ding -m serial_sqil -c qbert_sqil_config.py -s 0` # then input the config you used to generate your expert model in the path mentioned above # e.g. qbert_dqn_config.py from ding.entry import serial_pipeline_sqil from dizoo.atari.config.serial.qbert import qbert_dqn_config, qbert_dqn_create_config expert_main_config = qbert_dqn_config expert_create_config = qbert_dqn_create_config serial_pipeline_sqil([main_config, create_config], [expert_main_config, expert_create_config], seed=0)