from easydict import EasyDict qbert_a2c_config = dict( exp_name='qbert_a2c_seed0', env=dict( collector_env_num=16, evaluator_env_num=8, n_evaluator_episode=8, stop_value=1000000, env_id='QbertNoFrameskip-v4', #'ALE/Qbert-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=[32, 64, 64, 256], actor_head_hidden_size=256, critic_head_hidden_size=256, critic_head_layer_num=2, ), learn=dict( batch_size=300, # (bool) Whether to normalize advantage. Default to False. adv_norm=False, learning_rate=0.0001414, # (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=0.01, grad_norm=0.5, betas=(0.0, 0.99), ), collect=dict( # (int) collect n_sample data, train model 1 times n_sample=160, # (float) the trade-off factor lambda to balance 1step td and mc gae_lambda=0.99, discount_factor=0.99, ), eval=dict(evaluator=dict(eval_freq=500, )), ), ) main_config = EasyDict(qbert_a2c_config) qbert_a2c_create_config = dict( env=dict( type='atari', import_names=['dizoo.atari.envs.atari_env'], ), env_manager=dict(type='subprocess'), policy=dict(type='a2c'), replay_buffer=dict(type='naive'), ) create_config = EasyDict(qbert_a2c_create_config) if __name__ == '__main__': # or you can enter ding -m serial_onpolicy -c qbert_a2c_config.py -s 0 from ding.entry import serial_pipeline_onpolicy serial_pipeline_onpolicy((main_config, create_config), seed=0)