from easydict import EasyDict walker2d_d4pg_config = dict( exp_name='walker2d_d4pg_seed0', env=dict( env_id='Walker2d-v3', norm_obs=dict(use_norm=False, ), norm_reward=dict(use_norm=False, ), collector_env_num=4, evaluator_env_num=4, n_evaluator_episode=8, stop_value=7000, ), policy=dict( cuda=True, priority=True, nstep=5, random_collect_size=10000, model=dict( obs_shape=17, action_shape=6, actor_head_hidden_size=512, critic_head_hidden_size=512, action_space='regression', critic_head_type='categorical', v_min=0, v_max=2000, # [1000, 4000] n_atom=51, ), learn=dict( update_per_collect=3, # [1, 4] batch_size=256, learning_rate_actor=3e-4, learning_rate_critic=3e-4, ignore_done=False, target_theta=0.005, discount_factor=0.99, actor_update_freq=1, noise=False, ), collect=dict( n_sample=8, unroll_len=1, noise_sigma=0.2, # [0.1, 0.2] ), other=dict(replay_buffer=dict(replay_buffer_size=1000000, ), ), ) ) walker2d_d4pg_config = EasyDict(walker2d_d4pg_config) main_config = walker2d_d4pg_config walker2d_d4pg_create_config = dict( env=dict( type='mujoco', import_names=['dizoo.mujoco.envs.mujoco_env'], ), env_manager=dict(type='subprocess'), policy=dict( type='d4pg', import_names=['ding.policy.d4pg'], ), ) walker2d_d4pg_create_config = EasyDict(walker2d_d4pg_create_config) create_config = walker2d_d4pg_create_config if __name__ == "__main__": # or you can enter `ding -m serial -c walker2d_d4pg_config.py -s 0` from ding.entry import serial_pipeline serial_pipeline([main_config, create_config], seed=0)