from easydict import EasyDict ant_ppo_config = dict( exp_name="ant_onppo_seed0", env=dict( env_id='Ant-v3', norm_obs=dict(use_norm=False, ), norm_reward=dict(use_norm=False, ), collector_env_num=10, evaluator_env_num=10, n_evaluator_episode=10, stop_value=6000, manager=dict(shared_memory=False, ) ), policy=dict( cuda=True, recompute_adv=True, action_space='continuous', model=dict( action_space='continuous', obs_shape=111, action_shape=8, ), learn=dict( epoch_per_collect=10, update_per_collect=1, batch_size=320, learning_rate=3e-4, value_weight=0.5, entropy_weight=0.001, clip_ratio=0.2, adv_norm=True, value_norm=True, # When we recompute advantage, we need the key done in data to split trajectories, so we must # use 'ignore_done=False' here, but when we add key 'traj_flag' in data as the backup for key done, # we could choose to use 'ignore_done=True'. 'traj_flag' indicates termination of trajectory. ignore_done=False, grad_clip_type='clip_norm', grad_clip_value=0.5, ), collect=dict( n_sample=3200, unroll_len=1, discount_factor=0.99, gae_lambda=0.95, ), eval=dict(evaluator=dict(eval_freq=5000, )), ), ) ant_ppo_config = EasyDict(ant_ppo_config) main_config = ant_ppo_config ant_ppo_create_config = dict( env=dict( type='mujoco', import_names=['dizoo.mujoco.envs.mujoco_env'], ), env_manager=dict(type='subprocess'), policy=dict(type='ppo'), ) ant_ppo_create_config = EasyDict(ant_ppo_create_config) create_config = ant_ppo_create_config if __name__ == "__main__": # or you can enter `ding -m serial_onpolicy -c ant_onppo_config.py -s 0 --env-step 1e7` from ding.entry import serial_pipeline_onpolicy serial_pipeline_onpolicy((main_config, create_config), seed=0)