from easydict import EasyDict collector_env_num = 1 evaluator_env_num = 1 walker2d_onppo_config = dict( exp_name='walker2d_onppo_seed0', env=dict( env_id='Walker2d-v3', norm_obs=dict(use_norm=False, ), norm_reward=dict(use_norm=False, ), collector_env_num=collector_env_num, evaluator_env_num=evaluator_env_num, n_evaluator_episode=10, stop_value=6000, ), policy=dict( cuda=True, recompute_adv=True, action_space='continuous', model=dict( action_space='continuous', obs_shape=17, action_shape=6, ), 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, # for onppo, when we recompute adv, we need the key done in data to split traj, 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 # for halfcheetah, the length=1000 # ignore_done=True, ignore_done=False, grad_clip_type='clip_norm', grad_clip_value=0.5, ), collect=dict( collector_env_num=collector_env_num, n_sample=3200, unroll_len=1, discount_factor=0.99, gae_lambda=0.95, ), eval=dict(evaluator=dict(eval_freq=500, )), ), ) walker2d_onppo_config = EasyDict(walker2d_onppo_config) main_config = walker2d_onppo_config walker2d_onppo_create_config = dict( env=dict( type='mujoco', import_names=['dizoo.mujoco.envs.mujoco_env'], ), env_manager=dict(type='base'), # env_manager=dict(type='subprocess'), policy=dict(type='ppo', ), ) walker2d_onppo_create_config = EasyDict(walker2d_onppo_create_config) create_config = walker2d_onppo_create_config if __name__ == "__main__": # or you can enter `ding -m serial_onpolicy -c walker2d_onppo_config.py -s 0` from ding.entry import serial_pipeline_onpolicy serial_pipeline_onpolicy([main_config, create_config], seed=0)