Spaces:
Running
Running
| from easydict import EasyDict | |
| ant_ppo_config = dict( | |
| exp_name='ant_ppo_seed0', | |
| env=dict( | |
| manager=dict(shared_memory=False, reset_inplace=True), | |
| env_id='Ant-v3', | |
| norm_obs=dict(use_norm=False, ), | |
| norm_reward=dict(use_norm=False, ), | |
| collector_env_num=8, | |
| evaluator_env_num=10, | |
| n_evaluator_episode=10, | |
| stop_value=6000, | |
| ), | |
| policy=dict( | |
| cuda=True, | |
| recompute_adv=True, | |
| model=dict( | |
| obs_shape=111, | |
| action_shape=8, | |
| action_space='continuous', | |
| ), | |
| action_space='continuous', | |
| learn=dict( | |
| epoch_per_collect=10, | |
| batch_size=64, | |
| learning_rate=3e-4, | |
| value_weight=0.5, | |
| entropy_weight=0.0, | |
| clip_ratio=0.2, | |
| adv_norm=True, | |
| value_norm=True, | |
| ), | |
| collect=dict( | |
| n_sample=2048, | |
| unroll_len=1, | |
| discount_factor=0.99, | |
| gae_lambda=0.97, | |
| ), | |
| 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_ppo_config.py -s 0 --env-step 1e7` | |
| from ding.entry import serial_pipeline_onpolicy | |
| serial_pipeline_onpolicy((main_config, create_config), seed=0) | |