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| from easydict import EasyDict | |
| obs_shape = 111 | |
| act_shape = 8 | |
| ant_sac_gail_config = dict( | |
| exp_name='ant_sac_gail_seed0', | |
| env=dict( | |
| env_id='Ant-v3', | |
| norm_obs=dict(use_norm=False, ), | |
| norm_reward=dict(use_norm=False, ), | |
| collector_env_num=1, | |
| evaluator_env_num=8, | |
| n_evaluator_episode=8, | |
| stop_value=6000, | |
| ), | |
| reward_model=dict( | |
| input_size=obs_shape + act_shape, | |
| hidden_size=256, | |
| batch_size=64, | |
| learning_rate=1e-3, | |
| update_per_collect=100, | |
| # Users should add their own model path here. Model path should lead to a model. | |
| # Absolute path is recommended. | |
| # In DI-engine, it is ``exp_name/ckpt/ckpt_best.pth.tar``. | |
| expert_model_path='model_path_placeholder', | |
| # Path where to store the reward model | |
| reward_model_path='data_path_placeholder+/reward_model/ckpt/ckpt_best.pth.tar', | |
| # Users should add their own data path here. Data path should lead to a file to store data or load the stored data. | |
| # Absolute path is recommended. | |
| # In DI-engine, it is usually located in ``exp_name`` directory | |
| data_path='data_path_placeholder', | |
| collect_count=300000, | |
| ), | |
| policy=dict( | |
| cuda=True, | |
| random_collect_size=25000, | |
| model=dict( | |
| obs_shape=obs_shape, | |
| action_shape=act_shape, | |
| twin_critic=True, | |
| action_space='reparameterization', | |
| actor_head_hidden_size=256, | |
| critic_head_hidden_size=256, | |
| ), | |
| learn=dict( | |
| update_per_collect=1, | |
| batch_size=256, | |
| learning_rate_q=1e-3, | |
| learning_rate_policy=1e-3, | |
| learning_rate_alpha=3e-4, | |
| ignore_done=False, | |
| target_theta=0.005, | |
| discount_factor=0.99, | |
| alpha=0.2, | |
| reparameterization=True, | |
| auto_alpha=False, | |
| ), | |
| collect=dict( | |
| n_sample=64, | |
| unroll_len=1, | |
| ), | |
| command=dict(), | |
| eval=dict(), | |
| other=dict(replay_buffer=dict(replay_buffer_size=1000000, ), ), | |
| ), | |
| ) | |
| ant_sac_gail_config = EasyDict(ant_sac_gail_config) | |
| main_config = ant_sac_gail_config | |
| ant_sac_gail_create_config = dict( | |
| env=dict( | |
| type='mujoco', | |
| import_names=['dizoo.mujoco.envs.mujoco_env'], | |
| ), | |
| env_manager=dict(type='subprocess'), | |
| policy=dict( | |
| type='sac', | |
| import_names=['ding.policy.sac'], | |
| ), | |
| replay_buffer=dict(type='naive', ), | |
| reward_model=dict(type='gail'), | |
| ) | |
| ant_sac_gail_create_config = EasyDict(ant_sac_gail_create_config) | |
| create_config = ant_sac_gail_create_config | |
| if __name__ == "__main__": | |
| # or you can enter `ding -m serial_gail -c ant_gail_sac_config.py -s 0` | |
| # then input the config you used to generate your expert model in the path mentioned above | |
| # e.g. hopper_sac_config.py | |
| from ding.entry import serial_pipeline_gail | |
| from dizoo.mujoco.config.ant_sac_config import ant_sac_config, ant_sac_create_config | |
| expert_main_config = ant_sac_config | |
| expert_create_config = ant_sac_create_config | |
| serial_pipeline_gail( | |
| [main_config, create_config], [expert_main_config, expert_create_config], | |
| max_env_step=10000000, | |
| seed=0, | |
| collect_data=True | |
| ) | |