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| from easydict import EasyDict | |
| halfcheetah_sqil_config = dict( | |
| exp_name='halfcheetah_sqil_sac_seed0', | |
| env=dict( | |
| env_id='HalfCheetah-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=12000, | |
| ), | |
| policy=dict( | |
| cuda=True, | |
| random_collect_size=10000, | |
| expert_random_collect_size=10000, | |
| model=dict( | |
| obs_shape=17, | |
| action_shape=6, | |
| twin_critic=True, | |
| action_space='reparameterization', | |
| actor_head_hidden_size=256, | |
| critic_head_hidden_size=256, | |
| ), | |
| nstep=1, | |
| discount_factor=0.97, | |
| learn=dict( | |
| update_per_collect=1, | |
| batch_size=256, | |
| learning_rate_q=1e-3, | |
| learning_rate_policy=1e-3, | |
| learning_rate_alpha=2e-4, | |
| ignore_done=True, | |
| target_theta=0.005, | |
| discount_factor=0.99, | |
| alpha=0.2, | |
| reparameterization=True, | |
| auto_alpha=True, | |
| ), | |
| collect=dict( | |
| n_sample=32, | |
| # Users should add their own path here (path should lead to a well-trained model) | |
| model_path='model_path_placeholder', | |
| # Cut trajectories into pieces with length "unroll_len". | |
| unroll_len=1, | |
| ), | |
| eval=dict(evaluator=dict(eval_freq=500, )), # note: this is the times after which you learns to evaluate | |
| other=dict(replay_buffer=dict(replay_buffer_size=1000000, ), ), | |
| ), | |
| ) | |
| halfcheetah_sqil_config = EasyDict(halfcheetah_sqil_config) | |
| main_config = halfcheetah_sqil_config | |
| halfcheetah_sqil_create_config = dict( | |
| env=dict( | |
| type='mujoco', | |
| import_names=['dizoo.mujoco.envs.mujoco_env'], | |
| ), | |
| env_manager=dict(type='subprocess'), | |
| policy=dict(type='sqil_sac'), | |
| replay_buffer=dict(type='naive', ), | |
| ) | |
| halfcheetah_sqil_create_config = EasyDict(halfcheetah_sqil_create_config) | |
| create_config = halfcheetah_sqil_create_config | |
| if __name__ == "__main__": | |
| # or you can enter `ding -m serial_sqil -c halfcheetah_sqil_sac_config.py -s 0` | |
| # then input the config you used to generate your expert model in the path mentioned above | |
| # e.g. halfcheetah_sac_config.py | |
| from halfcheetah_sac_config import halfcheetah_sac_config, halfcheetah_sac_create_config | |
| from ding.entry import serial_pipeline_sqil | |
| expert_main_config = halfcheetah_sac_config | |
| expert_create_config = halfcheetah_sac_create_config | |
| serial_pipeline_sqil( | |
| [main_config, create_config], [expert_main_config, expert_create_config], seed=0, max_env_step=5000000 | |
| ) | |