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| from easydict import EasyDict | |
| ant_sac_config = dict( | |
| exp_name='ant_sac_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, | |
| manager=dict(shared_memory=False, reset_inplace=True), | |
| ), | |
| policy=dict( | |
| cuda=True, | |
| random_collect_size=10000, | |
| model=dict( | |
| obs_shape=111, | |
| action_shape=8, | |
| 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=1, | |
| unroll_len=1, | |
| ), | |
| command=dict(), | |
| eval=dict(), | |
| other=dict(replay_buffer=dict(replay_buffer_size=1000000, ), ), | |
| ), | |
| ) | |
| ant_sac_config = EasyDict(ant_sac_config) | |
| main_config = ant_sac_config | |
| ant_sac_create_config = dict( | |
| env=dict( | |
| type='mujoco', | |
| import_names=['dizoo.mujoco.envs.mujoco_env'], | |
| ), | |
| env_manager=dict(type='base'), | |
| policy=dict( | |
| type='sac', | |
| import_names=['ding.policy.sac'], | |
| ), | |
| replay_buffer=dict(type='naive', ), | |
| ) | |
| ant_sac_create_config = EasyDict(ant_sac_create_config) | |
| create_config = ant_sac_create_config | |
| if __name__ == "__main__": | |
| # or you can enter `ding -m serial -c ant_sac_config.py -s 0 --env-step 1e7` | |
| from ding.entry import serial_pipeline | |
| serial_pipeline((main_config, create_config), seed=0) | |