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| from easydict import EasyDict | |
| ant_sac_default_config = dict( | |
| exp_name='multi_mujoco_ant_2x4_sac', | |
| env=dict( | |
| scenario='Ant-v2', | |
| agent_conf="2x4d", | |
| agent_obsk=2, | |
| add_agent_id=False, | |
| episode_limit=1000, | |
| collector_env_num=8, | |
| evaluator_env_num=8, | |
| n_evaluator_episode=8, | |
| stop_value=6000, | |
| ), | |
| policy=dict( | |
| cuda=True, | |
| random_collect_size=0, | |
| multi_agent=True, | |
| model=dict( | |
| agent_obs_shape=54, | |
| global_obs_shape=111, | |
| action_shape=4, | |
| action_space='reparameterization', | |
| actor_head_hidden_size=256, | |
| critic_head_hidden_size=256, | |
| ), | |
| learn=dict( | |
| update_per_collect=10, | |
| batch_size=256, | |
| learning_rate_q=1e-3, | |
| learning_rate_policy=1e-3, | |
| learning_rate_alpha=3e-4, | |
| target_theta=0.005, | |
| discount_factor=0.99, | |
| ), | |
| collect=dict(n_sample=400, ), | |
| eval=dict(evaluator=dict(eval_freq=500, )), | |
| other=dict(replay_buffer=dict(replay_buffer_size=100000, ), ), | |
| ), | |
| ) | |
| ant_sac_default_config = EasyDict(ant_sac_default_config) | |
| main_config = ant_sac_default_config | |
| ant_sac_default_create_config = dict( | |
| env=dict( | |
| type='mujoco_multi', | |
| import_names=['dizoo.multiagent_mujoco.envs.multi_mujoco_env'], | |
| ), | |
| env_manager=dict(type='subprocess'), | |
| policy=dict(type='sac'), | |
| replay_buffer=dict(type='naive', ), | |
| ) | |
| ant_sac_default_create_config = EasyDict(ant_sac_default_create_config) | |
| create_config = ant_sac_default_create_config | |
| if __name__ == '__main__': | |
| # or you can enter `ding -m serial -c ant_masac_config.py -s 0` | |
| from ding.entry.serial_entry import serial_pipeline | |
| serial_pipeline((main_config, create_config), seed=0, max_env_step=int(1e7)) | |