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| from easydict import EasyDict | |
| collector_env_num = 8 | |
| evaluator_env_num = 8 | |
| main_config = dict( | |
| exp_name='multi_mujoco_ant_2x4_ppo', | |
| env=dict( | |
| scenario='Ant-v2', | |
| agent_conf="2x4d", | |
| agent_obsk=2, | |
| add_agent_id=False, | |
| episode_limit=1000, | |
| collector_env_num=collector_env_num, | |
| evaluator_env_num=evaluator_env_num, | |
| n_evaluator_episode=8, | |
| stop_value=6000, | |
| ), | |
| policy=dict( | |
| cuda=True, | |
| multi_agent=True, | |
| action_space='continuous', | |
| model=dict( | |
| # (int) agent_num: The number of the agent. | |
| # For SMAC 3s5z, agent_num=8; for 2c_vs_64zg, agent_num=2. | |
| agent_num=2, | |
| # (int) obs_shape: The shapeension of observation of each agent. | |
| # For 3s5z, obs_shape=150; for 2c_vs_64zg, agent_num=404. | |
| # (int) global_obs_shape: The shapeension of global observation. | |
| # For 3s5z, obs_shape=216; for 2c_vs_64zg, agent_num=342. | |
| agent_obs_shape=54, | |
| #global_obs_shape=216, | |
| global_obs_shape=111, | |
| # (int) action_shape: The number of action which each agent can take. | |
| # action_shape= the number of common action (6) + the number of enemies. | |
| # For 3s5z, obs_shape=14 (6+8); for 2c_vs_64zg, agent_num=70 (6+64). | |
| action_shape=4, | |
| # (List[int]) The size of hidden layer | |
| # hidden_size_list=[64], | |
| action_space='continuous' | |
| ), | |
| # used in state_num of hidden_state | |
| learn=dict( | |
| epoch_per_collect=3, | |
| batch_size=800, | |
| learning_rate=5e-4, | |
| # ============================================================== | |
| # The following configs is algorithm-specific | |
| # ============================================================== | |
| # (float) The loss weight of value network, policy network weight is set to 1 | |
| value_weight=0.5, | |
| # (float) The loss weight of entropy regularization, policy network weight is set to 1 | |
| entropy_weight=0.001, | |
| # (float) PPO clip ratio, defaults to 0.2 | |
| clip_ratio=0.2, | |
| # (bool) Whether to use advantage norm in a whole training batch | |
| adv_norm=True, | |
| value_norm=True, | |
| ppo_param_init=True, | |
| grad_clip_type='clip_norm', | |
| grad_clip_value=5, | |
| ), | |
| collect=dict(env_num=collector_env_num, n_sample=3200), | |
| eval=dict(env_num=evaluator_env_num, evaluator=dict(eval_freq=1000, )), | |
| ), | |
| ) | |
| main_config = EasyDict(main_config) | |
| 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='ppo'), | |
| ) | |
| create_config = EasyDict(create_config) | |
| if __name__ == '__main__': | |
| from ding.entry import serial_pipeline_onpolicy | |
| serial_pipeline_onpolicy((main_config, create_config), seed=0, max_env_step=int(1e7)) | |