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from easydict import EasyDict |
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ant_ddpg_default_config = dict( |
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exp_name='multi_mujoco_ant_2x4_ddpg', |
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env=dict( |
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scenario='Ant-v2', |
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agent_conf="2x4d", |
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agent_obsk=2, |
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add_agent_id=False, |
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episode_limit=1000, |
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collector_env_num=8, |
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evaluator_env_num=8, |
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n_evaluator_episode=8, |
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stop_value=6000, |
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), |
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policy=dict( |
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cuda=True, |
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random_collect_size=0, |
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multi_agent=True, |
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model=dict( |
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agent_obs_shape=54, |
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global_obs_shape=111, |
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action_shape=4, |
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action_space='regression', |
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actor_head_hidden_size=256, |
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critic_head_hidden_size=256, |
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), |
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learn=dict( |
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update_per_collect=10, |
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batch_size=256, |
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learning_rate_actor=1e-3, |
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learning_rate_critic=1e-3, |
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target_theta=0.005, |
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discount_factor=0.99, |
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), |
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collect=dict( |
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n_sample=400, |
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noise_sigma=0.1, |
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), |
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eval=dict(evaluator=dict(eval_freq=500, )), |
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other=dict(replay_buffer=dict(replay_buffer_size=100000, ), ), |
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), |
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) |
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ant_ddpg_default_config = EasyDict(ant_ddpg_default_config) |
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main_config = ant_ddpg_default_config |
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ant_ddpg_default_create_config = dict( |
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env=dict( |
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type='mujoco_multi', |
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import_names=['dizoo.multiagent_mujoco.envs.multi_mujoco_env'], |
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), |
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env_manager=dict(type='subprocess'), |
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policy=dict(type='ddpg'), |
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replay_buffer=dict(type='naive', ), |
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) |
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ant_ddpg_default_create_config = EasyDict(ant_ddpg_default_create_config) |
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create_config = ant_ddpg_default_create_config |
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if __name__ == '__main__': |
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from ding.entry.serial_entry import serial_pipeline |
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serial_pipeline((main_config, create_config), seed=0, max_env_step=int(1e7)) |
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