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from easydict import EasyDict |
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agent_num = 3 |
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collector_env_num = 8 |
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evaluator_env_num = 8 |
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special_global_state = True |
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SMAC_3m_masac_default_config = dict( |
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exp_name='smac_3m_masac_seed0', |
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env=dict( |
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map_name='3m', |
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difficulty=7, |
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reward_only_positive=True, |
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mirror_opponent=False, |
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agent_num=agent_num, |
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collector_env_num=collector_env_num, |
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evaluator_env_num=evaluator_env_num, |
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n_evaluator_episode=16, |
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stop_value=0.99, |
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death_mask=True, |
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special_global_state=special_global_state, |
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manager=dict( |
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shared_memory=False, |
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reset_timeout=6000, |
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), |
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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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model=dict( |
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agent_obs_shape=42, |
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global_obs_shape=77, |
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action_shape=9, |
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twin_critic=True, |
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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=50, |
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batch_size=320, |
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learning_rate_q=5e-4, |
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learning_rate_policy=5e-4, |
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learning_rate_alpha=5e-4, |
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ignore_done=False, |
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target_theta=0.005, |
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discount_factor=0.99, |
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alpha=0.2, |
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auto_alpha=True, |
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log_space=True, |
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), |
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collect=dict( |
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env_num=collector_env_num, |
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n_sample=3200, |
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unroll_len=1, |
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), |
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eval=dict( |
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evaluator=dict(eval_freq=50, ), |
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env_num=evaluator_env_num, |
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), |
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other=dict( |
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eps=dict( |
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type='linear', |
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start=1, |
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end=0.05, |
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decay=100000, |
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), |
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replay_buffer=dict(replay_buffer_size=1000000, ), |
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), |
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), |
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) |
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SMAC_3m_masac_default_config = EasyDict(SMAC_3m_masac_default_config) |
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main_config = SMAC_3m_masac_default_config |
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SMAC_3m_masac_default_create_config = dict( |
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env=dict( |
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type='smac', |
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import_names=['dizoo.smac.envs.smac_env'], |
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), |
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env_manager=dict(type='base'), |
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policy=dict(type='sac_discrete', ), |
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) |
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SMAC_3m_masac_default_create_config = EasyDict(SMAC_3m_masac_default_create_config) |
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create_config = SMAC_3m_masac_default_create_config |
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if __name__ == '__main__': |
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from ding.entry import serial_pipeline |
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serial_pipeline((main_config, create_config), seed=0) |
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