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from easydict import EasyDict |
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agent_num = 5 |
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collector_env_num = 16 |
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evaluator_env_num = 8 |
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main_config = dict( |
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exp_name='smac_5m6m_collaq_seed0', |
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env=dict( |
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map_name='5m_vs_6m', |
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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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stop_value=0.999, |
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n_evaluator_episode=32, |
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obs_alone=True, |
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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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model=dict( |
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agent_num=agent_num, |
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obs_shape=72, |
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alone_obs_shape=52, |
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global_obs_shape=98, |
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action_shape=12, |
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hidden_size_list=[128], |
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attention=True, |
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self_feature_range=[54, 55], |
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ally_feature_range=[34, 54], |
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attention_size=32, |
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mixer=True, |
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lstm_type='gru', |
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dueling=False, |
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), |
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learn=dict( |
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update_per_collect=20, |
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batch_size=32, |
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learning_rate=0.0005, |
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clip_value=4, |
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double_q=False, |
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target_update_theta=0.005, |
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discount_factor=0.95, |
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collaq_loss_weight=1.0, |
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), |
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collect=dict( |
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n_episode=32, |
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unroll_len=10, |
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env_num=collector_env_num, |
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), |
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eval=dict(env_num=evaluator_env_num, evaluator=dict(eval_freq=100, )), |
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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=30000, |
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), |
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replay_buffer=dict( |
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replay_buffer_size=50000, |
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max_reuse=1e+9, |
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max_staleness=1e+9, |
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), |
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), |
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), |
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) |
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main_config = EasyDict(main_config) |
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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='subprocess'), |
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policy=dict(type='collaq'), |
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collector=dict(type='episode', get_train_sample=True), |
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) |
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create_config = EasyDict(create_config) |
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def train(args): |
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config = [main_config, create_config] |
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serial_pipeline(config, seed=args.seed) |
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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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