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from ding.entry import serial_pipeline |
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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_madqn_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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shared_memory=False, |
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special_global_state=True, |
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stop_value=0.999, |
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n_evaluator_episode=32, |
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), |
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policy=dict( |
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nstep=3, |
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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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global_obs_shape=152, |
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action_shape=12, |
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hidden_size_list=[256, 256], |
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), |
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learn=dict( |
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update_per_collect=40, |
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batch_size=32, |
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learning_rate=0.0005, |
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clip_value=10, |
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target_update_theta=0.008, |
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discount_factor=0.95, |
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), |
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collect=dict( |
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collector=dict(get_train_sample=True, ), |
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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=1000, )), |
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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=50000, |
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), |
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replay_buffer=dict(replay_buffer_size=50000, ), |
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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='base'), |
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policy=dict(type='madqn'), |
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collector=dict(type='episode'), |
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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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import argparse |
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parser = argparse.ArgumentParser() |
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parser.add_argument('--seed', '-s', type=int, default=0) |
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args = parser.parse_args() |
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train(args) |
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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, max_env_step=1e7) |
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if __name__ == "__main__": |
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import argparse |
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parser = argparse.ArgumentParser() |
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parser.add_argument('--seed', '-s', type=int, default=0) |
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args = parser.parse_args() |
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train(args) |
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