from easydict import EasyDict agent_num = 10 collector_env_num = 16 evaluator_env_num = 8 main_config = dict( exp_name='smac_MMM2_coma_seed0', env=dict( map_name='MMM2', difficulty=7, reward_only_positive=True, mirror_opponent=False, agent_num=agent_num, collector_env_num=collector_env_num, evaluator_env_num=evaluator_env_num, stop_value=0.999, n_evaluator_episode=32, manager=dict( shared_memory=False, reset_timeout=6000, ), ), policy=dict( model=dict( agent_num=agent_num, obs_shape=dict( agent_state=204, global_state=322, ), action_shape=18, actor_hidden_size_list=[64], ), learn=dict( update_per_collect=20, batch_size=32, learning_rate=0.0005, target_update_theta=0.001, discount_factor=0.99, td_lambda=0.9, policy_weight=0.001, value_weight=1, entropy_weight=0.01, ), collect=dict( n_episode=32, unroll_len=10, env_num=collector_env_num, ), eval=dict(env_num=evaluator_env_num, evaluator=dict(eval_freq=100, )), other=dict( eps=dict( type='exp', start=0.5, end=0.01, decay=200000, ), replay_buffer=dict( replay_buffer_size=5000, max_use=10, ), ), ), ) main_config = EasyDict(main_config) create_config = dict( env=dict( type='smac', import_names=['dizoo.smac.envs.smac_env'], ), env_manager=dict(type='subprocess'), policy=dict(type='coma'), collector=dict(type='episode', get_train_sample=True), ) create_config = EasyDict(create_config) if __name__ == '__main__': from ding.entry import serial_pipeline serial_pipeline((main_config, create_config), seed=0)