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from easydict import EasyDict |
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cartpole_bco_config = dict( |
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exp_name='cartpole_bco_seed0', |
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env=dict( |
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collector_env_num=8, |
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evaluator_env_num=5, |
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n_evaluator_episode=5, |
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stop_value=195, |
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replay_path=None, |
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), |
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policy=dict( |
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cuda=True, |
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continuous=False, |
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loss_type='l1_loss', |
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model=dict( |
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obs_shape=4, |
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action_shape=2, |
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encoder_hidden_size_list=[128, 128, 64], |
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dueling=True, |
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), |
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learn=dict( |
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train_epoch=20, |
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batch_size=128, |
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learning_rate=0.001, |
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weight_decay=1e-4, |
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momentum=0.9, |
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decay_epoch=30, |
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decay_rate=1, |
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warmup_lr=1e-4, |
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warmup_epoch=3, |
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optimizer='SGD', |
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lr_decay=True, |
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), |
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collect=dict( |
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n_episode=10, |
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model_path='abs model path', |
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data_path='abs data path', |
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), |
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eval=dict(evaluator=dict(eval_freq=40, )), |
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other=dict(eps=dict( |
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type='exp', |
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start=0.95, |
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end=0.1, |
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decay=10000, |
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), ) |
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), |
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bco=dict( |
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learn=dict(idm_batch_size=32, idm_learning_rate=0.001, idm_weight_decay=1e-4, idm_train_epoch=10), |
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model=dict(idm_encoder_hidden_size_list=[60, 80, 100, 40], action_space='discrete'), |
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alpha=0.8, |
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) |
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) |
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cartpole_bco_config = EasyDict(cartpole_bco_config) |
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main_config = cartpole_bco_config |
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cartpole_bco_create_config = dict( |
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env=dict( |
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type='cartpole', |
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import_names=['dizoo.classic_control.cartpole.envs.cartpole_env'], |
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), |
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env_manager=dict(type='subprocess'), |
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policy=dict(type='bc'), |
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collector=dict(type='episode') |
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) |
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cartpole_bco_create_config = EasyDict(cartpole_bco_create_config) |
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create_config = cartpole_bco_create_config |
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if __name__ == "__main__": |
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from ding.entry import serial_pipeline_bco |
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from dizoo.classic_control.cartpole.config import cartpole_dqn_config, cartpole_dqn_create_config |
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expert_main_config = cartpole_dqn_config |
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expert_create_config = cartpole_dqn_create_config |
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serial_pipeline_bco( |
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[main_config, create_config], [cartpole_dqn_config, cartpole_dqn_create_config], seed=0, max_env_step=100000 |
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) |
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