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| from easydict import EasyDict | |
| cartpole_dqfd_config = dict( | |
| exp_name='cartpole_dqfd_seed0', | |
| env=dict( | |
| collector_env_num=8, | |
| evaluator_env_num=5, | |
| n_evaluator_episode=5, | |
| stop_value=195, | |
| ), | |
| policy=dict( | |
| cuda=True, | |
| priority=True, | |
| model=dict( | |
| obs_shape=4, | |
| action_shape=2, | |
| encoder_hidden_size_list=[128, 128, 64], | |
| dueling=True, | |
| ), | |
| nstep=3, | |
| discount_factor=0.97, | |
| learn=dict( | |
| batch_size=64, | |
| learning_rate=0.001, | |
| lambda1=1, # n-step return | |
| lambda2=3.0, # supervised loss | |
| # set this to be 0 (L2 loss = 0) with expert_replay_buffer_size = 0 and lambda1 = 0 | |
| # recover the one step pdd dqn | |
| lambda3=0, # L2 regularization | |
| per_train_iter_k=10, | |
| expert_replay_buffer_size=10000, # justify the buffer size of the expert buffer | |
| ), | |
| collect=dict( | |
| n_sample=8, | |
| # Users should add their own model path here. Model path should lead to a model. | |
| # Absolute path is recommended. | |
| # In DI-engine, it is ``exp_name/ckpt/ckpt_best.pth.tar``. | |
| model_path='model_path_placeholder', | |
| ), | |
| # note: this is the times after which you learns to evaluate | |
| eval=dict(evaluator=dict(eval_freq=50, )), | |
| other=dict( | |
| eps=dict( | |
| type='exp', | |
| start=0.95, | |
| end=0.1, | |
| decay=10000, | |
| ), | |
| replay_buffer=dict(replay_buffer_size=20000, ), | |
| ), | |
| ), | |
| ) | |
| cartpole_dqfd_config = EasyDict(cartpole_dqfd_config) | |
| main_config = cartpole_dqfd_config | |
| cartpole_dqfd_create_config = dict( | |
| env=dict( | |
| type='cartpole', | |
| import_names=['dizoo.classic_control.cartpole.envs.cartpole_env'], | |
| ), | |
| env_manager=dict(type='base'), | |
| policy=dict(type='dqfd'), | |
| ) | |
| cartpole_dqfd_create_config = EasyDict(cartpole_dqfd_create_config) | |
| create_config = cartpole_dqfd_create_config | |
| if __name__ == "__main__": | |
| # or you can enter `ding -m serial_dqfd -c cartpole_dqfd_config.py -s 0` | |
| # then input ``cartpole_dqfd_config.py`` upon the instructions. | |
| # The reason we need to input the dqfd config is we have to borrow its ``_get_train_sample`` function | |
| # in the collector part even though the expert model may be generated from other Q learning algos. | |
| from ding.entry.serial_entry_dqfd import serial_pipeline_dqfd | |
| from dizoo.classic_control.cartpole.config import cartpole_dqfd_config, cartpole_dqfd_create_config | |
| expert_main_config = cartpole_dqfd_config | |
| expert_create_config = cartpole_dqfd_create_config | |
| serial_pipeline_dqfd((main_config, create_config), (expert_main_config, expert_create_config), seed=0) | |