text: from easydict import EasyDict
  
  cartpole_trex_dqn_config = dict(
      exp_name='cartpole_trex_dqn_seed0',
      env=dict(
          collector_env_num=8,
          evaluator_env_num=5,
          n_evaluator_episode=5,
          stop_value=195,
          replay_path='cartpole_dqn/video',
      ),
      reward_model=dict(
          type='trex',
          algo_for_model='dqn',
          env_id='CartPole-v0',
          min_snippet_length=5,
          max_snippet_length=100,
          checkpoint_min=0,
          checkpoint_max=500,
          checkpoint_step=100,
          learning_rate=1e-5,
          update_per_collect=1,
          expert_model_path='abs model path',
          reward_model_path='abs data path + ./cartpole.params',
          offline_data_path='abs data path',
      ),
      policy=dict(
          load_path='',
          cuda=False,
          model=dict(
              obs_shape=4,
              action_shape=2,
              encoder_hidden_size_list=[128, 128, 64],
              dueling=True,
          ),
          nstep=1,
          discount_factor=0.97,
          learn=dict(
              batch_size=64,
              learning_rate=0.001,
          ),
          collect=dict(n_sample=8),
          eval=dict(evaluator=dict(eval_freq=40, )),
          other=dict(
              eps=dict(
                  type='exp',
                  start=0.95,
                  end=0.1,
                  decay=10000,
              ),
              replay_buffer=dict(replay_buffer_size=20000, ),
          ),
      ),
  )
  cartpole_trex_dqn_config = EasyDict(cartpole_trex_dqn_config)
  main_config = cartpole_trex_dqn_config
  cartpole_trex_dqn_create_config = dict(
      env=dict(
          type='cartpole',
          import_names=['dizoo.classic_control.cartpole.envs.cartpole_env'],
      ),
      env_manager=dict(type='base'),
      policy=dict(type='dqn'),
  )
  cartpole_trex_dqn_create_config = EasyDict(cartpole_trex_dqn_create_config)
  create_config = cartpole_trex_dqn_create_config
  
  if __name__ == "__main__":
      # Users should first run ``cartpole_dqn_config.py`` to save models (or checkpoints).
      # Note: Users should check that the checkpoints generated should include iteration_'checkpoint_min'.pth.tar, iteration_'checkpoint_max'.pth.tar with the interval checkpoint_step
      # where checkpoint_max, checkpoint_min, checkpoint_step are specified above.
      import argparse
      import torch
      from ding.entry import trex_collecting_data
      from ding.entry import serial_pipeline_reward_model_trex
      parser = argparse.ArgumentParser()
      parser.add_argument('--cfg', type=str, default='please enter abs path for this file')
      parser.add_argument('--seed', type=int, default=0)
      parser.add_argument('--device', type=str, default='cuda' if torch.cuda.is_available() else 'cpu')
      args = parser.parse_args()
      # The function ``trex_collecting_data`` below is to collect episodic data for training the reward model in trex.
      trex_collecting_data(args)
      serial_pipeline_reward_model_trex((main_config, create_config))
  