text: import numpy as np
  import os
  from visual_mpc.agent.benchmarking_agent import BenchmarkAgent
  from visual_mpc.envs.robot_envs.autograsp_env import AutograspEnv
  from visual_mpc.policy.cem_controllers.pixel_cost_controller import PixelCostController
  from visual_mpc.envs.robot_envs.util.topic_utils import IMTopic
  
  
  env_params = {
      'camera_topics': [IMTopic('/front/image_raw', flip=True)],                  #, IMTopic('/bot/image_raw'), IMTopic('/bot2/image_raw')],
      'gripper_attached': 'none',
      'cleanup_rate': -1,
      'duration': 2.5
  }
  
  agent = {'type' : BenchmarkAgent,
           'env': (AutograspEnv, env_params),
           'T': 14,  #number of commands per episodes (issued at control_rate / substeps HZ)
           'image_height': 48,
           'image_width': 64,
           'make_final_recording': ''
  }
  
  policy = {
      'type': PixelCostController,
      'replan_interval': 13,
      'num_samples': 600,
      'selection_frac': 0.05,
      'predictor_propagation': True,   # use the model get the designated pixel for the next step!
      'initial_std_lift': 0.2,  # std dev. in xy
      'initial_std_rot': np.pi / 10,
      'rejection_sampling': False,
      'nactions': 13,
      'repeat': 1,
  
      "model_params_path": "~/robotiq_models/robotiq_startall/experiment_state-2019-07-07_00-00-26.json",
      "model_restore_path": "~/robotiq_models/robotiq_startall/start_all/checkpoint_70000/model-190000",
  
  }
  
  config = {
      "experiment_name": "fine_tune_start_all",
      'traj_per_file':128,
      'save_data': True,
      'save_raw_images' : True,
      'start_index':0,
      'end_index': 30000,
      'agent': agent,
      'policy': policy,
      'ngroup': 1000,
      'nshuffle' : 200
  }
  