text: ### Multistep DQN Pong_ML-Agents Config ###
  
  env = {"name": "pong_mlagent", "train_mode": True}
  
  agent = {
      "name": "multistep",
      "network": "dqn",
      "gamma": 0.99,
      "n_step": 4,
      "epsilon_init": 1.0,
      "epsilon_min": 0.1,
      "explore_ratio": 0.1,
      "buffer_size": 50000,
      "batch_size": 32,
      "start_train_step": 25000,
      "target_update_period": 1000,
  }
  
  optim = {
      "name": "adam",
      "lr": 2.5e-4,
  }
  train = {
      "training": True,
      "load_path": None,
      "run_step": 200000,
      "print_period": 5000,
      "save_period": 50000,
      "eval_iteration": 10,
      # distributed setting
      "update_period": 8,
      "num_workers": 16,
  }
  