text: import gym
  
  from deephyper.search.nas.baselines import deepq
  
  
  def callback(lcl, _glb):
      # stop training if reward exceeds 199
      is_solved = lcl['t'] > 100 and sum(
          lcl['episode_rewards'][-101:-1]) / 100 >= 199
      return is_solved
  
  
  def main():
      env = gym.make("CartPole-v0")
      act = deepq.learn(
          env,
          network='mlp',
          lr=1e-3,
          total_timesteps=100000,
          buffer_size=50000,
          exploration_fraction=0.1,
          exploration_final_eps=0.02,
          print_freq=10,
          callback=callback
      )
      print("Saving model to cartpole_model.pkl")
      act.save("cartpole_model.pkl")
  
  
  if __name__ == '__main__':
      main()
  