text: import numpy as np
  
  from dps.train import training_loop
  from dps.config import DEFAULT_CONFIG
  from dps.rl.algorithms import qlearning
  from dps.env import cliff_walk
  from dps.rl.policy import BuildLinearController
  
  config = DEFAULT_CONFIG.copy()
  config.update(qlearning.config)
  config.update(cliff_walk.config)
  
  config.update(
      T=20,
      width=4,
      n_actions=2,
  
      build_controller=BuildLinearController(),
      max_steps=100000,
      threshold=-1000,
  
      steps_per_target_update=1,
      beta_schedule=0.0,
      alpha=0.0,
      double=False,
      reverse_double=False,
      exploration_schedule=1.0,
  )
  
  
  np.random.seed(10)
  config.order = np.random.randint(config.n_actions, size=config.width)
  np.random.seed()
  
  
  with config:
      training_loop()
  