code: def play_env_problem_randomly(env_problem,
                                num_steps):
    
    # Reset all environments.
    env_problem.reset()
  
    # Play all environments, sampling random actions each time.
    for _ in range(num_steps):
      # Sample batch_size actions from the action space and stack them.
      actions = np.stack([env_problem.action_space.sample() for _ in range(
          env_problem.batch_size)])
  
      # Execute actions, observations are stored in `env_problem`.
      _, _, dones, _ = env_problem.step(actions)
  
      # Get the indices where we are done and reset those.
      env_problem.reset(indices=done_indices(dones))
docs: Plays the env problem by randomly sampling actions for `num_steps`.