text: from trajectory_planner.environment import Environment
  from trajectory_planner.deep_q_learning import DeepQLearning
  import numpy as np
  import cProfile
  
  def test_deep_q_learning():
      reference = [[1, 2], [3, 4], [5, 6]]
      env = Environment(reference)
      learner = DeepQLearning(env)
      action = learner.eps_greedy(1, [[0, 0, 0, 0, 0, 0]])
      print("eps=1:", action)
  
      action = learner.eps_greedy(0, [[0, 0, 0, 0, 0, 0]])
      assert action == 0
  
      learner.replay_memory.append([np.array([0, 0, 0, 0, 0, 0]), 0, 10,
                                   np.array([1, 0, 0, 0, 0, 0]), False])
      learner.replay_memory.append([np.array([0, 1, 0, 0, 0, 0]), 1, 11,
                                   np.array([0, 0, 1, 0, 0, 0]), False])
      learner.replay_memory.append([np.array([0, 0, 0, 1, 0, 0]), 2, 12,
                                   np.array([0, 0, 0, 0, 1, 0]), True])
  
      learner.train()
      assert False
  
  
  def test_run_episode():
      reference = [[1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [
          1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [1, 0]]
      env = Environment(reference)
      learner = DeepQLearning(env, eps_scheduler_rate=20)
  
      for i in range(2):
          learner.runEpisode(600,400)
  
      assert False
  
  
  # from trajectory_planner.environment import Environment
  # from trajectory_planner.deep_q_learning import DeepQLearning
  # reference = [[1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [
  #     1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [1, 0]]
  # env = Environment(reference)
  # learner = DeepQLearning(env)
  
  # for i in range(10):
  #     learner.runEpisode(600, 400)
  
  