code: def select_action(self, q_values):
          
          assert q_values.ndim == 1
          nb_actions = q_values.shape[0]
  
          if np.random.uniform() < self.eps:
              action = np.random.randint(0, nb_actions)
          else:
              action = np.argmax(q_values)
          return action
docs: Return the selected action
  
          # Arguments
              q_values (np.ndarray): List of the estimations of Q for each action
  
          # Returns
              Selection action