def update_q(self, predicted_q_arr, reward_value_arr, next_max_q_arr):
        '''
        Update Q.
        
        Args:
            predicted_q_arr:    `np.ndarray` of predicted Q-Values.
            reward_value_arr:   `np.ndarray` of reward values.
            next_max_q_arr:     `np.ndarray` of maximum Q-Values in next time step.
        
        Returns:
            `np.ndarray` of real Q-Values.
        '''
        # Update Q-Value.
        return predicted_q_arr + (self.alpha_value * (reward_value_arr + (self.gamma_value * next_max_q_arr) - predicted_q_arr))
Update Q.
        
        Args:
            predicted_q_arr:    `np.ndarray` of predicted Q-Values.
            reward_value_arr:   `np.ndarray` of reward values.
            next_max_q_arr:     `np.ndarray` of maximum Q-Values in next time step.
        
        Returns:
            `np.ndarray` of real Q-Values.