code: def check_the_end_flag(self, state_key):
          '''
          Check the end flag.
          
          If this return value is `True`, the learning is end.
  
          Args:
              state_key:    The key of state in `self.t`.
  
          Returns:
              bool
          '''
          # As a rule, the learning can not be stopped.
          x, y = state_key
          end_point_tuple = np.where(self.__map_arr == self.__end_point_label)
          end_point_x_arr, end_point_y_arr = end_point_tuple
          if x == end_point_x_arr[0] and y == end_point_y_arr[0]:
              return True
          else:
              return Falsf check_the_end_flag(self, state_key):
          '''
          Check the end flag.
          
          If this return value is `True`, the learning is end.
  
          Args:
              state_key:    The key of state in `self.t`.
  
          Returns:
              bool
          '''
          # As a rule, the learning can not be stopped.
          x, y = state_key
          end_point_tuple = np.where(self.__map_arr == self.__end_point_label)
          end_point_x_arr, end_point_y_arr = end_point_tuple
          if x == end_point_x_arr[0] and y == end_point_y_arr[0]:
              return True
          else:
              return False
docs: Check the end flag.
          
          If this return value is `True`, the learning is end.
  
          Args:
              state_key:    The key of state in `self.t`.
  
          Returns:
              bool