code: def distribute_payoff(self, match_set):
          
  
          assert isinstance(match_set, MatchSet)
          assert match_set.algorithm is self
          assert match_set.selected_action is not None
  
          payoff = float(match_set.payoff)
  
          action_set = match_set[match_set.selected_action]
          action_set_size = sum(rule.numerosity for rule in action_set)
  
          # Update the average reward, error, and action set size of each
          # rule participating in the action set.
          for rule in action_set:
              rule.experience += 1
  
              update_rate = max(self.learning_rate, 1 / rule.experience)
  
              rule.average_reward += (
                  (payoff - rule.average_reward) *
                  update_rate
              )
  
              rule.error += (
                  (abs(payoff - rule.average_reward) - rule.error) *
                  update_rate
  
              )
  
              rule.action_set_size += (
                  (action_set_size - rule.action_set_size) *
                  update_rate
              )
  
          # Update the fitness of the rules.
          self._update_fitness(action_set)
  
          # If the parameters so indicate, perform action set subsumption.
          if self.do_action_set_subsumption:
              self._action_set_subsumption(action_set)
docs: Distribute the payoff received in response to the selected
          action of the given match set among the rules in the action set
          which deserve credit for recommending the action. The match_set
          argument is the MatchSet instance which suggested the selected
          action and earned the payoff.
  
          Usage:
              match_set = model.match(situation)
              match_set.select_action()
              match_set.payoff = reward
              model.algorithm.distribute_payoff(match_set)
  
          Arguments:
              match_set: A MatchSet instance for which the accumulated payoff
                  needs to be distributed among its classifier rules.
          Return: None