a1.previous_choice = a1_choice a2.previous_choice = a2_choice def play_game(self, rounds): for i in range(rounds): for j in range(len(self.agents)): for k in range(j+1, len(self.agents)): self.play_round(self.agents[j], self.agents[k]) 这段代码的意思

这段代码是一个多智能体博弈的游戏循环。在这个游戏中，有多个智能体（即self.agents列表中的元素），每个智能体具有一个previous_choice属性来记录上一轮选择的行动。play_game方法接受一个rounds参数，表示要进行多少轮博弈。在每一轮博弈中，每个智能体都会与其他智能体进行一次对决，即play_round方法。play_round方法会接受两个智能体作为参数，并根据经典的囚徒困境博弈的计分规则来计算它们的得分。在计算得分之后，智能体的previous_choice属性会被更新为本轮选择的行动。整个游戏循环会在多轮博弈后结束。

相关问题

class AbstractGreedyAndPrune(): def __init__(self, aoi: AoI, uavs_tours: dict, max_rounds: int, debug: bool = True): self.aoi = aoi self.max_rounds = max_rounds self.debug = debug self.graph = aoi.graph self.nnodes = self.aoi.n_targets self.uavs = list(uavs_tours.keys()) self.nuavs = len(self.uavs) self.uavs_tours = {i: uavs_tours[self.uavs[i]] for i in range(self.nuavs)} self.__check_depots() self.reachable_points = self.__reachable_points() def __pruning(self, mr_solution: MultiRoundSolution) -> MultiRoundSolution: return utility.pruning_multiroundsolution(mr_solution) def solution(self) -> MultiRoundSolution: mrs_builder = MultiRoundSolutionBuilder(self.aoi) for uav in self.uavs: mrs_builder.add_drone(uav) residual_ntours_to_assign = {i : self.max_rounds for i in range(self.nuavs)} tour_to_assign = self.max_rounds * self.nuavs visited_points = set() while not self.greedy_stop_condition(visited_points, tour_to_assign): itd_uav, ind_tour = self.local_optimal_choice(visited_points, residual_ntours_to_assign) residual_ntours_to_assign[itd_uav] -= 1 tour_to_assign -= 1 opt_tour = self.uavs_tours[itd_uav][ind_tour] visited_points |= set(opt_tour.targets_indexes) # update visited points mrs_builder.append_tour(self.uavs[itd_uav], opt_tour) return self.__pruning(mrs_builder.build()) class CumulativeGreedyCoverage(AbstractGreedyAndPrune): choice_dict = {} for ind_uav in range(self.nuavs): uav_residual_rounds = residual_ntours_to_assign[ind_uav] if uav_residual_rounds > 0: uav_tours = self.uavs_tours[ind_uav] for ind_tour in range(len(uav_tours)): tour = uav_tours[ind_tour] quality_tour = self.evaluate_tour(tour, uav_residual_rounds, visited_points) choice_dict[quality_tour] = (ind_uav, ind_tour) best_value = max(choice_dict, key=int) return choice_dict[best_value] def evaluate_tour(self, tour : Tour, round_count : int, visited_points : set): new_points = (set(tour.targets_indexes) - visited_points) return round_count * len(new_points) 如何改写上述程序，使其能返回所有已经探索过的目标点visited_points的数量，请用代码表示

可以在 `solution()` 方法中添加一个变量来记录已经探索过的目标点数量，然后在每次更新 `visited_points` 后更新这个变量。下面是修改后的代码：

class AbstractGreedyAndPrune():
    def __init__(self, aoi: AoI, uavs_tours: dict, max_rounds: int, debug: bool = True):
        self.aoi = aoi
        self.max_rounds = max_rounds
        self.debug = debug
        self.graph = aoi.graph
        self.nnodes = self.aoi.n_targets
        self.uavs = list(uavs_tours.keys())
        self.nuavs = len(self.uavs)
        self.uavs_tours = {i: uavs_tours[self.uavs[i]] for i in range(self.nuavs)}
        self.__check_depots()
        self.reachable_points = self.__reachable_points()
        
    def __pruning(self, mr_solution: MultiRoundSolution) -> MultiRoundSolution:
        return utility.pruning_multiroundsolution(mr_solution)
    
    def solution(self) -> Tuple[MultiRoundSolution, int]:
        mrs_builder = MultiRoundSolutionBuilder(self.aoi)
        
        for uav in self.uavs:
            mrs_builder.add_drone(uav)
            
        residual_ntours_to_assign = {i : self.max_rounds for i in range(self.nuavs)}
        tour_to_assign = self.max_rounds * self.nuavs
        visited_points = set()
        explored_points = 0
        
        while not self.greedy_stop_condition(visited_points, tour_to_assign):
            itd_uav, ind_tour = self.local_optimal_choice(visited_points, residual_ntours_to_assign)
            residual_ntours_to_assign[itd_uav] -= 1
            tour_to_assign -= 1
            opt_tour = self.uavs_tours[itd_uav][ind_tour]
            new_points = set(opt_tour.targets_indexes) - visited_points
            explored_points += len(new_points)
            visited_points |= new_points  # update visited points
            mrs_builder.append_tour(self.uavs[itd_uav], opt_tour)
        
        return self.__pruning(mrs_builder.build()), explored_points
    
    
class CumulativeGreedyCoverage(AbstractGreedyAndPrune):
    def evaluate_tour(self, tour : Tour, round_count : int, visited_points : set):
        new_points = set(tour.targets_indexes) - visited_points
        return round_count * len(new_points)
    
    def local_optimal_choice(self, visited_points, residual_ntours_to_assign):
        choice_dict = {}
        for ind_uav in range(self.nuavs):
            uav_residual_rounds = residual_ntours_to_assign[ind_uav]
            if uav_residual_rounds > 0:
                uav_tours = self.uavs_tours[ind_uav]
                for ind_tour in range(len(uav_tours)):
                    tour = uav_tours[ind_tour]
                    quality_tour = self.evaluate_tour(tour, uav_residual_rounds, visited_points)
                    choice_dict[quality_tour] = (ind_uav, ind_tour)
        
        best_value = max(choice_dict, key=int)
        return choice_dict[best_value]

class CumulativeGreedyCoverage(AbstractGreedyAndPrune): choice_dict = {} for ind_uav in range(self.nuavs): uav_residual_rounds = residual_ntours_to_assign[ind_uav] if uav_residual_rounds > 0: uav_tours = self.uavs_tours[ind_uav] for ind_tour in range(len(uav_tours)): tour = uav_tours[ind_tour] quality_tour = self.evaluate_tour(tour, uav_residual_rounds, visited_points) choice_dict[quality_tour] = (ind_uav, ind_tour) best_value = max(choice_dict, key=int) return choice_dict[best_value] def evaluate_tour(self, tour : Tour, round_count : int, visited_points : set): new_points = (set(tour.targets_indexes) - visited_points) return round_count * len(new_points) 如何改写上述程序，使其能返回所有已经探索过的目标点visited_points的数量，请用代码表示

可以将 `visited_points` 作为类成员变量，在每次调用 `evaluate_tour()` 函数时，将探索过的目标点添加到该变量中。然后在 `CumulativeGreedyCoverage` 类中，将所有巡游的 `visited_points` 汇总起来，即可得到所有已经探索过的目标点数量。具体实现如下：

class CumulativeGreedyCoverage(AbstractGreedyAndPrune):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.visited_points = set()
        
    def run(self):
        choice_dict = {}
        for ind_uav in range(self.nuavs):
            uav_residual_rounds = residual_ntours_to_assign[ind_uav]
            if uav_residual_rounds > 0:
                uav_tours = self.uavs_tours[ind_uav]
                for ind_tour in range(len(uav_tours)):
                    tour = uav_tours[ind_tour]
                    quality_tour = self.evaluate_tour(tour, uav_residual_rounds, self.visited_points)
                    choice_dict[quality_tour] = (ind_uav, ind_tour)
        best_value = max(choice_dict, key=int)
        return choice_dict[best_value], self.visited_points

    def evaluate_tour(self, tour : Tour, round_count : int, visited_points : set):
        new_points = set(tour.targets_indexes) - visited_points
        self.visited_points.update(new_points)
        return round_count * len(new_points)

在 `run()` 函数中，我们新增了一个变量 `self.visited_points` 来记录所有已经探索过的目标点，然后在 `evaluate_tour()` 函数中，我们将探索过的目标点添加到该变量中。最后，我们在 `run()` 函数中返回所有已经探索过的目标点数量。