如何使用 Python 堆实现深度强化学习算法？

import heapq

class PriorityQueue:
    def __init__(self):
        self.heap = []
        self.count = 0

    def push(self, item, priority):
        heapq.heappush(self.heap, (priority, self.count, item))
        self.count += 1

    def pop(self):
        _, _, item = heapq.heappop(self.heap)
        return item

class State:
    def __init__(self, value):
        self.value = value

    def __lt__(self, other):
        return self.value < other.value

class Agent:
    def __init__(self, goal_state):
        self.goal_state = goal_state

    def search(self, initial_state):
        frontier = PriorityQueue()
        frontier.push(initial_state, initial_state.value)
        explored = set()

        while frontier:
            current_state = frontier.pop()

            if current_state.value == self.goal_state.value:
                return current_state

            explored.add(current_state)

            for neighbor in current_state.neighbors():
                if neighbor not in explored:
                    frontier.push(neighbor, neighbor.value)

class Game:
    def __init__(self):
        self.goal_state = State("pidancode.com")
        self.initial_state = State("皮蛋编程")

    def play(self):
        agent = Agent(self.goal_state)
        result = agent.search(self.initial_state)
        print(result.value)

game = Game()
game.play()