python实现吃豆人AI代码

以下是一个使用Q-learning算法实现吃豆人AI的Python代码示例：

import random

class PacmanAI:
    def __init__(self):
        self.learning_rate = 0.8
        self.discount_rate = 0.95
        self.epsilon = 0.2
        self.q_table = {}

    def get_state(self, game_state):
        # 将游戏状态转化为一个向量
        state = []
        state.append(game_state['pacman_position'])
        for ghost in game_state['ghost_positions']:
            state.append(ghost)
        for bean in game_state['bean_positions']:
            state.append(bean)
        return tuple(state)

    def get_action(self, game_state):
        state = self.get_state(game_state)
        if random.uniform(0, 1) < self.epsilon:
            # 探索
            return random.choice(game_state['legal_actions'])
        else:
            # 选择最佳动作
            if state not in self.q_table:
                self.q_table[state] = {}
                for action in game_state['legal_actions']:
                    self.q_table[state][action] = 0
            return max(self.q_table[state], key=self.q_table[state].get)

    def update_q_table(self, state, action, reward, next_state):
        if state not in self.q_table:
            self.q_table[state] = {}
            for a in legal_actions:
                self.q_table[state][a] = 0
        if next_state not in self.q_table:
            self.q_table[next_state] = {}
            for a in legal_actions:
                self.q_table[next_state][a] = 0
        old_value = self.q_table[state][action]
        next_max = max(self.q_table[next_state].values())
        new_value = (1 - self.learning_rate) * old_value + self.learning_rate * (reward + self.discount_rate * next_max)
        self.q_table[state][action] = new_value

在训练过程中，可以使用以下代码来更新Q-table：

state = agent.get_state(game_state)
action = agent.get_action(game_state)
next_state, reward, done = game.next_state(game_state, action)
next_state = agent.get_state(next_state)
agent.update_q_table(state, action, reward, next_state)

在测试过程中，可以使用以下代码来选择动作：

action = agent.get_action(game_state)

这只是一个简单的实现，需要根据具体情况进行调整和优化，比如添加更多的特征、增加训练次数等等。