人工智猴子摘香蕉代码实现

以下是一个简单的人工智能猴子摘香蕉的代码实现，基于遗传算法：

import numpy as np

# 定义状态空间和动作空间
states = range(1, 101)
actions = ['left', 'right', 'up', 'down', 'pick']

# 定义奖励函数
def reward(state, action):
    if state == 100 and action == 'pick':
        return 10
    elif action == 'pick':
        return -1
    else:
        return 0

# 定义状态转移函数
def transition(state, action):
    if action == 'left':
        return max(state - 1, 1)
    elif action == 'right':
        return min(state + 1, 100)
    elif action == 'up':
        return max(state - 10, 1)
    elif action == 'down':
        return min(state + 10, 100)
    else:
        return state

# 定义遗传算法
def genetic_algorithm(states, actions, reward, transition, generations=100, population_size=50, mutation_rate=0.1):
    # 初始化种群
    population = []
    for i in range(population_size):
        chromosome = np.random.choice(actions, size=100)
        population.append(chromosome)

    # 进行若干轮迭代
    for generation in range(generations):
        # 计算每个染色体的适应度
        fitness = []
        for chromosome in population:
            state = 1
            total_reward = 0
            for action in chromosome:
                total_reward += reward(state, action)
                state = transition(state, action)
            fitness.append(total_reward)

        # 选择优秀染色体进行交叉和变异
        new_population = []
        for i in range(population_size):
            # 选择父母染色体
            parent1 = population[np.random.choice(range(population_size), p=fitness/np.sum(fitness))]
            parent2 = population[np.random.choice(range(population_size), p=fitness/np.sum(fitness))]

            # 进行交叉操作
            crossover_point = np.random.choice(range(100))
            child = np.concatenate((parent1[:crossover_point], parent2[crossover_point:]))

            # 进行变异操作
            for j in range(100):
                if np.random.uniform() < mutation_rate:
                    child[j] = np.random.choice(actions)

            new_population.append(child)

        # 使用新的种群进行下一轮迭代
        population = new_population

    # 返回最优解
    best_chromosome = population[np.argmax(fitness)]
    return best_chromosome

# 进行遗传算法搜索
best_chromosome = genetic_algorithm(states, actions, reward, transition)

# 输出最优解
state = 1
for action in best_chromosome:
    print("State: %d, Action: %s" % (state, action))
    state = transition(state, action)
    if state == 100:
        break

该代码实现了一个基于遗传算法的人工智能猴子，可以在一个 $10 \times 10$ 的网格世界中寻找最优的动作序列，以摘到香蕉并获得最大奖励。在遗传算法的迭代过程中，我们使用染色体表示动作序列，并使用适应度函数来评估染色体的优劣程度。在每一轮迭代中，我们选择适应度高的染色体进行交叉和变异操作，生成新的染色体来构成下一代种群。最终，我们找到了一条最优的动作序列，可以让猴子摘到香蕉并获得最大奖励。