pso-Q-learning多分类python代码

下面是一个简单的PSO-Q-learning多分类的Python代码示例，仅供参考：

import numpy as np
import random

# 定义状态和动作
states = np.random.rand(100, 10)
actions = np.arange(5)

# 初始化Q值函数和粒子群
Q = np.zeros((100, 5))
particles = np.random.rand(10, 5, 15)

# 定义PSO参数
w = 0.8
c1 = 0.2
c2 = 0.6

# 定义Q-learning参数
alpha = 0.5
gamma = 0.9

# 计算粒子适应度
def fitness(particle):
    scores = []
    for i in range(10):
        for j in range(5):
            state = states[i]
            action = actions[j]
            next_state = states[np.random.choice(100)]
            reward = np.dot(state, particle[j*3:j*3+3])
            next_action = np.argmax(Q[next_state])
            Q[state, action] = (1-alpha)*Q[state, action] + alpha*(reward+gamma*Q[next_state, next_action])
        scores.append(np.mean(np.argmax(Q, axis=1) == i))
    return np.mean(scores)

# 更新粒子速度和位置
for i in range(100):
    for j in range(5):
        particle = particles[i,j]
        velocity = particles[i,j,5:10]
        pbest_particle = particles[i,j,10:]
        gbest_particle = particles[i,j+1,10:]
        fitness_p = fitness(particle)
        fitness_pbest = fitness(pbest_particle)
        fitness_gbest = fitness(gbest_particle)
        velocity = w*velocity + c1*random.random()*(pbest_particle-particle) + c2*random.random()*(gbest_particle-particle)
        particle = particle + velocity
        particles[i,j] = particle
        if fitness(particle) > fitness_pbest:
            particles[i,j,10:] = particle
        if fitness(particle) > fitness_gbest:
            particles[i,j+1,10:] = particle

# 更新Q值函数
for i in range(100):
    for j in range(5):
        state = states[i]
        action = actions[j]
        next_state = states[np.random.choice(100)]
        reward = np.dot(state, particles[i,j*3:j*3+3])
        next_action = np.argmax(Q[next_state])
        Q[state, action] = (1-alpha)*Q[state, action] + alpha*(reward+gamma*Q[next_state, next_action])

需要注意的是，上述代码仅作为一个简单的示例，实际应用中需要根据具体问题进行调整和优化。