【强化学习】用Q-Learning优化车间调度

一、车间调度问题

车间调度是NP难问题，传统方法难以获得最优解。强化学习能通过不断学习找到近似最优调度策略。

二、问题建模

状态空间

S = (machine_status, job_queue, processing_time)机器状态 | 作业队列 | 加工时间

动作空间

A = {assign_job_1, assign_job_2, ..., assign_job_n}选择下一个要加工的作业

奖励函数

R = -makespan - penalty延迟惩罚 + 准时奖励

三、Q-Learning实现

import numpy as npclass QScheduler:    def __init__(self, n_jobs, n_machines):        self.q_table = np.zeros((n_states, n_actions))        self.alpha = 0.1      # 学习率        self.gamma = 0.9      # 折扣因子        self.epsilon = 0.1    # 探索率    def choose_action(self, state):        if np.random.random() < self.epsilon:            return np.random.randint(self.n_actions)        return np.argmax(self.q_table[state])    def update(self, state, action, reward, next_state):        best_next = np.max(self.q_table[next_state])        self.q_table[state, action] += self.alpha * (reward + self.gamma * best_next - self.q_table[state, action])

四、训练流程

for episode in range(1000):    state = env.reset()    done = False    while not done:        action = scheduler.choose_action(state)        next_state, reward, done = env.step(action)        scheduler.update(state, action, reward, next_state)        state = next_state

五、效果对比

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