强化学习选址的伪代码

强化学习（Reinforcement Learning, RL）中的选址策略通常用于确定智能体在给定状态下应该采取的动作。这里有一个简单的强化学习选址策略的伪代码示例，使用Q-learning算法作为基础：

// 假设我们有一个环境类Environment和智能体类Agent
class Environment:
    def __init__(self):
        # 环境初始化

    def step(self, action):
        # 根据动作执行一步并返回新的状态和奖励

class Agent:
    def __init__(self, actions, learning_rate, discount_factor):
        self.q_table = {state: {action: 0 for action in actions} for state in states}
        self.learning_rate = learning_rate
        self.discount_factor = discount_factor

    def select_action(self, state):
        if random.random() < epsilon:  # 选择随机动作
            return random.choice(actions)
        else:  # 选择具有最大Q值的动作
            return max(self.q_table[state], key=self.q_table[state].get)

    def update_q_value(self, state, action, reward, next_state):
        max_future_q = max(self.q_table[next_state].values()) if next_state is not None else 0
        current_q = self.q_table[state][action]
        new_q = (1 - self.learning_rate) * current_q + self.learning_rate * (reward + self.discount_factor * max_future_q)
        self.q_table[state][action] = new_q

# 使用
agent = Agent(environment.actions, learning_rate=0.1, discount_factor=0.9)
for episode in range(num_episodes):
    state = environment.reset()
    while True:
        action = agent.select_action(state)
        next_state, reward, done = environment.step(action)
        agent.update_q_value(state, action, reward, next_state)
        if done:
            break
        state = next_state