请帮我写一个q_learning寻找一阶惯性环节的系数k的最优区间的代码

以下是一个简单的q_learning寻找一阶惯性环节系数k的最优区间的代码示例：

import numpy as np

# 定义状态空间和动作空间
states = [i for i in range(1, 11)]
actions = [i for i in range(1, 6)]

# 初始化Q值表
Q = np.zeros((len(states), len(actions)))

# 定义参数
alpha = 0.1
gamma = 0.9
epsilon = 0.1
num_episodes = 1000

# 定义环节模型
def process(s, a):
    k = 0.5  # 初始值
    y = 0
    for i in range(1, 101):
        y = k * y + (1 - k) * np.random.normal(0, 1)
        if i == s:
            y += a
    return y

# 定义epsilon-greedy策略
def epsilon_greedy_policy(state):
    if np.random.uniform(0, 1) < epsilon:
        action = np.random.choice(actions)
    else:
        action = np.argmax(Q[state-1])
    return action

# 进行q_learning算法
for i in range(num_episodes):
    state = np.random.choice(states)
    action = epsilon_greedy_policy(state)
    while True:
        reward = process(state, action)
        next_state = state + 1  # 一阶惯性环节的状态转移
        next_action = epsilon_greedy_policy(next_state)
        Q[state-1][action-1] += alpha * (reward + gamma * Q[next_state-1][next_action-1] - Q[state-1][action-1])
        state = next_state
        action = next_action
        if state == 10:  # 终止状态
            break

# 寻找最优区间
max_reward = -float('inf')
best_k = None
for k in np.arange(0.1, 1.0, 0.1):
    total_reward = 0
    for state in states:
        for action in actions:
            reward = process(state, action)
            total_reward += reward - k * action ** 2
    if total_reward > max_reward:
        max_reward = total_reward
        best_k = k

print("最优区间为：[{}, {}]".format(best_k-0.1, best_k+0.1))

该代码首先定义了状态空间和动作空间，然后初始化Q值表。接着定义了一阶惯性环节模型和epsilon-greedy策略，并进行了q_learning算法的训练。最后，通过遍历一定区间内的系数k值，计算总奖励并选取最大值作为最优区间的依据。