Q-learing算法实现自适应调制，其中不同信噪比作为状态，不同调制方式作为动作，系统吞吐量作为奖励python代码

这里提供一个简单的 Q-learning 算法实现自适应调制的 Python 代码，其中使用不同信噪比作为状态，不同调制方式作为动作，系统吞吐量作为奖励。

import numpy as np

# 定义状态、动作、奖励以及 Q-table
states = [(0, 10), (5, 10), (10, 10)]  # 不同信噪比
actions = ['BPSK', 'QPSK', '8PSK']  # 不同调制方式
rewards = np.zeros((3, 3))  # 奖励
q_table = np.zeros((3, 3))

# 定义参数
alpha = 0.1  # 学习率
gamma = 0.9  # 折扣因子
epsilon = 0.1  # 探索率

# 定义状态转移函数
def get_next_state(current_state, action):
    snr_index = states.index(current_state)
    action_index = actions.index(action)
    snr = current_state[0]
    next_snr = np.random.choice([snr-5, snr, snr+5])
    if next_snr < 0:
        next_snr = 0
    if next_snr > 10:
        next_snr = 10
    next_snr_index = states.index((next_snr, 10))
    return next_snr_index, rewards[snr_index, action_index]

# 训练 Q-table
for i in range(10000):
    current_state_index = np.random.randint(0, 3)
    current_state = states[current_state_index]
    if np.random.uniform() < epsilon:
        action_index = np.random.randint(0, 3)
    else:
        action_index = np.argmax(q_table[current_state_index])
    action = actions[action_index]
    next_state_index, reward = get_next_state(current_state, action)
    q_table[current_state_index, action_index] += alpha * (reward + gamma * np.max(q_table[next_state_index]) - q_table[current_state_index, action_index])
    
# 测试 Q-table
total_throughput = 0
for snr in range(0, 11, 1):
    state_index = states.index((snr, 10))
    action_index = np.argmax(q_table[state_index])
    action = actions[action_index]
    # 计算系统吞吐量
    if action == 'BPSK':
        throughput = 1/2 * np.log2(1 + snr)
    elif action == 'QPSK':
        throughput = 2/3 * np.log2(1 + 2 * snr)
    else:
        throughput = 3/4 * np.log2(1 + 3 * snr)
    total_throughput += throughput
print(total_throughput)

注：上述代码中奖励矩阵 rewards 需要根据具体场景进行定义。本示例中为简化问题，将奖励矩阵设为全零。