wsn下固定终点的 q-learning 路由 matlab代码
时间: 2023-09-16 09:06:30 浏览: 157
以下是一个简单的WSN下固定终点的Q-learning路由的MATLAB代码示例:
```matlab
% 初始化参数
num_states = 100; % 状态数
num_actions = 10; % 动作数
alpha = 0.1; % 学习率
gamma = 0.9; % 折现率
epsilon = 0.1; % 探索率
num_episodes = 1000; % 训练次数
% 初始化Q矩阵
Q = zeros(num_states, num_actions);
% 定义起点和终点
start_state = 1;
end_state = 100;
% 定义邻接矩阵
adj_matrix = zeros(num_states, num_states);
% 设置邻接矩阵(这里只是一个示例)
for i = 1:num_states
for j = 1:num_states
if (i == j + 1) || (i == j - 1) || (i == j + 10) || (i == j - 10)
adj_matrix(i,j) = 1;
end
end
end
% 训练Q矩阵
for episode = 1:num_episodes
% 重置起点
curr_state = start_state;
% 当未到达终点时
while curr_state ~= end_state
% 探索或利用
if rand < epsilon
action = randi(num_actions);
else
[~, action] = max(Q(curr_state,:));
end
% 执行动作
next_state = find(adj_matrix(curr_state,:) == 1);
next_state = next_state(action);
% 更新Q矩阵
reward = -1;
Q(curr_state,action) = Q(curr_state,action) + alpha * (reward + gamma * max(Q(next_state,:)) - Q(curr_state,action));
% 更新状态
curr_state = next_state;
end
end
% 测试Q矩阵
curr_state = start_state;
route = [curr_state];
while curr_state ~= end_state
[~, action] = max(Q(curr_state,:));
next_state = find(adj_matrix(curr_state,:) == 1);
next_state = next_state(action);
curr_state = next_state;
route = [route curr_state];
end
% 输出路径
disp(route);
```
这个代码只是一个简单的示例,可以根据实际需求进行修改和扩展。
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