wsn下固定终点的 q-learning 路由 matlab代码

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：

% 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。