无人机强化学习路径规划matlab代码

以下是一个简单的基于Q-learning强化学习算法的无人机路径规划的 MATLAB 代码示例：

% 初始化状态空间、动作空间和奖励函数
stateSpace = [1 2 3 4 5];    % 状态空间
actionSpace = [-1 1];        % 动作空间
rewardMatrix = [0 -1 -1 -1 -1;
                -1 0 -1 -1 -1;
                -1 -1 0 -1 10;
                -1 -1 -1 0 -1;
                -1 -1 -1 -1 0]; % 奖励函数矩阵

% 定义超参数和回放缓冲
alpha = 0.8;    % 学习率
gamma = 0.95;   % 折扣因子
epsilon = 0.1;  % ε-贪心策略
maxEpochs = 1000;   % 最大迭代轮数
bufferSize = 1000;  % 回放缓冲区大小
batchSize = 32;     % 批量大小
replayBuffer = nan(bufferSize, 4);  % 回放缓冲区

% 初始化Q值矩阵
qMatrix = randn(5, 2);

% 遍历训练数据
for epoch = 1:maxEpochs
    % 初始化状态
    currentState = 1;
    % ε-贪心策略选择动作
    if rand < epsilon
        currentAction = randi(length(actionSpace));
    else
        [~, currentAction] = max(qMatrix(currentState, :));
    end
    % 执行动作并获得奖励
    reward = rewardMatrix(currentState, currentAction);
    % 转移到下一个状态
    nextState = currentState + actionSpace(currentAction);
    % 更新Q值矩阵
    qMatrix(currentState, currentAction) = qMatrix(currentState, currentAction) + alpha * (reward + gamma * max(qMatrix(nextState, :)) - qMatrix(currentState, currentAction));
    % 将数据存储到回放缓冲区
    replayBuffer(epoch, :) = [currentState, currentAction, reward, nextState];
    % 随机从回放缓冲区中抽取数据进行训练
    batch = replayBuffer(randi(size(replayBuffer, 1), batchSize, 1), :);
    % 更新Q值矩阵
    for i = 1:batchSize
        currentState = batch(i, 1);
        currentAction = batch(i, 2);
        reward = batch(i, 3);
        nextState = batch(i, 4);
        qMatrix(currentState, currentAction) = qMatrix(currentState, currentAction) + alpha * (reward + gamma * max(qMatrix(nextState, :)) - qMatrix(currentState, currentAction));
    end
end

% 使用训练好的模型规划路径
startState = 1;
endState = 5;
currentState = startState;
path = currentState;
while currentState ~= endState
    [~, currentAction] = max(qMatrix(currentState, :));
    nextState = currentState + actionSpace(currentAction);
    currentState = nextState;
    path(end+1) = currentState;
end

% 输出路径
disp(path);

需要注意的是，这只是一个简单的示例代码，实际应用需要根据具体场景进行修改和优化。