基于近端策略优化(PPO)强化学习的四足机器人步态控制系统的研究

% 加载模型model = MjModel('path_to_your_model.xml');data = MjData(model);
% 初始化可视化viz = MjViz(model, data);

% 定义策略网络actor = [    featureInputLayer(numStateDimensions, 'Name', 'state')    fullyConnectedLayer(64, 'Name', 'fc1')    reluLayer('Name', 'relu1')    fullyConnectedLayer(numActionDimensions, 'Name', 'fc2')    softmaxLayer('Name', 'softmax')];
% 定义价值网络critic = [    featureInputLayer(numStateDimensions, 'Name', 'state')    fullyConnectedLayer(64, 'Name', 'fc1')    reluLayer('Name', 'relu1')    fullyConnectedLayer(1, 'Name', 'value')];
% 设置优化器options = trainingOptions('adam', 'InitialLearnRate', 0.001, 'MaxEpochs', 100);actorNet = trainNetwork(actor, [], options);criticNet = trainNetwork(critic, [], options);

% 参数设置gamma = 0.99; % 折扣因子lambda = 0.95; % GAE衰减因子epsilon = 0.2; % 剪裁参数c1 = 0.5; % 价值函数损失权重c2 = 0.01; % 熵正则项权重batchSize = 64;numEpisodes = 1000;
% 训练循环for episode = 1:numEpisodes    % 重置环境    resetEnv(model, data);        % 初始化存储    states = [];    actions = [];    rewards = [];    values = [];    logProbs = [];    dones = [];        % 采集轨迹    while ~isDone(model, data)        state = getState(model, data);        [action, logProb] = getAction(state, actorNet);        value = getValue(state, criticNet);                % 执行动作        reward = stepEnv(model, data, action);                % 存储数据        states = [states; state];        actions = [actions; action];        rewards = [rewards; reward];        values = [values; value];        logProbs = [logProbs; logProb];        dones = [dones; isDone(model, data)];    end        % 计算GAE和目标值    nextValue = 0;    advantages = zeros(size(rewards));    returns = zeros(size(rewards));    for t = length(rewards):-1:1        delta = rewards(t) + gamma * nextValue * (1 - dones(t)) - values(t);        advantages(t) = delta + gamma * lambda * (1 - dones(t)) * nextValue;        returns(t) = advantages(t) + values(t);        nextValue = advantages(t);    end        % 更新策略和价值网络    for epoch = 1:4        idx = randperm(length(states), batchSize);        batchStates = states(idx, :);        batchActions = actions(idx, :);        batchAdvantages = advantages(idx, :);        batchReturns = returns(idx, :);        batchLogProbs = logProbs(idx, :);                % 计算旧策略的概率        oldLogProbs = getLogProb(batchStates, batchActions, actorNet);                % 计算新策略的概率        [newLogProbs, entropy] = getLogProb(batchStates, batchActions, actorNet, true);                % 计算比率        ratios = exp(newLogProbs - oldLogProbs);                % 计算剪裁后的目标        surr1 = ratios .* batchAdvantages;        surr2 = min(ratios, 1 + epsilon) .* batchAdvantages;        policyLoss = -mean(min(surr1, surr2));                % 价值函数损失        valueLoss = mean((getValue(batchStates, criticNet) - batchReturns).^2);                % 总损失        totalLoss = policyLoss + c1 * valueLoss - c2 * mean(entropy);                % 更新网络        updateNetwork(actorNet, totalLoss);        updateNetwork(criticNet, totalLoss);    end        % 打印训练信息    fprintf('Episode: %d, Total Reward: %.2f\n', episode, sum(rewards));end