matlb DDPG 混合动力汽车能量管理策略代码 有车速

以下是一个基于DDPG算法的混合动力汽车能量管理策略的Matlab代码示例，其中考虑了车速作为状态的一部分：

% 状态空间维度
state_dim = 3;  % 车速、电池SOC、剩余距离

% 动作空间维度
action_dim = 1; % 发动机功率

% 初始化神经网络参数
hidden_size = 10;
actor_lr = 0.001;
critic_lr = 0.001;

% 初始化经验回放缓冲区
buffer_size = 10000;
buffer = zeros(buffer_size, state_dim + action_dim + 1);

% 设置训练参数
batch_size = 32;
gamma = 0.99;

% 初始化Actor和Critic神经网络
actor_net = feedforwardnet(hidden_size);
actor_net.layers{end}.transferFcn = 'tansig';
actor_net.outputs{end}.transferFcn = 'purelin';
actor_net.initFcn = 'initlay';
actor_net = init(actor_net);

critic_net = feedforwardnet(hidden_size);
critic_net.layers{end}.transferFcn = 'tansig';
critic_net.outputs{end}.transferFcn = 'purelin';
critic_net.initFcn = 'initlay';
critic_net = init(critic_net);

% 开始训练
for episode = 1:num_episodes
    % 初始化环境并观察初始状态
    state = env.reset();
    done = false;
    total_reward = 0;

    while ~done
        % 使用Actor神经网络生成动作
        action = actor_net(state);

        % 在环境中执行动作并观察下一个状态和奖励
        [next_state, reward, done] = env.step(action);

        % 存储经验到回放缓冲区
        buffer = store_experience(buffer, state, action, reward, next_state);

        % 从回放缓冲区中随机采样一个批次的经验
        batch = sample_batch(buffer, batch_size);

        % 更新Critic网络
        critic_loss = update_critic(critic_net, batch, gamma);

        % 更新Actor网络
        actor_loss = update_actor(actor_net, critic_net, batch);

        % 更新目标网络（可选）
        % target_update(actor_target_net, actor_net);
        % target_update(critic_target_net, critic_net);

        % 更新状态和累计奖励
        state = next_state;
        total_reward = total_reward + reward;
    end

    % 打印每个回合的累计奖励
    disp(['Episode: ', num2str(episode), ', Total Reward: ', num2str(total_reward)]);
end


function buffer = store_experience(buffer, state, action, reward, next_state)
    % 存储经验到回放缓冲区
    buffer(end+1, :) = [state, action, reward, next_state];
end


function batch = sample_batch(buffer, batch_size)
    % 从回放缓冲区中随机采样一个批次的经验
    buffer_size = size(buffer, 1);
    indices = randperm(buffer_size, batch_size);
    batch = buffer(indices, :);
end


function critic_loss = update_critic(critic_net, batch, gamma)
    % 更新Critic网络
    states = batch(:, 1:3);
    actions = batch(:, 4);
    rewards = batch(:, 5);
    next_states = batch(:, 6:8);

    next_actions = actor_net(next_states);
    next_qvals = critic_net([next_states, next_actions]);

    targets = rewards + gamma * next_qvals;
    critic_loss = train(critic_net, [states, actions], targets);
end


function actor_loss = update_actor(actor_net, critic_net, batch)
    % 更新Actor网络
    states = batch(:, 1:3);
    actions = actor_net(states);
    actor_loss = -mean(critic_net([states, actions]));
    train(actor_net, states, actor_loss);
end

请注意，这只是一个简单的混合动力汽车能量管理策略的示例代码，具体的实现可能会因任务的不同而有所变化。你可能需要根据你的任务对代码进行适当的修改和调整。

希望这个示例对你有帮助！如果你有任何问题，请随时提问。