ddpg算法matlab代码

### DDPG算法的MATLAB实现 DDPG（Deep Deterministic Policy Gradient）是一种用于解决连续动作空间强化学习问题的方法。下面是一个简化版的DDPG算法在MATLAB中的实现示例[^1]。 #### 初始化环境和参数设置 ```matlab % 定义超参数 stateSize = 3; % 状态维度大小 actionSize = 1; % 动作维度大小 actorLearningRate = 0.001; criticLearningRate = 0.002; discountFactor = 0.99; tau = 0.01; % 创建Actor网络 actorNetwork = [ featureInputLayer(stateSize, 'Normalization', 'none', 'Name', 'state') fullyConnectedLayer(400, 'Name', 'fc1') reluLayer('Name', 'relu1') fullyConnectedLayer(300, 'Name', 'fc2') reluLayer('Name', 'relu2') fullyConnectedLayer(actionSize, 'Name', 'fc3') tanhLayer('Name', 'tanh')]; % 创建Critic网络 criticNetwork = [ featureInputLayer(stateSize, 'Normalization', 'none', 'Name', 'state') fullyConnectedLayer(400, 'Name', 'fc1_state') reluLayer('Name', 'relu1_state') featureInputLayer(actionSize, 'Normalization', 'none', 'Name', 'action') fullyConnectedLayer(300, 'Name', 'fc_action') additionLayer(2,'Name','add') reluLayer('Name', 'relu_add') fullyConnectedLayer(1, 'Name', 'qValue')]; ``` #### 构建Agent对象 利用上述定义好的两个神经网络结构来创建`rlDDPGAgent`类型的代理实例，这一步骤会自动初始化目标网络以及配置所需的学习率和其他属性[^1]. #### 训练循环框架 ```matlab for episode = 1:numEpisodes observation = reset(env); % Reset environment at start of each episode while ~isDone(observation) action = getAction(agent,observation); [nextObservation,reward,isDone] = step(env,action); experience = Experience(observation,action,reward,nextObservation,isDone); addExperience(replayBuffer,experience); if length(replayBuffer)>batchSize miniBatch = sampleMiniBatch(replayBuffer,batchSize); trainOnMiniBatch(miniBatch); end observation = nextObservation; end end ``` 此代码片段展示了如何在一个典型的训练过程中迭代地与模拟器交互并更新策略。实际应用时还需要考虑更多细节如探索噪声、经验回放缓冲区管理等[^1].