用于MATLAB的DDPG代码整定PID参数

DDPG（Deep Deterministic Policy Gradient）是一种深度强化学习算法，可以用于连续控制问题的解决。在MATLAB中，可以使用Deep Learning Toolbox中的函数以及Reinforcement Learning Toolbox中的函数来实现DDPG算法。

下面是一个使用DDPG算法来优化PID控制器参数的示例代码：

%% 初始化环境
mdl = 'pendulum';
open_system(mdl);

Ts = 0.05;
Tf = 10;

obsInfo = rlNumericSpec([3 1],'LowerLimit',[-pi/2; -8; -Inf],'UpperLimit',[pi/2; 8; Inf]);
obsInfo.Name = 'observations';
obsInfo.Description = 'theta;thetadot;thetaerror';

actInfo = rlNumericSpec([1 1],'LowerLimit',-10,'UpperLimit',10);
actInfo.Name = 'torque';
env = rlSimulinkEnv(mdl,mdl,obsInfo,actInfo);

%% 确定深度神经网络的结构
statePath = [
    imageInputLayer([3 1 1],'Normalization','none','Name','observation')
    fullyConnectedLayer(64,'Name','CriticStateFC1')
    reluLayer('Name','CriticRelu1')
    fullyConnectedLayer(64,'Name','CriticStateFC2')];
actionPath = [
    imageInputLayer([1 1 1],'Normalization','none','Name','action')
    fullyConnectedLayer(64,'Name','CriticActionFC1','BiasLearnRateFactor',0)];
commonPath = [
    additionLayer(2,'Name','add')
    reluLayer('Name','CriticCommonRelu')
    fullyConnectedLayer(1,'Name','output')];
criticNetwork = layerGraph(statePath);
criticNetwork = addLayers(criticNetwork,actionPath);
criticNetwork = addLayers(criticNetwork,commonPath);
criticNetwork = connectLayers(criticNetwork,'CriticStateFC2','add/in1');
criticNetwork = connectLayers(criticNetwork,'CriticActionFC1','add/in2');

%% 建立深度决策网络
actorNetwork = [
    imageInputLayer([3 1 1],'Normalization','none','Name','observation')
    fullyConnectedLayer(64,'Name','ActorFC1')
    reluLayer('Name','ActorRelu1')
    fullyConnectedLayer(64,'Name','ActorFC2')
    reluLayer('Name','ActorRelu2')
    fullyConnectedLayer(1,'Name','ActorFC3')
    tanhLayer('Name','ActorTanh1')
    scalingLayer('Name','ActorScaling1','Scale',2)];

%% 设置DDPG代理
agentOpts = rlDDPGAgentOptions;
agentOpts.SampleTime = Ts;
agentOpts.DiscountFactor = 0.99;
agentOpts.MiniBatchSize = 256;
agentOpts.ExperienceBufferLength = 1e6;
agentOpts.TargetSmoothFactor = 1e-3;
agentOpts.NoiseOptions.Variance = 0.2;
agentOpts.NoiseOptions.VarianceDecayRate = 1e-5;
agentOpts.SaveExperienceBufferWithAgent = true;
agentOpts.ResetExperienceBufferBeforeTraining = false;
agentOpts.UseParallel = false;
agentOpts.UseGPU = false;

agent = rlDDPGAgent(actorNetwork,criticNetwork,agentOpts);

%% 训练代理
trainOpts = rlTrainingOptions;
trainOpts.MaxEpisodes = 100;
trainOpts.MaxStepsPerEpisode = ceil(Tf/Ts);
trainOpts.StopTrainingCriteria = 'AverageReward';
trainOpts.StopTrainingValue = -400;
trainOpts.ScoreAveragingWindowLength = 30;
trainOpts.SaveAgentCriteria = 'EpisodeReward';
trainOpts.SaveAgentValue = -350;
trainOpts.Plots = 'training-progress';

trainingStats = train(agent,env,trainOpts);

在这个示例中，我们使用了一个具有64个神经元的深度神经网络来近似Q函数，另一个具有64个神经元的深度神经网络来确定actor的策略。我们还设置了一些DDPG代理的选项，如样本时间、折扣因子、噪声参数等。

在训练代理之后，我们可以使用以下代码来测试代理的性能，并使用优化的PID参数控制摆杆系统：

agentOpts.NoiseOptions.Variance = 0;
simOpts = rlSimulationOptions('MaxSteps',1000);
experience = sim(env,agent,simOpts);

这段代码将关闭DDPG代理的噪声，并使用优化的PID参数控制摆杆系统。