mdl = 'Buck_Boost'; open_system(mdl) obsInfo=rlNumericSpec([2 1]); obsInfo.Name='observations'; actInfo=rlNumericSpec([1 1],'LowerLimit',0,'UpperLimit',1); actInfo.Name='action'; blk=[mdl,'/RL Agent']; env=rlSimulinkEnv(mdl,blk,obsInfo,actInfo); %env.ResetFcn = @(in)localResetFcn(in); Ts=0.001; Tf=0.05; rng(0) %Create Critic % Observation path obsPath = [ featureInputLayer(obsInfo.Dimension(1),Name="obsInputLayer") fullyConnectedLayer(50) reluLayer fullyConnectedLayer(25,Name="obsPathOutLayer")]; % Action path actPath = [ featureInputLayer(actInfo.Dimension(1),Name="actInputLayer") fullyConnectedLayer(25,Name="actPathOutLayer")]; % Common path commonPath = [ additionLayer(2,Name="add") reluLayer fullyConnectedLayer(1,Name="CriticOutput")]; criticNetwork = layerGraph(); criticNetwork = addLayers(criticNetwork,obsPath); criticNetwork = addLayers(criticNetwork,actPath); criticNetwork = addLayers(criticNetwork,commonPath); criticNetwork = connectLayers(criticNetwork, ... "obsPathOutLayer","add/in1"); criticNetwork = connectLayers(criticNetwork, ... "actPathOutLayer","add/in2"); %View the critic network configuration. %figure %plot(criticNetwork) %Convert the network to a dlnetwork object and summarize its properties. criticNetwork = dlnetwork(criticNetwork); critic = rlQValueFunction(criticNetwork, ... obsInfo,actInfo, ... ObservationInputNames="obsInputLayer", ... ActionInputNames="actInputLayer"); %getValue(critic, ... %{rand(obsInfo.Dimension)}, ... %{rand(actInfo.Dimension)}) %Create Actor actorNetwork = [ featureInputLayer(obsInfo.Dimension(1)) fullyConnectedLayer(3) tanhLayer fullyConnectedLayer(actInfo.Dimension(1)) ]; actorNetwork = dlnetwork(actorNetwork); actor = rlContinuousDeterministicActor(actorNetwork,obsInfo,actInfo); %getAction(actor,{rand(obsInfo.Dimension)}) %Create DDPG Agent agent = rlDDPGAgent(actor,critic); agent.SampleTime = Ts; agent.AgentOptions.TargetSmoothFactor = 1e-3; agent.AgentOptions.DiscountFactor = 1.0; agent.AgentOptions.MiniBatchSize = 64; agent.AgentOptions.ExperienceBufferLength = 1e6; agent.AgentOptions.NoiseOptions.Variance = 0.3; agent.AgentOptions.NoiseOptions.VarianceDecayRate = 1e-5; agent.AgentOptions.CriticOptimizerOptions.LearnRate = 1e-03; agent.AgentOptions.CriticOptimizerOptions.GradientThreshold = 1; agent.AgentOptions.ActorOptimizerOptions.LearnRate = 1e-04; agent.AgentOptions.ActorOptimizerOptions.GradientThreshold = 1; %Train Agent trainOpts = rlTrainingOptions(... MaxEpisodes=500, ... MaxStepsPerEpisode=ceil(Tf/Ts), ... ScoreAveragingWindowLength=20, ... Verbose=false, ... Plots="training-progress",... StopTrainingCriteria="AverageReward",... StopTrainingValue=2000); doTraining =true; %doTraining =false; rng(1) if doTraining % Train the agent. trainingStats = train(agent,env,trainOpts); else load agent.mat % Load the pretrained agent for the example. end %simOpts = rlSimulationOptions(MaxSteps=ceil(Tf/Ts),StopOnError="on"); %experiences = sim(env,agent,simOpts);帮我检查这个matlab中构建并训练DDPG智能体的程序是否有问题，并加以改进

这段代码看起来没有明显的错误，但是可能存在一些可以改进的地方。以下是一些建议：

1. 在创建 actor 和 critic 时，可以尝试使用更复杂的神经网络结构，例如增加更多的隐藏层或使用卷积层等。

2. 在训练智能体时，可以尝试调整训练参数，例如增加训练次数、调整批大小或学习速率等。

3. 可以尝试使用更复杂的环境，例如添加噪声或随机性，以提高智能体的鲁棒性和泛化能力。

4. 最后，建议在训练智能体之前先对环境进行一些基本的测试和调试，以确保代码能够正确运行并产生有意义的结果。

相关问题

mdl = mdl.strip()

根据提供的引用内容，`mdl = mdl.strip()`是一行代码，它的作用是去除字符串`mdl`两端的空格和换行符。这个操作可以用于清理字符串中的不必要的空白字符，以便后续处理。

范例：

mdl = "  Hello World!  "
mdl = mdl.strip()
print(mdl)  # 输出：Hello World!

for i = 1:ncomp rslt(i).ncomp = i; cal_p = squeeze(cal_preds(i, :)); cal_p = mode(cal_p,1); cal_t = squeeze(cal_trues(i, :)); cal_t = mode(cal_t,1); [cal_confus, rslt_confusionOrder] = confusionmat(cal_t, cal_p); cal_rslt = statsOfMeasure(cal_confus, 0); rslt(i).cal_confus = cal_confus; rslt(i).cal_rslt = cal_rslt; rslt(i).cal_acc = cal_rslt.microAVG(end-1); rslt(i).cal_sen = cal_rslt.microAVG(end-3); rslt(i).cal_spe = cal_rslt.microAVG(end-2); rslt(i).cal_y_true = cal_t; rslt(i).cal_y_pred = cal_p; val_p = squeeze(val_preds(i, :)); val_t = squeeze(val_trues(i, :)); [val_confus, rslt_confusionOrder] = confusionmat(val_t, val_p); val_rslt = statsOfMeasure(val_confus, 0); rslt(i).val_confus = val_confus; rslt(i).val_rslt = val_rslt; rslt(i).val_acc = val_rslt.microAVG(end-1); rslt(i).val_sen = val_rslt.microAVG(end-3); rslt(i).val_spe = val_rslt.microAVG(end-2); rslt(i).val_y_true = val_t; rslt(i).val_y_pred = val_p; mdl = plsda(x_pp, y, i, opts0); trainedModel{i} = mdl; mdl = plsda(x_test_pp,[],i,mdl, opts0); rslt(i).probability = mdl.classification.probability; y_test_pred = mdl.classification.mostprobable; [test_confus, rslt_confusionOrder] = confusionmat(y_test, y_test_pred); test_rslt = statsOfMeasure(test_confus, 0); rslt(i).test_confus = test_confus; rslt(i).test_rslt = test_rslt; rslt(i).test_acc = test_rslt.microAVG(end-1); rslt(i).test_sen = test_rslt.microAVG(end-3); rslt(i).test_spe = test_rslt.microAVG(end-2); rslt(i).test_y_true = y_test; rslt(i).test_y_pred = y_test_pred; 什么意思

这是一段MATLAB代码，其中包含了一些计算分类模型性能指标的代码，包括计算训练集、验证集和测试集的混淆矩阵、精度、敏感度和特异度等指标，并且包含了使用偏最小二乘判别分析（PLSDA）构建分类模型的代码。具体来说，对于每个组件（i=1:ncomp），代码计算训练集、验证集和测试集的分类性能指标，并将结果存储在一个结构体数组（rslt）中。同时，代码使用PLSDA构建分类模型，并将训练集、测试集的真实标签和预测标签也存储在结构体数组中。