PPO算法matlab

以下是PPO算法的MATLAB实现示例：

% 定义神经网络结构
obsInfo = rlNumericSpec([4 1],'LowerLimit',-inf*ones(4,1),'UpperLimit',inf*ones(4,1));
actInfo = rlFiniteSetSpec([-1 1]);
criticNetwork = [
    imageInputLayer([4 1 1],'Normalization','none','Name','observation')
    fullyConnectedLayer(24,'Name','CriticStateFC1')
    reluLayer('Name','CriticRelu1')
    fullyConnectedLayer(24,'Name','CriticStateFC2')
    reluLayer('Name','CriticCommonRelu')
    fullyConnectedLayer(1,'Name','output')];
actorNetwork = [
    imageInputLayer([4 1 1],'Normalization','none','Name','observation')
    fullyConnectedLayer(24,'Name','ActorStateFC1')
    reluLayer('Name','ActorRelu1')
    fullyConnectedLayer(24,'Name','ActorStateFC2')
    reluLayer('Name','ActorRelu2')
    softmaxLayer('Name','ActorSoftMax')
    rlRepresentation(actorNetwork,obsInfo,'Observation',{'ActorSoftMax'},actInfo)];

% 定义PPO代理
agentOpts = rlPPOAgentOptions(...
    'SampleTime',0.01,...
    'EntropyLossWeight',0.01,...
    'ExperienceHorizon',256,...
    'MiniBatchSize',64,...
    'ClipFactor',0.2,...
    'NumEpoch',10,...
    'AdvantageEstimateMethod','gae',...
    'GAEFactor',0.95,...
    'DiscountFactor',0.99,...
    'UseBaseline',true,...
    'BaselineMode','moving-average',...
    'BaselinePeriod',50,...
    'LearnRate',1e-4,...
    'GradientThreshold',1);

agent = rlPPOAgent(actorNetwork,criticNetwork,agentOpts);

% 训练代理
maxepisodes = 500;
maxsteps = ceil(10/agentOpts.SampleTime);
trainOpts = rlTrainingOptions(...
    'MaxEpisodes',maxepisodes,...
    'MaxStepsPerEpisode',maxsteps,...
    'ScoreAveragingWindowLength',20,...
    'Verbose',false,...
    'Plots','training-progress',...
    'StopTrainingCriteria','AverageReward',...
    'StopTrainingValue',200);

trainingStats = train(agent,env,trainOpts);

% 测试代理
simOpts = rlSimulationOptions('MaxSteps',500);
experience = sim(env,agent,simOpts);