td3算法的matlab代码

TD3算法是一种强化学习算法，主要用于解决连续控制问题，它在深度强化学习中具有很高的实用性。下面是一个简单的MATLAB实现：

% TD3算法实现
% 请注意，这是一个简单的代码示例，可能需要根据实际情况进行修改

% 环境初始化
env = rlPredefinedEnv('Pendulum-Continuous');
obsInfo = getObservationInfo(env);
actInfo = getActionInfo(env);

% 神经网络参数初始化
actorNetwork = [
    imageInputLayer([obsInfo.Dimension(1) 1 1],'Normalization','none','Name','observation')
    fullyConnectedLayer(256,'Name','fc1')
    reluLayer('Name','relu1')
    fullyConnectedLayer(256,'Name','fc2')
    reluLayer('Name','relu2')
    fullyConnectedLayer(actInfo.Dimension(1),'Name','actorOutput')
    tanhLayer('Name','actorTanh')];
criticNetwork = [
    imageInputLayer([obsInfo.Dimension(1) 1 1],'Normalization','none','Name','observation')
    fullyConnectedLayer(256,'Name','fc1')
    reluLayer('Name','relu1')
    fullyConnectedLayer(256,'Name','fc2')
    reluLayer('Name','relu2')
    fullyConnectedLayer(1,'Name','criticOutput')];
actorOpts = rlRepresentationOptions('LearnRate',1e-3,'GradientThreshold',1);
criticOpts = rlRepresentationOptions('LearnRate',1e-3,'GradientThreshold',1);
actor = rlDeterministicActorRepresentation(actorNetwork,obsInfo,actInfo,'Observation',{'observation'},'Action',{'actorTanh'},actorOpts);
critic = rlValueRepresentation(criticNetwork,obsInfo,criticOpts,'Observation',{'observation'});

% TD3算法参数初始化
agentOpts = rlTD3AgentOptions;
agentOpts.SampleTime = 0.01;
agentOpts.DiscountFactor = 0.99;
agentOpts.ExperienceBufferLength = 1e6;
agentOpts.TargetSmoothFactor = 5e-3;
agentOpts.NoiseOptions.Variance = 0.2;
agentOpts.NoiseOptions.VarianceDecayRate = 1e-5;
agentOpts.NoiseOptions.StepSize = 0.01;

% 创建TD3代理
agent = rlTD3Agent(actor,critic,agentOpts);

% 训练代理
trainOpts = rlTrainingOptions;
trainOpts.MaxEpisodes = 500;
trainOpts.MaxStepsPerEpisode = ceil(env.Ts/env.StepSize);
trainOpts.ScoreAveragingWindowLength = 10;
trainOpts.StopTrainingCriteria = 'AverageReward';
trainOpts.StopTrainingValue = -100;
trainOpts.SaveAgentCriteria = 'EpisodeReward';
trainOpts.SaveAgentValue = -100;
trainOpts.Plots = 'training-progress';
trainOpts.Verbose = false;

% 训练代理
trainingStats = train(agent,env,trainOpts);

% 测试代理
simOptions.ResetFcn = @(in) setVariable(env,in,env.ResetFcn());
simOptions.StopTime = 20;
experience = sim(env,agent,simOptions);