在运行最后一句trainingStats = train(agent, env, trainOpts)​;之前没报错，DDP​GAgent也创建出​来了

clear all

clc

addpath('matpower6.0');
define_constants;
% define named indices into bus, gen, branch matrices
[PQ, PV, REF, NONE, BUS_I, BUS_TYPE, PD, QD, GS, BS, BUS_AREA, VM, ...
VA, BASE_KV, ZONE, VMAX, VMIN, LAM_P, LAM_Q, MU_VMAX, MU_VMIN] = idx_bus;
[F_BUS, T_BUS, BR_R, BR_X, BR_B, RATE_A, RATE_B, RATE_C, ...
TAP, SHIFT, BR_STATUS, PF, QF, PT, QT, MU_SF, MU_ST, ...
ANGMIN, ANGMAX, MU_ANGMIN, MU_ANGMAX] = idx_brch;
[GEN_BUS, PG, QG, QMAX, QMIN, VG, MBASE, GEN_STATUS, PMAX, PMIN, ...
MU_PMAX, MU_PMIN, MU_QMAX, MU_QMIN, PC1, PC2, QC1MIN, QC1MAX, ...
QC2MIN, QC2MAX, RAMP_AGC, RAMP_10, RAMP_30, RAMP_Q, APF] = idx_gen;

%% 创建环境
env = PowerSystemEnv;

% 查看环境的状态和动作规范
obsInfo = getObservationInfo(env);
actInfo = getActionInfo(env);

% 固定随机种子确保实验的可重复性
rng(0)

%% 创建神经网络
% 创建观察路径
obsPath = [
    featureInputLayer(obsInfo.Dimension(1), 'Normalization', 'none', 'Name', 'state')
    fullyConnectedLayer(200, 'Name', 'fc1')
    reluLayer('Name', 'relu1')
    fullyConnectedLayer(100, 'Name', 'fc2')
    reluLayer('Name', 'relu2')];

% 创建动作路径
actPath = [
    featureInputLayer(actInfo.Dimension(1), 'Normalization', 'none', 'Name', 'action')
    fullyConnectedLayer(100, 'Name', 'fc3')
    reluLayer('Name', 'relu3')];

% 创建公共路径
commonPath = [
    concatenationLayer(1, 2, 'Name', 'concat')
    fullyConnectedLayer(50, 'Name', 'fc4')
    reluLayer('Name', 'relu4')
    fullyConnectedLayer(1, 'Name', 'output')];

% 创建并连接网络
net = layerGraph(obsPath);
net = addLayers(net, actPath);
net = addLayers(net, commonPath);
net = connectLayers(net, 'relu2', 'concat/in1');
net = connectLayers(net, 'relu3', 'concat/in2');

% 将神经网络转换为dlnetwork
net = dlnetwork(net);

% 使用神经网络创建连续动作空间的价值函数
critic = rlQValueFunction(net, obsInfo, actInfo, ...
    'ObservationInputNames', 'state', 'ActionInputNames', 'action');

%% 创建Actor网络
layers = [
    featureInputLayer(obsInfo.Dimension(1), 'Normalization', 'none', 'Name', 'state')
    fullyConnectedLayer(200, 'Name', 'fc1')
    reluLayer('Name', 'relu1')
    fullyConnectedLayer(100, 'Name', 'fc2')
    reluLayer('Name', 'relu2')
    fullyConnectedLayer(actInfo.Dimension(1), 'Name', 'output')
    tanhLayer('Name', 'tanh')];
net = dlnetwork(layers);
actor = rlContinuousDeterministicActor(net, obsInfo, actInfo);

%% 设置优化选项
criticOpts = rlOptimizerOptions('LearnRate', 1e-3, 'GradientThreshold', 1);
actorOpts = rlOptimizerOptions('LearnRate', 1e-4, 'GradientThreshold', 1);

%% 设置DDPG超参数
agentOpts = rlDDPGAgentOptions( ...
    'SampleTime', 0.1, ...
    'CriticOptimizerOptions', criticOpts, ...
    'ActorOptimizerOptions', actorOpts, ...
    'ExperienceBufferLength', 1e4, ...
    'MiniBatchSize', 256, ...
    'DiscountFactor', 0.99, ...
    'TargetSmoothFactor', 1e-3, ...
    'TargetUpdateFrequency', 1 );

% 创建DDPG智能体
agent = rlDDPGAgent(actor, critic, agentOpts);

%% 训练智能体
trainOpts = rlTrainingOptions( ...
    'MaxEpisodes', 2000, ...
    'MaxStepsPerEpisode', 5000, ...
    'StopTrainingCriteria', 'EpisodeReward', ...
    'StopTrainingValue', 40, ...
    'SaveAgentCriteria', 'EpisodeReward', ...
    'SaveAgentValue', 40, ...
    'Verbose', false, ...
    'Plots', 'training-progress');

% 开始训练智能体
trainingStats = train(agent, env, trainOpts);


% 运行后报错
错误使用 rl.train.SeriesTrainer/run
There was an error executing the ProcessExperienceFcn.

Caused by:
	错误使用 rl.policy.rlAdditiveNoisePolicy/getAction_
	Batch observations not supported for Ornstein-Uhlenbeck noise model.

	出错 rl.policy.PolicyInterface/getAction (第 36 行)
	            [action,this] = getAction_(this,observation);

	出错 rl.agent.AbstractOffPolicyAgent/getExplorationAction_ (第 116 行)
	            [action,this.ExplorationPolicy_] = getAction(this.ExplorationPolicy_,...

	出错 rl.agent.AbstractAgent/getAction_ (第 90 行)
	                [action,this] = getExplorationAction_(this,observation);

	出错 rl.policy.PolicyInterface/getAction (第 36 行)
	            [action,this] = getAction_(this,observation);

	出错 rl.env.internal.PolicyExperienceProcessorInterface/evaluateAction_ (第 32 行)
	            [action,this.Policy_] = getAction(this.Policy_,observation);

	出错 rl.env.internal.ExperienceProcessorInterface/evaluateAction (第 62 行)
	                action = evaluateAction_(this,observation);

	出错 rl.env.internal.MATLABSimulator/simInternal_ (第 109 行)
	                    act = evaluateAction(expProcessor,obs);

	出错 rl.env.internal.MATLABSimulator/sim_ (第 67 行)
	                out = simInternal_(this,simPkg);

	出错 rl.env.internal.AbstractSimulator/sim (第 30 行)
	            out = sim_(this,simData,policy,processExpFcn,processExpData);

	出错 rl.env.AbstractEnv/runEpisode (第 144 行)
	    out = sim(simulator,simData,policy,processExpFcn,processExpData);

	出错 rl.train.SeriesTrainer/run (第 64 行)
	                out = runEpisode(...

	出错 rl.train.TrainingManager/train (第 516 行)
	            run(trainer);

	出错 rl.train.TrainingManager/run (第 253 行)
	            train(this);

	出错 rl.agent.AbstractAgent/train (第 187 行)
	trainingResult = run(trainMgr,checkpoint);

	出错 main (第 104 行)
	trainingStats = train(agent, env, trainOpts);

出错 rl.train.TrainingManager/train (第 516 行)
            run(trainer);

出错 rl.train.TrainingManager/run (第 253 行)
            train(this);

出错 rl.agent.AbstractAgent/train (第 187 行)
trainingResult = run(trainMgr,checkpoint);

出错 main (第 104 行)
trainingStats = train(agent, env, trainOpts);
 
 open 'run Run script.
      ↑
错误: 字符向量未正常终止。