How to fix the error in PPO rlwatertank : Model input sizes must match the dimensions specified in the corresponding observation and action info specifications.

open_system('rlwatertank')

obsInfo = rlNumericSpec([3 1],'LowerLimit',[-inf -inf 0 ]','UpperLimit',[ inf inf inf]');

obsInfo.Name = 'observations';

obsInfo.Description = 'integrated error, error, and measured height';

numObs = obsInfo.Dimension(1);

actInfo = rlNumericSpec([1 1],'LowerLimit',-10','UpperLimit',10);

actInfo.Name = 'Action';

numAct = actInfo.Dimension(1);

env = rlSimulinkEnv('rlwatertank','rlwatertank/RL Agent',...

obsInfo,actInfo);

env.ResetFcn = @(in)localResetFcn(in);

Ts = 1.0;

Tf = 200;

rng(0)

criticNet = [

imageInputLayer([obsInfo.Dimension 1],'Normalization','none','Name','observations')

fullyConnectedLayer(10,'Name', 'fc_in')

reluLayer('Name', 'relu')

fullyConnectedLayer(1,'Name','out')];

% set some training options for the critic

criticOpts = rlRepresentationOptions('LearnRate',8e-3,'GradientThreshold',1);

% create the critic representation from the network

critic = rlValueRepresentation(criticNet,obsInfo,'Observation',{'observations'},criticOpts);

%actor

inPath = [ imageInputLayer([6 1 1], 'Normalization','none','Name','observations')

fullyConnectedLayer(2,'Name','infc') ]; % 2 by 1 output

% path layers for mean value (2 by 1 input and 2 by 1 output)

% using scalingLayer to scale the range

meanPath = [ tanhLayer('Name','tanh'); % output range: (-1,1)

scalingLayer('Name','scale','Scale',actInfo.UpperLimit) ]; % output range: (-10,10)

% path layers for standard deviations (2 by 1 input and output)

% using softplus layer to make it non negative

sdevPath = softplusLayer('Name', 'splus');

% conctatenate two inputs (along dimension #3) to form a single (4 by 1) output layer

outLayer = concatenationLayer(3,2,'Name','mean&sdev');

% add layers to network object

net = layerGraph(inPath);

net = addLayers(net,meanPath);

net = addLayers(net,sdevPath);

net = addLayers(net,outLayer);

% connect layers: the mean value path output MUST be connected to the FIRST input of the concatenationLayer

net = connectLayers(net,'infc','tanh/in'); % connect output of inPath to meanPath input

net = connectLayers(net,'infc','splus/in'); % connect output of inPath to sdevPath input

net = connectLayers(net,'scale','mean&sdev/in1'); % connect output of meanPath to gaussPars input #1

net = connectLayers(net,'splus','mean&sdev/in2'); % connect output of sdevPath to gaussPars input #2

% plot network

plot(net)

actorOpts = rlRepresentationOptions('LearnRate',8e-3,'GradientThreshold',1);

% create the actor using the network

Actor = rlStochasticActorRepresentation(net,obsInfo,actInfo,...

'Observation','observations',actorOpts);

agentOpts = rlPPOAgentOptions(...

'SampleTime',Ts,...

'DiscountFactor',1.0, ...

'MiniBatchSize',64);

agent = rlPPOAgent(actor,critic,agentOpts);

maxepisodes = 5000;

maxsteps = ceil(Tf/Ts);

trainOpts = rlTrainingOptions(...

'MaxEpisodes',maxepisodes, ...

'MaxStepsPerEpisode',maxsteps, ...

'ScoreAveragingWindowLength',20, ...

'Verbose',false, ...

'Plots','training-progress',...

'StopTrainingCriteria','AverageReward',...

'StopTrainingValue',800);

doTraining = true;

if doTraining

% Train the agent.

trainingStats = train(agent,env,trainOpts);

else

% Load the pretrained agent for the example.

load('WaterTankDDPG.mat','agent')

end

simOpts = rlSimulationOptions('MaxSteps',maxsteps,'StopOnError','on');

experiences = sim(env,agent,simOpts);

function in = localResetFcn(in)

% randomize reference signal

blk = sprintf('rlwatertank/Desired \nWater Level');

h = 3*randn + 10;

while h <= 0 || h >= 20

h = 3*randn + 10;

end

in = setBlockParameter(in,blk,'Value',num2str(h));

% randomize initial height

h = 3*randn + 10;

while h <= 0 || h >= 20

h = 3*randn + 10;

end

blk = 'rlwatertank/Water-Tank System/H';

in = setBlockParameter(in,blk,'InitialCondition',num2str(h));

end

i've error like this, ho to fix it?

Error using rl.representation.rlAbstractRepresentation/validateModelInputDimension (line 557)

Model input sizes must match the dimensions specified in the corresponding observation and action info specifications.

Error in rl.representation.rlAbstractActorRepresentation (line 38)

validateModelInputDimension(this)

Error in rl.representation.rlStochasticActorRepresentation (line 18)

this = [e-mail]ation(Model, ObservationInfo, ActionInfo, Options);

Error in rlStochasticActorRepresentation (line 139)

Rep = rl.representation.rlStochasticActorRepresentation(...

Error in kodinganrlwatertankfixxxxxx (line 67)

Actor = rlStochasticActorRepresentation(net,obsInfo,actInfo,...

>>

[Tiva Rahmita]

PLs everyone who knows the solutions, i need the solutions quickly for my minithesis :"

[Luca D'Angelo]

try to use ActionInfo = rlFiniteSetSpec([1 1])