I received this error in DDPG "Model input sizes must match the dimensions specified in the corresponding observation and action info specifications."
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ali kadkhodaei
el 12 de Oct. de 2023
Respondida: Emmanouil Tzorakoleftherakis
el 13 de Oct. de 2023
hi every body
i want to impliment LSTM layers in state path of my critic network
my code is :
obsInfo = rlNumericSpec([42 1]);
obsInfo.Name = 'observation';
actInfo = rlNumericSpec([6 1]);
actInfo.Name = 'action';
actInfo.UpperLimit =0.01*ones(6,1);
actInfo.LowerLimit =-0.01*ones(6,1);
numObs=prod(obsInfo.Dimension) ;
numAcs=prod(actInfo.Dimension) ;
numHiddenUnits=100;
statePath = [sequenceInputLayer(numObs)
lstmLayer(numHiddenUnits,'OutputMode','sequence')
fullyConnectedLayer(500,'Name','CriticStateFC1')
reluLayer('Name', 'CriticStateRelu1')
fullyConnectedLayer(450,'Name','CriticStateFC2')
reluLayer('Name', 'CriticStateRelu2')
fullyConnectedLayer(300,'Name','CriticStateFC3')
reluLayer('Name', 'CriticStateRelu3')
fullyConnectedLayer(250,'Name','CriticStateFC4')
reluLayer('Name', 'CriticStateRelu4')
fullyConnectedLayer(200,'Name','CriticStateFC5')
reluLayer('Name', 'CriticStateRelu5')
fullyConnectedLayer(100,'Name','CriticStateFC6')
reluLayer('Name', 'CriticStateRelu6')
fullyConnectedLayer(50,'Name','CriticStateFC7')
reluLayer('Name', 'CriticStateRelu7')
fullyConnectedLayer(25,'Name','CriticStateFC8')
];
actionPath = [
sequenceInputLayer(numAcs)
fullyConnectedLayer(300,'Name','CriticActionFC1')
reluLayer('Name', 'CriticActionRelu1')
fullyConnectedLayer(250,'Name','CriticActionFC2')
reluLayer('Name', 'CriticActionRelu2')
fullyConnectedLayer(200,'Name','CriticActionFC3')
reluLayer('Name', 'CriticActionRelu3')
fullyConnectedLayer(100,'Name','CriticActionFC4')
reluLayer('Name', 'CriticActionRelu4')
fullyConnectedLayer(50,'Name','CriticActionFC5')
reluLayer('Name', 'CriticActionRelu5')
fullyConnectedLayer(25,'Name','CriticActionFC6')];
commonPath = [
concatenationLayer(1,2,Name='concat')
reluLayer('Name','CriticCommonRelu')
fullyConnectedLayer(512,'Name','CriticCommon1')
reluLayer('Name','CriticCommonRelu2')
fullyConnectedLayer(300,'Name','CriticCommon2')
reluLayer('Name','CriticCommonRelu3')
fullyConnectedLayer(1,'Name','CriticOutput')];
criticOpts =rlOptimizerOptions(LearnRate=1e-04,GradientThreshold=inf,...
L2RegularizationFactor=0.0001,Optimizer="adam");
criticNetwork = layerGraph();
criticNetwork = addLayers(criticNetwork,statePath);
criticNetwork = addLayers(criticNetwork,actionPath);
criticNetwork = addLayers(criticNetwork,commonPath);
criticNetwork = connectLayers(criticNetwork,'CriticStateFC8','concat/in1');
criticNetwork = connectLayers(criticNetwork,'CriticActionFC6','concat/in2');
criticNetwork = dlnetwork(criticNetwork);
analyzeNetwork(criticNetwork)
critic = rlQValueFunction(criticNetwork,obsInfo,actInfo,...
ObservationInputNames='observation',ActionInputNames='action',UseDevice="gpu");
when i run above code i received this error
"Error using rlQValueFunction
Model input sizes must match the dimensions specified in the corresponding observation
and action info specifications." please help me
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Emmanouil Tzorakoleftherakis
el 13 de Oct. de 2023
The easiest way to discover your error yourself is to use the default agent feature have use the network architecture that's automatically generated by Reinforcement Learning Toolbox. Please take a look here. Don't forget to indicate that you want your network to use LSTM layers in the initialization options.
Per the example in the link above, after the agent is created, you can extract the actor and critic and compare the generated architecture with what you have.
Hope this helps
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