Normalization process in Reinforcement Learning

Hi everyone, I have a question about 'Normalization', I'm asking that how do this process operate in ==>featureInputLayer?

The documentation says us that "Normalization" part is performed automatically. Is that true? Because when the featureInputLayer is created, 'Normalization' is defined "None" as following:

----------------------------------------------------------------------------------

featureInputLayer(numObservations,'Normalization','none','Name','observation')

----------------------------------------------------------------------------------

But, How it normalize observations/actions? Can you explain it clearly?

UASS

[H. M.]

Hi

did you get any answer yet

[Jyo Deshmukh]

In fact when I use normalization with any other option, I get an error:

Error using rl.internal.model.DLNetworkModel>localValidateLayer

Set 'Normalization' parameter

of featureInputLayer to 'none'.

[Unmanned Aerial and Space Systems]

yes, i agree with you, how can we use other options? the question is how we gonna change this option with other?