Normalization of input data to Qnetwork
Normalizing the input can lead to faster convergence. It is highly recommended to normalize the inputs. 
And as the network will progress through different layers due to use of non-linearities the data flowing between the different layers will not be normalized anymore and therefore, for faster convergence we often use batch normalization layers. Unit Gaussian data always helps in faster convergence and therefore make sure to keep it in unit Gaussian form as much as possible.
I am well known with that a “normal” neural network should use normalized input data so one variable does not have a bigger influence on the weights in the NN than others.
But what if you have a Qnetwork where your training data and test data can differ a lot and can change over time in a continous problem?
My idea was to just run a normal run without normalization of input data and then see the variance and mean from the input datas of the run and then use the variance and mean to normalize my input data of my next run.
But what is the standard to do in this case?
Best regards Søren Koch