Blackbox optimizers do not check for standard deviation of 0

In ES training, when all of the rewards are the same value, the standard deviation is 0. The blackbox optimizers do not check for this case before dividing by the standard deviation of the function values (which is zero), resulting in a list of nan for the gradient and thus setting the model weights to nan as well. This case needs to be addressed so that the model weights result in real float values instead.