# How does normalization of the inputs work in the context of PPO?

What does the normalization of the inputs mean in the context of PPO? At each time step of an episode, I only know the values of this time step and of the previous ones, if I take track of them. This means that for each observation and for each reward at each time step I will do:

value = (value - mean) / std


before passing them to the NN, right? Specifically, I compute mean and std by keeping track of the values for the whole episode and at each time step, I add the new values to an array. Is this a valid approach?

Also, how can I handle negative rewards, such that being positive?