1. A method of performing wavelet-modulated reinforcement learning-based run-by-run control to control output variability in a manufacturing process run, comprising the steps of: receiving process output signal (y t ) for a run t; modulating the process output signal (y t ) to generate a wavelet reconstructed signal (f t ); generating a predicted model output (ŷ t ) for the run t using a control recipe (u t ); calculating a process deviation (E t ) by taking the difference between the wavelet reconstructed signal (f t ) and the predicted model output (ŷ t ); predicting a reinforcement learning-based offset (a t ) using the process deviation (E t ); and generating a next control recipe (u t+1 ) using the reinforcement learning-based offset (a t ), wherein the next control recipe (u t+1 ) is used to predict the next process output at run t+1 and as a set-point for a PID controller for the next run t+1.