  We study paycheck optimization, which examines how to allocate income in
order to achieve several competing financial goals. For paycheck optimization,
a quantitative methodology is missing, due to a lack of a suitable problem
formulation. To deal with this issue, we formulate the problem as a utility
maximization problem. The proposed formulation is able to (i) unify different
financial goals; (ii) incorporate user preferences regarding the goals; (iii)
handle stochastic interest rates. The proposed formulation also facilitates an
end-to-end reinforcement learning solution, which is implemented on a variety
of problem settings.
