Reinforcement Learning Paycheck Optimization for Multivariate Financial
  Goals
Melda Alaluf Giulia Crippa Sinong Geng Zijian Jing Nikhil Krishnan Sanjeev Kulkarni Wyatt Navarro Ronnie Sircar Jonathan Tang
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.