Reinforcement Learning Paycheck Optimization for Multivariate Financial Goals
This addresses a quantitative gap in personal finance management for individuals, though it appears incremental as it builds on existing optimization and RL methods.
The paper tackled paycheck optimization by formulating it as a utility maximization problem to unify financial goals, incorporate user preferences, and handle stochastic interest rates, and implemented an end-to-end reinforcement learning solution across various settings.
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.