1. Reinforcement Learning Paycheck Optimization for Multivariate Financial Goals
- Author
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Alaluf, Melda, Crippa, Giulia, Geng, Sinong, Jing, Zijian, Krishnan, Nikhil, Kulkarni, Sanjeev, Navarro, Wyatt, Sircar, Ronnie, and Tang, Jonathan
- Subjects
Computer Science - Machine Learning ,Mathematics - Optimization and Control - Abstract
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.
- Published
- 2024