Reinforcement Learning Paycheck Optimization for Multivariate Financial Goals
<|reference_start|>Reinforcement Learning Paycheck Optimization for Multivariate Financial Goals: 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.<|reference_end|>
@article{alaluf2024reinforcement,
    title={Reinforcement Learning Paycheck Optimization for Multivariate Financial
  Goals},
    author={Melda Alaluf, Giulia Crippa, Sinong Geng, Zijian Jing, Nikhil
  Krishnan, Sanjeev Kulkarni, Wyatt Navarro, Ronnie Sircar, Jonathan Tang},
    journal={Risk and Decision Analysis, Volume 9, 2023},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.06011},
    primaryClass={cs.LG math.OC}
}