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

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)11-18
Number of pages8
JournalRisk and Decision Analysis
Issue number1
StatePublished - Oct 20 2023

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Finance
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty


  • Reinforcement learning
  • financial planning
  • personal finance
  • wealth management


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