# Reinforcement Learning Paycheck Optimization for Multivariate Financial Goals

arXiv: https://arxiv.org/abs/2403.06011 · HTML: https://mlsanity.com/paper/2403.06011/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods.
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 9 Mar 2024.

## Placed in

- Deep reinforcement learning methods / [Adversarial robustness and RL in finance](https://mlsanity.com/deeprl/adversarial-robustness-and-rl-in-finance/)

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress](https://mlsanity.com/paper/1806.06877/) (2018)
- [Improving Offline RL by Blending Heuristics](https://mlsanity.com/paper/2306.00321/) (2023)
- [Partially Linear Additive Gaussian Graphical Models](https://mlsanity.com/paper/1906.03362/) (2019)
- [Temporal Poisson Square Root Graphical Models](https://mlsanity.com/paper/2005.06462/) (2020)
- [Variance Allocation and Shapley Value](https://mlsanity.com/paper/1606.09424/) (2016)
- [Joint Nonparametric Precision Matrix Estimation with Confounding](https://mlsanity.com/paper/1810.07147/) (2018)
- [A Data-Driven State Aggregation Approach for Dynamic Discrete Choice Models](https://mlsanity.com/paper/2304.04916/) (2023)

## Abstract (arXiv)

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.