多变量财务目标的强化学习工资优化

Q3 Economics, Econometrics and Finance
Melda Alaluf, Giulia Crippa, Sinong Geng, Zijian Jing, Nikhil Krishnan, Sanjeev Kulkarni, Wyatt Navarro, R. Sircar, Jonathan Tang
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引用次数: 4

摘要

我们研究薪资优化,研究如何分配收入以实现几个相互竞争的财务目标。对于薪资优化,由于缺乏合适的问题公式,缺少定量方法。为了解决这个问题,我们将这个问题表述为效用最大化问题。拟议的提法能够(一)统一不同的财务目标;(ii)结合关于目标的用户偏好;(iii)处理随机利率。所提出的公式还促进了端到端的强化学习解决方案,该解决方案可在各种问题环境中实现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
Risk and Decision Analysis
Risk and Decision Analysis Economics, Econometrics and Finance-Economics and Econometrics
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