# G-Learner and GIRL: Goal Based Wealth Management with Reinforcement Learning

arXiv: https://arxiv.org/abs/2002.10990 · HTML: https://mlsanity.com/paper/2002.10990/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2020.

## Built on by (most cited first, 9 of 9)

- [Recent Advances in Reinforcement Learning in Finance](https://mlsanity.com/paper/2112.04553/) (2021)
- [The Evolution of Reinforcement Learning in Quantitative Finance: A Survey](https://mlsanity.com/paper/2408.10932/) (2024)
- [A Survey on Deep Reinforcement Learning for Data Processing and Analytics](https://mlsanity.com/paper/2108.04526/) (2021)
- [Systematic Review on Reinforcement Learning in the Field of Fintech](https://mlsanity.com/paper/2305.07466/) (2023)
- [Discrete-Time Mean-Variance Strategy Based on Reinforcement Learning](https://mlsanity.com/paper/2312.15385/) (2023)
- [Decoupling Time and Risk: Risk-Sensitive Reinforcement Learning with General Discounting](https://mlsanity.com/paper/2602.04131/) (2026)
- [Deep Reinforcement Learning for Robust Goal-Based Wealth Management](https://mlsanity.com/paper/2307.13501/) (2023)
- [Reinforcement Learning for Portfolio Optimization with a Financial Goal and Defined Time Horizons](https://mlsanity.com/paper/2511.18076/) (2025)
- [A Meta Reinforcement Learning Approach to Goals-Based Wealth Management](https://mlsanity.com/paper/2605.02300/) (2026)

## Abstract (arXiv)

We present a reinforcement learning approach to goal based wealth management problems such as optimization of retirement plans or target dated funds. In such problems, an investor seeks to achieve a financial goal by making periodic investments in the portfolio while being employed, and periodically draws from the account when in retirement, in addition to the ability to re-balance the portfolio by selling and buying different assets (e.g. stocks). Instead of relying on a utility of consumption, we present G-Learner: a reinforcement learning algorithm that operates with explicitly defined one-step rewards, does not assume a data generation process, and is suitable for noisy data. Our approach is based on G-learning - a probabilistic extension of the Q-learning method of reinforcement learning. In this paper, we demonstrate how G-learning, when applied to a quadratic reward and Gaussian reference policy, gives an entropy-regulated Linear Quadratic Regulator (LQR). This critical insight provides a novel and computationally tractable tool for wealth management tasks which scales to high dimensional portfolios. In addition to the solution of the direct problem of G-learning, we also present a new algorithm, GIRL, that extends our goal-based G-learning approach to the setting of Inverse Reinforcement Learning (IRL) where rewards collected by the agent are not observed, and should instead be inferred. We demonstrate that GIRL can successfully learn the reward parameters of a G-Learner agent and thus imitate its behavior. Finally, we discuss potential applications of the G-Learner and GIRL algorithms for wealth management and robo-advising.