# Deep Reinforcement Learning for Robust Goal-Based Wealth Management

arXiv: https://arxiv.org/abs/2307.13501 · HTML: https://mlsanity.com/paper/2307.13501/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 25 Jul 2023.

## Builds on (most cited first, 5 of 5)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Deep Reinforcement Learning for Trading](https://mlsanity.com/paper/1911.10107/) (2019)
- [An Application of Deep Reinforcement Learning to Algorithmic Trading](https://mlsanity.com/paper/2004.06627/) (2020)
- [G-Learner and GIRL: Goal Based Wealth Management with Reinforcement Learning](https://mlsanity.com/paper/2002.10990/) (2020)
- [Deep Reinforcement Learning for Market Making Under a Hawkes Process-Based Limit Order Book Model](https://mlsanity.com/paper/2207.09951/) (2022)

## Abstract (arXiv)

Goal-based investing is an approach to wealth management that prioritizes achieving specific financial goals. It is naturally formulated as a sequential decision-making problem as it requires choosing the appropriate investment until a goal is achieved. Consequently, reinforcement learning, a machine learning technique appropriate for sequential decision-making, offers a promising path for optimizing these investment strategies. In this paper, a novel approach for robust goal-based wealth management based on deep reinforcement learning is proposed. The experimental results indicate its superiority over several goal-based wealth management benchmarks on both simulated and historical market data.