# Can Interpretable Reinforcement Learning Manage Prosperity Your Way?

arXiv: https://arxiv.org/abs/2202.09064 · HTML: https://mlsanity.com/paper/2202.09064/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 40 references that are in the map.
- First posted on arXiv 18 Feb 2022.

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

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Reinforcement Learning with Deep Energy-Based Policies](https://mlsanity.com/paper/1702.08165/) (2017)
- [Explainability in Deep Reinforcement Learning](https://mlsanity.com/paper/2008.06693/) (2020)
- [AI in Finance: Challenges, Techniques and Opportunities](https://mlsanity.com/paper/2107.09051/) (2021)
- [A Boolean Task Algebra for Reinforcement Learning](https://mlsanity.com/paper/2001.01394/) (2020)
- [Reinforcement Learning Your Way: Agent Characterization through Policy Regularization](https://mlsanity.com/paper/2201.10003/) (2022)
- [Clustering in Recurrent Neural Networks for Micro-Segmentation using Spending Personality](https://mlsanity.com/paper/2109.09425/) (2021)
- [Understanding Spending Behavior: Recurrent Neural Network Explanation and Interpretation](https://mlsanity.com/paper/2109.11871/) (2021)

## Built on by (most cited first, 6 of 6)

- [A Comprehensive Review on Financial Explainable AI](https://mlsanity.com/paper/2309.11960/) (2023)
- [Survey on AI Ethics: A Socio-technical Perspective](https://mlsanity.com/paper/2311.17228/) (2023)
- [Towards Artificial Virtuous Agents: Games, Dilemmas and Machine Learning](https://mlsanity.com/paper/2208.14037/) (2022)
- [Reinforcement Learning with Intrinsic Affinity for Personalized Prosperity Management](https://mlsanity.com/paper/2204.09218/) (2022)
- [Symbolic Explanation of Affinity-Based Reinforcement Learning Agents with Markov Models](https://mlsanity.com/paper/2208.12627/) (2022)
- [Fog of Love: Engineering Virtuous Agent Behavior with Affinity-based Reinforcement Learning in a Game Environment](https://mlsanity.com/paper/2606.04750/) (2026)

## Abstract (arXiv)

Personalisation of products and services is fast becoming the driver of success in banking and commerce. Machine learning holds the promise of gaining a deeper understanding of and tailoring to customers' needs and preferences. Whereas traditional solutions to financial decision problems frequently rely on model assumptions, reinforcement learning is able to exploit large amounts of data to improve customer modelling and decision-making in complex financial environments with fewer assumptions. Model explainability and interpretability present challenges from a regulatory perspective which demands transparency for acceptance; they also offer the opportunity for improved insight into and understanding of customers. Post-hoc approaches are typically used for explaining pretrained reinforcement learning models. Based on our previous modeling of customer spending behaviour, we adapt our recent reinforcement learning algorithm that intrinsically characterizes desirable behaviours and we transition to the problem of asset management. We train inherently interpretable reinforcement learning agents to give investment advice that is aligned with prototype financial personality traits which are combined to make a final recommendation. We observe that the trained agents' advice adheres to their intended characteristics, they learn the value of compound growth, and, without any explicit reference, the notion of risk as well as improved policy convergence.