# Reinforcement Learning with Intrinsic Affinity for Personalized Prosperity Management

arXiv: https://arxiv.org/abs/2204.09218 · HTML: https://mlsanity.com/paper/2204.09218/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 45 references that are in the map.
- First posted on arXiv 20 Apr 2022.

## Builds on (most cited first, 21 of 21)

- [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)
- [Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation](https://mlsanity.com/paper/1604.06057/) (2016)
- [Explainability in Deep Reinforcement Learning](https://mlsanity.com/paper/2008.06693/) (2020)
- [A survey on intrinsic motivation in reinforcement learning](https://mlsanity.com/paper/1908.06976/) (2019)
- [AI in Finance: Challenges, Techniques and Opportunities](https://mlsanity.com/paper/2107.09051/) (2021)
- [Reinforcement Learning with Convex Constraints](https://mlsanity.com/paper/1906.09323/) (2019)
- [Information asymmetry in KL-regularized RL](https://mlsanity.com/paper/1905.01240/) (2019)
- [A Boolean Task Algebra for Reinforcement Learning](https://mlsanity.com/paper/2001.01394/) (2020)
- [Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics](https://mlsanity.com/paper/1906.10720/) (2019)
- [Dot-to-Dot: Explainable Hierarchical Reinforcement Learning for Robotic Manipulation](https://mlsanity.com/paper/1904.06703/) (2019)
- [Hierarchical Reinforcement Learning with Hindsight](https://mlsanity.com/paper/1805.08180/) (2018)
- [Interpreting recurrent neural networks behaviour via excitable network attractors](https://mlsanity.com/paper/1807.10478/) (2018)
- [Towards Hierarchical Task Decomposition using Deep Reinforcement Learning for Pick and Place Subtasks](https://mlsanity.com/paper/2102.04022/) (2021)
- [Reinforcement Learning Your Way: Agent Characterization through Policy Regularization](https://mlsanity.com/paper/2201.10003/) (2022)
- [Can Interpretable Reinforcement Learning Manage Prosperity Your Way?](https://mlsanity.com/paper/2202.09064/) (2022)
- [Clustering in Recurrent Neural Networks for Micro-Segmentation using Spending Personality](https://mlsanity.com/paper/2109.09425/) (2021)
- [Collaborative Training of Heterogeneous Reinforcement Learning Agents in Environments with Sparse Rewards: What and When to Share?](https://mlsanity.com/paper/2202.12174/) (2022)
- [Understanding Spending Behavior: Recurrent Neural Network Explanation and Interpretation](https://mlsanity.com/paper/2109.11871/) (2021)
- [Intelligent Vector-based Customer Segmentation in the Banking Industry](https://mlsanity.com/paper/2012.11876/) (2020)
- [Explainable AI for Psychological Profiling from Digital Footprints: A Case Study of Big Five Personality Predictions from Spending Data](https://mlsanity.com/paper/2111.06908/) (2021)

## Built on by (most cited first, 1 of 1)

- [Symbolic Explanation of Affinity-Based Reinforcement Learning Agents with Markov Models](https://mlsanity.com/paper/2208.12627/) (2022)

## Abstract (arXiv)

The common purpose of applying reinforcement learning (RL) to asset management is the maximization of profit. The extrinsic reward function used to learn an optimal strategy typically does not take into account any other preferences or constraints. We have developed a regularization method that ensures that strategies have global intrinsic affinities, i.e., different personalities may have preferences for certain assets which may change over time. We capitalize on these intrinsic policy affinities to make our RL model inherently interpretable. We demonstrate how RL agents can be trained to orchestrate such individual policies for particular personality profiles and still achieve high returns.