# Reinforcement Learning Your Way: Agent Characterization through Policy Regularization

arXiv: https://arxiv.org/abs/2201.10003 · HTML: https://mlsanity.com/paper/2201.10003/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 24 references that are in the map.
- First posted on arXiv 21 Jan 2022.

## Builds on (most cited first, 15 of 15)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [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)
- [Risk-Constrained Reinforcement Learning with Percentile Risk Criteria](https://mlsanity.com/paper/1512.01629/) (2015)
- [Explainable Reinforcement Learning Through a Causal Lens](https://mlsanity.com/paper/1905.10958/) (2019)
- [Explainability in Deep Reinforcement Learning](https://mlsanity.com/paper/2008.06693/) (2020)
- [Reinforcement Learning with Convex Constraints](https://mlsanity.com/paper/1906.09323/) (2019)
- [Hybrid Reward Architecture for Reinforcement Learning](https://mlsanity.com/paper/1706.04208/) (2017)
- [Information asymmetry in KL-regularized RL](https://mlsanity.com/paper/1905.01240/) (2019)
- [Control Regularization for Reduced Variance Reinforcement Learning](https://mlsanity.com/paper/1905.05380/) (2019)
- [Dot-to-Dot: Explainable Hierarchical Reinforcement Learning for Robotic Manipulation](https://mlsanity.com/paper/1904.06703/) (2019)
- [TD-Regularized Actor-Critic Methods](https://mlsanity.com/paper/1812.08288/) (2018)
- [Towards Hierarchical Task Decomposition using Deep Reinforcement Learning for Pick and Place Subtasks](https://mlsanity.com/paper/2102.04022/) (2021)
- [Clustering in Recurrent Neural Networks for Micro-Segmentation using Spending Personality](https://mlsanity.com/paper/2109.09425/) (2021)

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

- [Can Interpretable Reinforcement Learning Manage Prosperity Your Way?](https://mlsanity.com/paper/2202.09064/) (2022)
- [Towards Artificial Virtuous Agents: Games, Dilemmas and Machine Learning](https://mlsanity.com/paper/2208.14037/) (2022)
- [Artificial virtuous agents in a multiagent tragedy of the commons](https://mlsanity.com/paper/2210.02769/) (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)

The increased complexity of state-of-the-art reinforcement learning (RL) algorithms have resulted in an opacity that inhibits explainability and understanding. This has led to the development of several post-hoc explainability methods that aim to extract information from learned policies thus aiding explainability. These methods rely on empirical observations of the policy and thus aim to generalize a characterization of agents' behaviour. In this study, we have instead developed a method to imbue a characteristic behaviour into agents' policies through regularization of their objective functions. Our method guides the agents' behaviour during learning which results in an intrinsic characterization; it connects the learning process with model explanation. We provide a formal argument and empirical evidence for the viability of our method. In future work, we intend to employ it to develop agents that optimize individual financial customers' investment portfolios based on their spending personalities.