# Utilizing Explainability Techniques for Reinforcement Learning Model Assurance

arXiv: https://arxiv.org/abs/2311.15838 · HTML: https://mlsanity.com/paper/2311.15838/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 27 Nov 2023.

## Builds on (most cited first, 7 of 7)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Interestingness Elements for Explainable Reinforcement Learning: Understanding Agents' Capabilities and Limitations](https://mlsanity.com/paper/1912.09007/) (2019)
- [Explainable Artificial Intelligence Approaches: A Survey](https://mlsanity.com/paper/2101.09429/) (2021)
- [A Survey of Explainable Reinforcement Learning](https://mlsanity.com/paper/2202.08434/) (2022)
- [Deep Reinforcement Learning Discovers Internal Models](https://mlsanity.com/paper/1606.05174/) (2016)
- [Terminal Adaptive Guidance for Autonomous Hypersonic Strike Weapons via Reinforcement Learning](https://mlsanity.com/paper/2110.00634/) (2021)

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

- [A Framework for Adversarial Analysis of Decision Support Systems Prior to Deployment](https://mlsanity.com/paper/2505.21414/) (2025)

## Abstract (arXiv)

Explainable Reinforcement Learning (XRL) can provide transparency into the decision-making process of a Deep Reinforcement Learning (DRL) model and increase user trust and adoption in real-world use cases. By utilizing XRL techniques, researchers can identify potential vulnerabilities within a trained DRL model prior to deployment, therefore limiting the potential for mission failure or mistakes by the system. This paper introduces the ARLIN (Assured RL Model Interrogation) Toolkit, an open-source Python library that identifies potential vulnerabilities and critical points within trained DRL models through detailed, human-interpretable explainability outputs. To illustrate ARLIN's effectiveness, we provide explainability visualizations and vulnerability analysis for a publicly available DRL model. The open-source code repository is available for download at https://github.com/mitre/arlin.