Utilizing Explainability Techniques for Reinforcement Learning Model
  Assurance
Alexander Tapley Kyle Gatesman Luis Robaina Brett Bissey Joseph Weissman
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.