Cyber-Physical Systems (CPS) often leverage Reinforcement Learning (RL) techniques to adapt dynamically to changing environments and optimize performance. However, it is challenging to construct safety cases for RL components. We therefore propose the SAFE-RL (Safety and Accountability Framework for Evaluating Reinforcement Learning) for supporting the development, validation, and safe deployment of RL-based CPS. We adopt a design science approach to construct the framework and demonstrate its use in three RL applications in small Uncrewed Aerial systems (sUAS)
View on arXiv@article{dearstyne2025_2503.09388,
title={ Evaluating Reinforcement Learning Safety and Trustworthiness in Cyber-Physical Systems },
author={ Katherine Dearstyne and Pedro and Alarcon Granadeno and Theodore Chambers and Jane Cleland-Huang },
journal={arXiv preprint arXiv:2503.09388},
year={ 2025 }
}