Verification of Adversarially Robust Reinforcement Learning Mechanisms in Aerospace Systems
Taehwan Seo, Prachi Pratyusha Sahoo, Kyriakos G. Vamvoudakis · AIAA SCITECH 2023 Forum · 2023
View Video Presentation: https://doi.org/10.2514/6.2023-1070.vid In this paper, we present a detailed framework for the verification and validation of learning-based reinforcement learning (RL) mechanisms in aerospace control software. First, we integrate an adversarial input mitigation and moving target defense framework and verify its efficacy in real-time. Then, we provide a testing framework to verify the robustness of closed-loop RL mechanisms. The reliability of the adversarially robust RL mechanism is tested using the VerifAI toolkit and an X-plane 11 Cessna 172.