A Survey on Reinforcement Learning Security with Application to Autonomous Driving

Abstract:

Reinforcement learning allows machines to learn from their own experience. Nowadays, it is used in safety-critical applications, such as autonomous driving, despite being vulnerable to attacks carefully crafted to either prevent that the reinforcement learning algorithm learns an effective and reliable policy, or to induce the trained agent to make a wrong decision. The literature about the security of reinforcement learning is rapidly growing, and some surveys have been proposed to shed light on this field. However, their categorizations are insufficient for choosing an appropriate defense given the kind of system at hand. In our survey, we do not only overcome this limitation by considering a different perspective, but we also discuss the applicability of state-of-the-art attacks and defenses when reinforcement learning algorithms are used in the context of autonomous driving.

BibTeX:

@article{demontis2022survey,
author = {Demontis, Ambra and Pintor, Maura and Demetrio, Luca and Grosse, Kathrin and Lin, Hsiao-Ying and Fang, Chengfang and Biggio, Battista and Roli, Fabio},
title = {A Survey on Reinforcement Learning Security with Application to Autonomous Driving},
journal = {arXiv preprint arXiv:2212.06123},
year = {2022}
}

Recommended citation: Ambra Demontis, Maura Pintor, Luca Demetrio, Kathrin Grosse, Hsiao-Ying Lin, Chengfang Fang, Battista Biggio, Fabio Roli, "A Survey on Reinforcement Learning Security with Application to Autonomous Driving." arXiv preprint arXiv:2212.06123, 2022.