Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning

Abstract

Deep reinforcement learning (DRL) has numerous applications in the real world, thanks to its ability to achieve high performance in a range of environments with little manual oversight. Despite its great advantages, DRL is susceptible to adversarial attacks, which precludes its use in real-life critical systems and applications (e.g., smart grids, traffic controls, and autonomous vehicles) unless its vulnerabilities are addressed and mitigated. To address this problem, we provide a comprehensive survey that discusses emerging attacks on DRL-based systems and the potential countermeasures to defend against these attacks. We first review the fundamental background on DRL and present emerging adversarial attacks on machine learning techniques. We then investigate the vulnerabilities that an adversary can exploit to attack DRL along with state-of-the-art countermeasures to prevent such attacks. Finally, we highlight open issues and research challenges for developing solutions to deal with attacks on DRL-based intelligent systems.