Defending Observation Attacks in Deep Reinforcement Learning via Detection and Denoising

Zikang Xiong, Joe Eappen, He Zhu +1

Neural network policies trained using Deep Reinforcement Learning (DRL) are well-known to be susceptible to adversarial attacks. In this paper, we consider attacks manifesting as p…