Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learning

Understand

Adversarial attacks against conventional Deep Learning (DL) systems and algorithms have been widely studied, and various defenses were proposed.

• However, the possibility and feasibility of such attacks against Deep Reinforcement Learning (DRL) are less explored.
• As DRL has achieved great success in various complex tasks, designing effective adversarial attacks is an indispensable prerequisite towards building robust DRL algorithms.
• In this paper, we introduce two novel adversarial attack techniques to \emph{stealthily} and \emph{efficiently} attack the DRL agents.