FURI | Fall 2019

Adversarial Attacks in Reinforcement Learning for Autonomous Vehicle Control

Autonomous vehicles are believed to be the next disruptive technology, but there is evidence that their marriage with AI may be complicated since the current intelligent systems are not robust to adversarial attacks. Such attacks manipulate the environments used for Reinforcement Learning systems and cause the resulting controllers to embed backdoors that produce target (malicious) actions when a visual trigger is present. This study is the basis for exploring the vulnerability of existing deep RL systems, and a necessity for counterfactual reasoning to achieve robust AI