Resilience Analysis of Deep Q-Learning Algorithms in Driving Simulations Against Cyberattacks
Godwyll Aikins, Sagar Jagtap, Weinan Gao · 2022
Deep reinforcement learning (DRL) has attracted attentions by researchers to complete complex tasks in engineering, such as autonomous driving, that are typically very difficult to achieve using traditional model-based approaches. With the safety being critical in self-driving vehicles and the increased reliance on vehicle connectivity, the resilience to cyberattacks has to be systematically studied. In this paper, we train a deep Q-learning based agent to drive autonomously in the CARLA simulator under various scenarios that the agent may experience during a cyberattack. Specifically, we observe the agent’s behavior and performance in the presence of denial-of-service and deception attacks. The results reflect an inbuilt level of resilience to cyberattacks with the DRL methods. Comparing with conventional driving agents, deep Q-learning agents can learn to deal with uncertainty and missing information without explicitly modeling such behavior