Adversarial Deep Reinforcement Learning Attacks on Multi-Agent Autonomous Cooperative Driving Policies

Abstract

In recent years, multi-agent reinforcement learning (MARL) has been increasingly applied in training cooperative decision models for connected autonomous vehicles (CAVs). Despite the success they have demonstrated, they are bound to inherit issues that deep learning models suffer, such as vulnerability to adversarial attacks which is the focus of this study. Consequently, this paper aims to assess and enhance the robustness of MARL-trained cooperative policies used by CAVs, in terms of their resilience to adversarial behavior encountered during deployment. First, a specific existing cooperative policy was identified to be the victim policy, deployed in an on-ramp merging road scenario. Second, two adversarial policies, namely collision adversary ($ad{v}_c$) and speed adversary ($ad{v}_s$), were developed and trained to disrupt the performance of the victim policy. The adversarial policies significantly impacted the victim policy, increasing the collision rate to 62% and decreasing the average speed from 25 m/s to 21.73 m/s. Finally, several adversarial training approaches were developed, producing more robust cooperative policies against adversarial scenarios, by significantly bolstering road safety in adversarial conditions. The collision rate was cut by half against $ad{v}_c$, whereas, 0% collision scored in the face of $ad{v}_s$.