Adversarial Deep Reinforcement Learning for Improving the Robustness of Multi-agent Autonomous Driving Policies

TL;DR

This paper presents a two-step adversarial reinforcement learning approach to identify failure modes and enhance the robustness of autonomous driving policies in multi-agent urban simulation environments.

Contribution

It introduces a novel methodology combining adversarial testing and retraining to improve autonomous cars' robustness against adversarial attacks in multi-agent settings.

Findings

Adversarial testing effectively uncovers driving errors.

Retraining with adversarial inputs reduces collisions.

Enhanced policies show fewer off-road steering errors.

Abstract

Autonomous cars are well known for being vulnerable to adversarial attacks that can compromise the safety of the car and pose danger to other road users. To effectively defend against adversaries, it is required to not only test autonomous cars for finding driving errors but to improve the robustness of the cars to these errors. To this end, in this paper, we propose a two-step methodology for autonomous cars that consists of (i) finding failure states in autonomous cars by training the adversarial driving agent, and (ii) improving the robustness of autonomous cars by retraining them with effective adversarial inputs. Our methodology supports testing autonomous cars in a multi-agent environment, where we train and compare adversarial car policy on two custom reward functions to test the driving control decision of autonomous cars. We run experiments in a vision-based high-fidelity urban driving simulated environment. Our results show that adversarial testing can be used for finding erroneous autonomous driving behavior, followed by adversarial training for improving the robustness of deep reinforcement learning-based autonomous driving policies. We demonstrate that the autonomous cars retrained using the effective adversarial inputs noticeably increase the performance of their driving policies in terms of reduced collision and offroad steering errors.