Mohan, Adithya (Technische Hochschule Ingolstadt), Rößle, Dominik (Technische Hochschule Ingolstadt), Cremers, Daniel (TU Munich), Schön, Torsten (Technische Hochschule Ingolstadt)

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach

Scheduled for presentation during the Invited Session "S17a-Synthetic-Data-Aided Safety-Critical Scenario Understanding in ITS" (TH-LM-T17), Thursday, November 20, 2025, 10:30−10:50, Southport 2

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Abstract

Recent advancements in Deep Reinforcement Learning (DRL) have demonstrated its applicability across various domains, including robotics, healthcare, energy optimization, and autonomous driving. However, a critical question remains: How robust are DRL models when exposed to adversarial attacks? While existing defense mechanisms such as adversarial training and distillation enhance the resilience of DRL models, there remains a significant research gap regarding the integration of multiple defenses in autonomous driving scenarios specifically. This paper addresses this gap by proposing a novel ensemble-based defense architecture to mitigate adversarial attacks in autonomous driving. Our evaluation demonstrates that the proposed architecture significantly enhances the robustness of DRL models. Compared to the baseline under FGSM attacks, our ensemble method improves the mean reward from 5.87 to 18.38 (over 213% increase) and reduces the mean collision rate from 0.50 to 0.09 (an 82% decrease) in the highway scenario and merge scenario, outperforming all standalone defense strategies.