# Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach

arXiv: https://arxiv.org/abs/2507.17070 · HTML: https://mlsanity.com/paper/2507.17070/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods.
- Builds on 25 of its 39 references that are in the map.
- First posted on arXiv 22 Jul 2025.

## Placed in

- Deep reinforcement learning methods / [Adversarial robustness and RL in finance](https://mlsanity.com/deeprl/adversarial-robustness-and-rl-in-finance/)

## Builds on (most cited first, 25 of 25)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Deep Reinforcement Learning: An Overview](https://mlsanity.com/paper/1701.07274/) (2017)
- [Adversarial Attacks on Neural Network Policies](https://mlsanity.com/paper/1702.02284/) (2017)
- [Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations](https://mlsanity.com/paper/2003.08938/) (2020)
- [Action Robust Reinforcement Learning and Applications in Continuous Control](https://mlsanity.com/paper/1901.09184/) (2019)
- [Tactics of Adversarial Attack on Deep Reinforcement Learning Agents](https://mlsanity.com/paper/1703.06748/) (2017)
- [Robust Deep Reinforcement Learning with Adversarial Attacks](https://mlsanity.com/paper/1712.03632/) (2017)
- [Robust Reinforcement Learning on State Observations with Learned Optimal Adversary](https://mlsanity.com/paper/2101.08452/) (2021)
- [Delving into adversarial attacks on deep policies](https://mlsanity.com/paper/1705.06452/) (2017)
- [Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks](https://mlsanity.com/paper/1701.04143/) (2017)
- [Robust Deep Reinforcement Learning through Adversarial Loss](https://mlsanity.com/paper/2008.01976/) (2020)
- [Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learning](https://mlsanity.com/paper/2005.07099/) (2020)
- [Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning](https://mlsanity.com/paper/2001.09684/) (2020)
- [Certified Adversarial Robustness for Deep Reinforcement Learning](https://mlsanity.com/paper/1910.12908/) (2019)
- [Whatever Does Not Kill Deep Reinforcement Learning, Makes It Stronger](https://mlsanity.com/paper/1712.09344/) (2017)
- [Ensemble Methods as a Defense to Adversarial Perturbations Against Deep Neural Networks](https://mlsanity.com/paper/1709.03423/) (2017)
- [TrojDRL: Trojan Attacks on Deep Reinforcement Learning Agents](https://mlsanity.com/paper/1903.06638/) (2019)
- [Provably Efficient Black-Box Action Poisoning Attacks Against Reinforcement Learning](https://mlsanity.com/paper/2110.04471/) (2021)
- [Online Robustness Training for Deep Reinforcement Learning](https://mlsanity.com/paper/1911.00887/) (2019)
- [Optimal Attacks on Reinforcement Learning Policies](https://mlsanity.com/paper/1907.13548/) (2019)
- [Detecting Adversarial Attacks on Neural Network Policies with Visual Foresight](https://mlsanity.com/paper/1710.00814/) (2017)
- [Adversarial attacks in consensus-based multi-agent reinforcement learning](https://mlsanity.com/paper/2103.06967/) (2021)
- [Sequential Attacks on Agents for Long-Term Adversarial Goals](https://mlsanity.com/paper/1805.12487/) (2018)
- [CopyCAT: Taking Control of Neural Policies with Constant Attacks](https://mlsanity.com/paper/1905.12282/) (2019)
- [Gradient Band-based Adversarial Training for Generalized Attack Immunity of A3C Path Finding](https://mlsanity.com/paper/1807.06752/) (2018)

## Built on by (most cited first, 3 of 3)

- [DrivIng: A Large-Scale Multimodal Driving Dataset with Full Digital Twin Integration](https://mlsanity.com/paper/2601.15260/) (2026)
- [Real-Time Evaluation of Autonomous Systems under Adversarial Attacks](https://mlsanity.com/paper/2605.03491/) (2026)
- [RoAd-RL: A Unified Library and Benchmark for Robust Adversarial Reinforcement Learning](https://mlsanity.com/paper/2606.29867/) (2026)

## Abstract (arXiv)

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.