mlsanityData to 14 Oct 2026

RoAd-RL: A Unified Library and Benchmark for Robust Adversarial Reinforcement Learning

arXiv 2606.29867 ↗First posted 29 Jun 2026cs.LG cs.AIcited by 0

On the map: Deep reinforcement learning methods → Adversarial robustness and RL in finance

Abstract (arXiv)

Deep Reinforcement Learning (DRL) has achieved significant success in robotics and autonomous systems, yet remains vulnerable to adversarial perturbations that can severely degrade performance. Research in adversarial reinforcement learning is often limited by fragmented implementations, inconsistent evaluation protocols, and poor reproducibility. To address these challenges, we present RoAd-RL, an open-source benchmarking framework that provides unified abstractions for policies, attacks, defenses, and robustness metrics, together with reproducible evaluation pipelines and seamless integration with Stable-Baselines3 and Gymnasium. We evaluate DQN, PPO, and SAC agents in LunarLander and Highway-v0 under 192 attack-defense configurations. Results reveal substantial variations in robustness across environments and show that some commonly used defenses can be more detrimental than the attacks they aim to mitigate, while temporal smoothing consistently achieves strong performance. RoAd-RL establishes a standardized benchmark for adversarial reinforcement learning research and is publicly available at https://pypi.org/project/road-rl.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods. Builds on 19 of its 26 references that are in the map. First posted on arXiv 29 Jun 2026.

Builds on

19 of 26 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach22 Jul

Other references · 18

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  3. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  4. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  5. 2019Certified Adversarial Robustness via Randomized Smoothingcited by 1,340
  6. 2015The Limitations of Deep Learning in Adversarial Settingscited by 790
  7. 2020RobustBench: a standardized adversarial robustness benchmarkcited by 661
  8. 2017Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networkscited by 486
  9. 2018Certified Robustness to Adversarial Examples with Differential Privacycited by 468
  10. 2017Adversarial Attacks on Neural Network Policiescited by 320
  11. 2020Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observationscited by 238
  12. 2017Tactics of Adversarial Attack on Deep Reinforcement Learning Agentscited by 166
6 more
  1. 2017Robust Deep Reinforcement Learning with Adversarial Attackscited by 154
  2. 2016Technical Report on the CleverHans v2.1.0 Adversarial Examples Librarycited by 124
  3. 2020Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learningcited by 63
  4. 2025UrbanIng-V2X: A Large-Scale Multi-Vehicle, Multi-Infrastructure Dataset Across Multiple Intersections for Cooperative Perceptioncited by 15
  5. 2026DrivIng: A Large-Scale Multimodal Driving Dataset with Full Digital Twin Integrationcited by 3
  6. 2026Real-Time Evaluation of Autonomous Systems under Adversarial Attackscited by 1

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  1. 2024Robust Deep Reinforcement Learning Through Adversarial Attacks and Training : A Survey
  2. 2024Towards Robust Policy: Enhancing Offline Reinforcement Learning with Adversarial Attacks and Defenses
  3. 2026Robust Adversarial Reinforcement Learning with Risk Sensitivity and Critic Consistency Regularization
  4. 2025Robust Deep Reinforcement Learning in Robotics via Adaptive Gradient-Masked Adversarial Attacks
  5. 2024Analyzing Adversarial Inputs in Deep Reinforcement Learning
  6. 2026Post-Hoc Robustness for Model-Based Reinforcement Learning
  7. 2026Robust Policy Optimization via Adversarial Importance Sampling
  8. 2022RAMBO-RL: Robust Adversarial Model-Based Offline Reinforcement Learning