Jun 2026· arXiv.org· Vol abs/2606.29867· 0 citations· 26 references
Computer Science
Abstract
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 \textbf{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.
Evidential Adversarial Training (EV-AT), which models uncertainty through a Dirichlet distribution and combines an evidence-based loss promoting clean accuracy and reliable uncertainty with a robust evidence-alignment loss matching clean and adversarial predictions in log Dirichlet-parameter space, is proposed.
Nicolas Sournac, Ahmed Baha Ben Jmaa, B. Braeckeveldt· 0 citations
Deep reinforcement learning (DRL) has achieved great success in many simulated and real-world robotic tasks. However, the difficulty of designing efficient and dense reward functions makes applying DRL to tackle complex long-horizon and open-world tasks a great challenge. Generative adversarial imitation learning (GAIL) can directly learn policies from the expert trajectories and generalize well in large and complex environments, but relies on high-quality demonstrations and can seldom surpass the performance of the demonstration. Recent work used additional human evaluative feedback to facilitate GAIL to learn faster and surpass the demonstrations, but still requires suboptimal demonstrations. Moreover, it is costly and difficult for human expert to provide relatively high-quality demonstrations and evaluative feedback for various tasks. To address the above issues, in this article, we propose generative adversarial self-imitation learning from demonstration and large language model (LLM) feedback (GASL<inline-formula><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula>MF), since LLMs encode rich commonsense knowledge and can perform a variety of reasoning tasks. GASL<inline-formula><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula>MF allows a robot to learn from poor demonstrations and gradually replace them with its own good trajectories evaluated by LLM feedback. Our results in four physics-based control tasks and a mobile robot navigation task show that, even with demonstrations of poor performance or not completing the task, GASL<inline-formula><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula>MF can learn faster with close to optimal performance, and generalize well to different environments and the real world with sim-to-real adaptation. Further analysis shows that the overall distribution of LLM feedback closely resembles that of human feedback and remains closer to that of ground-truth rewards than human feedback. Finally, our GASL<inline-formula><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula>MF method works regardless of the LLM employed, and the LLM feedback from different LLMs remain robust across tasks and even better consistency than human feedback for robot learning in some tasks. These results shed light on the potential of robot imitation learning from even poor or failed demonstrations and broaden its application to a wide range of real-world tasks.
Keyvan Zhang, Zheng Fang, Enqi Zhao et al.· IEEE Transactions on robotic...· 0 citations
A systematic analysis of 207 studies selected from 4447 records following the PRISMA 2020 guidelines, covering work published between 2020 and 2026 across cybersecurity and computer vision finds systems that are robust against adaptive adversaries, interpretable under operational constraints, and auditable in environments where AI accountability is a legal requirement.
This work constraining reward function candidacy during IRL to the space of CBFs yields a formulation that exhibits safe online control with continuous experiential improvement, and demonstrates that the recovered barrier function is robust to unsafe states entirely absent from the expert data.
Anubhav Vishwakarma, Bhaumik Mehta, Caleb Hsu et al.· 0 citations
This paper advocates for a forward-thinking approach that balances technical sophistication with human-centric principles, ensuring that adversarial deep learning evolves into a discipline not just of technical defense, but also of trust, transparency, and accountability.
Maisam Abbas, Ran-Zan Wang· IEEE Open Journal of the Com...· 0 citations
Empirical evaluations reveal a fundamental brittleness in existing defenses: with a single trainable 7B planner, Trident reduces blue agent defensive performance by an average of 522% compared to static red agent baselines while autonomously discovering emergent behaviors such as decoy avoidance and adaptive state prioritization that static heuristics entirely fail to uncover.
Ryozo Masukawa, Ian Bryant, Armita Kazeminajafabadi et al.· 0 citations