arXiv AI July 28, 2026 By Dhiraj Neupane, Mohamed Reda Bouadjenek, Richard Dazeley, Sunil Aryal

Label-free Industrial Fault Detection via Adversarial Inverse Reinforcement Learning: A System for Run-to-Failure Prognostics

arXiv:2607. 22987v1 Announce Type: cross Abstract: Machinery fault detection (MFD) remains heavily reliant on supervised learning, which struggles with the scarcity of fault labels in real-world settings.

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arXiv Machine Learning

Jun 9

Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks

arXiv:2506. 06891v3 Announce Type: replace Abstract: We study the corruption-robustness of in-context reinforcement learning (ICRL), focusing on the Decision-Pretrained Transformer (DPT, Lee et al.

By Paulius Sasnauskas, Yi\u{g}it Yal{\i}n, Goran Radanovi\'c

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arXiv AI

Jul 3

Trust Region Inverse Reinforcement Learning: Explicit Dual Ascent using Local Policy Updates

arXiv:2605. 11020v2 Announce Type: replace-cross Abstract: Inverse reinforcement learning (IRL) is typically formulated as maximizing entropy subject to matching the distribution of expert trajectories.

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Hugging Face Trending Papers

Jul 14

OOD-RL-Bench: A Benchmark Framework for Out-of-Distribution Detection in Reinforcement Learning

Reliable reinforcement learning (RL) agents must maintain operational integrity amidst sensor malfunctions, dynamic disturbances, and slow environmental shifts. The detection of out-of-distribution conditions is pivotal to determining when an agent's observations, transitions, or trajectory dynamics deviate from the assumptions underpinning its policy training.

agents reinforcement-learning benchmarks

arXiv AI

Aug 24

AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning

arXiv:2508.14313v4 Announce Type: replace-cross Abstract: Test-time scaling strategies for Large Language Models predominantly rely on either reinforcement learning with sparse outcome rewards or sea...

By Can Jin, Yang Zhou, Qixin Zhang, Hongwu Peng, Di Zhang, Zihan Dong, Marco Pavone, Ligong Han, Zhang-Wei Hong, Tong Che, Dimitris N. Metaxas

llms reinforcement-learning benchmarks safety

arXiv Machine Learning

Sep 24

Robust Adversarial Reinforcement Learning with Risk Sensitivity and Critic Consistency Regularization

arXiv:2609.27667v1 Announce Type: new Abstract: Reinforcement learning (RL) achieves strong performance in sequential decision-making but remains brittle under dynamic uncertainty and distributional...

By Jiaxi Wu, Tiantian Zhang, Yuxing Wang, Yongzhe Chang, Xueqian Wang

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arXiv AI

Jun 30

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

arXiv:2606. 29867v1 Announce Type: cross 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.

By Adithya Mohan, Daniel Kriegl, Torsten Sch\"on

agents reinforcement-learning robotics benchmarks safety

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Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks

Trust Region Inverse Reinforcement Learning: Explicit Dual Ascent using Local Policy Updates

OOD-RL-Bench: A Benchmark Framework for Out-of-Distribution Detection in Reinforcement Learning

AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning

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