arXiv AI August 21, 2026 By Astrid Horn Brorholt (Aalborg University, Aalborg, Denmark), Maris F. L. Galesloot (Radboud University, Nijmegen, Netherlands), Nils Jansen (Radboud University, Nijmegen, Netherlands), Kim Guldstrand Larsen (Aalborg University, Aalborg, Denmark), Christian Schilling (Aalborg University, Aalborg, Denmark)

Adaptive Probabilistic Shielding by Learning MDPs for Safe Reinforcement Learning

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arXiv:2608. 19836v1 Announce Type: cross Abstract: Probabilistic shielding is a technique for safe reinforcement learning (RL).

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arXiv AI

Jun 2

Robust Shielding for Safe Reinforcement Learning

arXiv:2606. 00270v1 Announce Type: new Abstract: Shielding is an effective approach to formally guarantee the safety of reinforcement learning agents in Markov decision processes (MDPs).

By Edwin Hamel-De le Court, Thom Badings, Alessandro Abate, Francesco Belardinelli, Francesco Fabiano

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Jun 4

Explainably Safe Reinforcement Learning

arXiv:2606. 04634v1 Announce Type: new Abstract: Trust in a decision-making system requires both safety guarantees and the ability to interpret and understand its behavior.

By Sabine Rieder, Stefan Pranger, Debraj Chakraborty, Jan K\v{r}et\'insk\'y, Bettina K\"onighofer

reinforcement-learning safety

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1d ago

Interval POMDP Shielding for Imperfect-Perception Agents

arXiv:2604. 20728v2 Announce Type: replace Abstract: Autonomous systems that rely on learned perception can make unsafe decisions when sensor readings are misclassified.

By William Scarbro, Ravi Mangal

agents reinforcement-learning benchmarks safety

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Jun 3

Easy-to-Use Shielding for Reinforcement Learning

arXiv:2606. 03804v1 Announce Type: new Abstract: Safe exploration is a key challenge in Reinforcement Learning (RL) that aims to prevent agents from making harmful decisions while exploring their environment.

By Stefan Pranger, Bettina K\"onighofer

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Jun 16

Safe Exploration via Policy Priors

arXiv:2601. 19612v3 Announce Type: replace-cross Abstract: Safe exploration is a key requirement for reinforcement learning (RL) agents to learn and adapt online, beyond controlled (e.

By Manuel Wendl, Yarden As, Manish Prajapat, Anton Pollak, Stelian Coros, Andreas Krause

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Jun 2

All Models are Wrong, Knowing Where is Useful: On Model Uncertainty in Reinforcement Learning

arXiv:2606. 01363v1 Announce Type: new Abstract: Model-based reinforcement learning (MBRL) infers information about the environment from a learned dynamics model and bears the potential to address open problems such as data efficient and safe learning in robotics.

By Bernd Frauenknecht, Devdutt Subhasish, Artur Eisele, Friedrich Solowjow, Sebastian Trimpe

agents reinforcement-learning robotics

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