RL Shielding

RL: Reinforcement Learning

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14 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

RECREG: Recovery-based Shielding for Regular Safety Properties in Reinforcement Learning
Oct 16, 2026Alexander W. Goodall, Francesco BelardinelliRL Shielding Safe RL
Department of Computer Science Imperial College London
Decentralized Safe Multi-Agent Reinforcement Learning via Predictive Shielding
Sep 7, 2026Yacine El Yamani, Hanna Krasowski, Elena VanneauxMulti-Robot Coordination Robot Safety
ENSTA, IP Paris, Palaiseau, France · UC Berkeley, USA
Adaptive Probabilistic Shielding by Learning MDPs for Safe Reinforcement Learning
Aug 20, 2026Astrid Horn Brorholt, Maris F. L. Galesloot, Nils Jansen +2Safe RL RL Shielding
Aalborg University, Aalborg, Denmark · Radboud University, Nijmegen, Netherlands · Ruhr University Bochum, Bochum, Germany
Adjustment Speed as a Safety Constraint for Nonstationary Reinforcement Learning
Jul 21, 2026Timothy TomashevskiyNon-Stationary RL Safe RL
Department of Computing and Software McMaster University Hamilton, Ontario, Canada
Contract-Based Compositional Shielding for Safe Multi-Agent Reinforcement Learning
Jun 12, 2026Omar Adalat, Edwin Hamel-De le Court, Francesco BelardinelliMulti-Agent Coordination Formal Verification
Imperial College London, London SW7 2AZ, UK · University of Manchester, Manchester M13 9PL, UK
Shield-Loco: Shielding Locomotion Policies with Predictive Safety Filtering
Jun 5, 2026Aditya Shirwatkar, Sebastian Sanokowski, Shishir Kolathaya +2Robot Safety Safety Filtering
Robert Bosch Center for Cyber Physical Systems, Indian Institute of Science, Bangalore, India · Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich, Munich, Germany · Department of Computer Science & Automation, Indian Institute of Science, Bangalore, India +2
Explainably Safe Reinforcement Learning
Jun 3, 2026Sabine Rieder, Stefan Pranger, Debraj Chakraborty +2Reinforcement Learning Interpretability in RL
Masaryk University · Technical University of Munich · Graz University of Technology
Easy-to-Use Shielding for Reinforcement Learning
Jun 2, 2026Stefan Pranger, Bettina KönighoferFormal Methods for RL Reinforcement Learning
Institute of Information Security, Graz University of Technology
Robust Shielding for Safe Reinforcement Learning
May 29, 2026Edwin Hamel-De le Court, Thom Badings, Alessandro Abate +2Robust Markov Decision Processes Formal Verification
Department of Computer Science, University of Manchester, United Kingdom · Faculty of Computer Science & DSME, RWTH Aachen University, Germany · Department of Computer Science, University of Oxford, United Kingdom +1
Latent Q-Barrier Shielding for Safe In-Context Reinforcement Learning
May 24, 2026Minjae Kwon, Amir Moeini, Shangtong Zhang +1Reinforcement Learning Control Barrier Functions
University of Virginia
Learning When to Act: Communication-Efficient Reinforcement Learning via Run-Time Assurance
May 11, 2026Adam Haroon, Erick J. Rodríguez-Seda, Cody Fleming +1RL Control Lyapunov-Based Control
Department of Mechanical Engineering, Iowa State University, Ames, IA, USA · Navy Center for Applied Research in Artificial Intelligence (NCARAI), U.S. Naval Research Laboratory, Washington, D.C., USA · Department of Weapons, Robotics, and Control Engineering, United States Naval Academy, Annapolis, MD, USA
Robust Probabilistic Shielding for Safe Offline Reinforcement Learning
May 11, 2026Maris F. L. Galesloot, Thomas Rhemrev, Nils JansenOffline RL Safe RL
Radboud University · Nijmegen, The Netherlands · Ruhr University & Radboud University +1
Worst-Case Discovery and Runtime Protection for RL-Based Network Controllers
May 6, 2026Hongyu Hè, Minhao Jin, Maria ApostolakiRL Control Safe RL
Princeton
Safe Reinforcement Learning via Recovery-based Shielding with Gaussian Process Dynamics Models
Feb 12, 2026Alexander W. Goodall, Francesco BelardinelliRL Shielding Safe RL
Imperial College London, Department of Computing London, United Kingdom

Oct 16, 2026J/K move · Enter open · S save

RECREG: Recovery-based Shielding for Regular Safety Properties in Reinforcement Learning

Alexander W. Goodall, Francesco Belardinelli

Department of Computer Science Imperial College London

RL Shielding Safe RL Formal Methods for RL Non-Markovian RL

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In many real-world scenarios we need to consider formal requirements, such as safety and constraint compliance, to deploy Reinforcement Learning (RL) agents. In this paper we introduce RecReg, a safe RL framework capable of enforcing non-Markovian regular safety properties via recovery-based shielding. RecReg is a unified approach that supports different levels of a priori knowledge and safety guarantees. The paper primarily focuses on building the theoretical foundations for RecReg in the tabular setting, providing rigorous safety and convergence guarantees that are not present in related work. We provide an extensive set of empirical results using the MASA-Safe-RL library, and we outline an extension of RecReg to deep RL domains using SOTA model-based RL. Our empirical results demonstrate that RecReg achieves efficient learning and matches the theory in this paper.