Non-Markovian RL

RL: Reinforcement Learning

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13 papers3 papers in the last four weeks, against 2 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 BelardinelliSafe RL Formal Methods for RL
Department of Computer Science Imperial College London
Reinforcement Learning with Hawkes-Structured Networks for Market Making
Oct 12, 2026Fernando Acero, Philipp Plank, Yufei ZhangHawkes Processes Non-Markovian RL
Department of Mathematics, Imperial College London, United Kingdom
Exact Distinguishability in Non-Markovian Decision Processes
Oct 1, 2026Kabir Murjani, Nisarg PatelNon-Markovian RL
Nirma University Ahmedabad, India · Google Mountain View, CA
Adaptive Agent Design
Sep 11, 2026Raj Kiriti Velicheti, Subhonmesh Bose, Tamer BaşarReinforcement Learning Q-Learning
Department of Electrical and Computer Engineering and the Coordinated Science Laboratory at the University of Illinois, Urbana-Champaign, Urbana, IL 61801
Temporal-Causal Inference for Reinforcement Learning via Automata Learning
Sep 7, 2026Jan Corazza, Daniil Kaminskyi, Simon Lutz +4Reinforcement Learning Automata Learning
Research Center Trustworthy Data Science and Security, TU Dortmund University, Dortmund, 44227, Germany. · School for Engineering of Matter, Transport, and Energy at Arizona State University, Tempe, Az 85281, USA.
Pareto Q-Learning with Reward Machines
Jun 17, 2026Arnaud Lequen, Clément Legrand-Lixon, Léo SaulièresMulti-Objective Reinforcement Learning Reinforcement Learning
Linköping University, Sweden · Univ. Lille, CNRS, Centrale Lille, UMR 9189 CRIStal, F-59000 Lille, France · Univ. Toulouse, INRAE-MIAT, Toulouse, France
Commit to the Bit: Reactive Reinforcement Learning Done Right
May 27, 2026Onno Eberhard, Claire Vernade, Michael MuehlebachReinforcement Learning Partially Observable RL
Max Planck Institute for Intelligent Systems, Tübingen, Germany · University of Tübingen · University of Technology Nuremberg.
Policy Gradient Methods for Non-Markovian Reinforcement Learning
May 11, 2026Avik Kar, Siddharth Chandak, Rahul Singh +4Reinforcement Learning Policy Gradient Methods
Department of Computer Science and Automation, Indian Institute of Science, Bengaluru, India. · Department of Electrical Engineering, Stanford University, Stanford, CA, USA. · Department of Electrical and Electronics Engineering, Nanyang Technological University, Singapore. +1
Live LTL Progress Tracking: Towards Task-Based Exploration
Apr 18, 2026Noel Brindise, Cedric Langbort, Melkior OrnikReinforcement Learning Linear Temporal Logic
Coordinated Science Laboratory, University of Illinois Urbana-Champaign, Urbana, USA
Anticipatory Reinforcement Learning: From Generative Path-Laws to Distributional Value Functions
Apr 6, 2026Daniel BlochNon-Markovian RL Reinforcement Learning
UNIVERSITY OF PARIS 6 & VINUNIVERSITY · Visting Professor at the College of Engineering and Computer Science, VinUniversity, Hanoi.
Prediction-Based Markov Violation Scores for Detecting Non-Markovian Observations in Reinforcement Learning
Mar 28, 2026Naveen MysoreReinforcement Learning Non-Markovian RL
Department of Electrical and Computer Engineering, University of California, Santa Barbara, USA
Reinforcement Learning with Symbolic Reward Machines
Mar 3, 2026Thomas Krug, Daniel NeiderNon-Markovian RL Reinforcement Learning
TU Dortmund University · Research Center Trustworthy Data Science and Security
Reinforcement Learning for Long-Horizon Unordered Tasks: From Boolean to Coupled Reward Machines
Oct 31, 2025Kristina Levina, Nikolaos Pappas, Athanasios Karapantelakis +2Reinforcement Learning Q-Learning
Linköping University, Linköping, Sweden · Ericsson Research, Stockholm, Sweden

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

Alexander W. Goodall, Francesco Belardinelli

Department of Computer Science Imperial College London

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.