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11 papers in the last four weeks, up 175% on the four weeks before. 0.1% of all new papers.

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Q-Learning for Reachability in MEC-Free MDPs
Oct 1, 2026Lu-Chin Chang, Suguman BansalQ-Learning Markov Decision Processes
Georgia Institute of Technology
Persistent Partners Raise Prices Among Learning Agents
Sep 28, 2026Paul-Peter Arslan, Yubin Kim, Xiao XiaoDynamic Pricing Prices
Institute For Future Technologies Devinci Higher Education · Massachusetts Institute of Technology
Q-learning Penalized Transformer for Safe Offline Reinforcement Learning
Sep 28, 2026Shengchao Hu, Peng Wang, Jifeng Hu +5Model-Based Reinforcement Learning Q-Learning
Shanghai Jiao Tong University, China · Shenzhen Campus of Sun Yat-sen University, China · Jilin University, China +1
Right Choice of Classification Algorithms Based on Reinforcement Learning for Prediction of Non-Alcoholic Fatty Liver
Sep 24, 2026Hasan Samadbin, Arman DaliriCholangiocarcinoma Classifier
Department of Computer Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran
Offline Reinforcement Learning for Distribution-Grid Protection
Sep 21, 2026Julian Oelhaf, Alexander Luce, Christian Bergler +2Power Systems Q-Learning
Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen–Nürnberg, Erlangen, Germany · Department of Electrical Engineering, Media and Computer Science, Ostbayerische Technische Hochschule Amberg-Weiden, Amberg, Germany
Mitigating Retaliatory Algorithmic Collusion in Repeated Games
Sep 17, 2026Karthik Sivachandran, Rohan PalejaCollusion Q-Learning
Department of Computer Science Purdue University
APGEM: Adaptive Policy-Guided Error Mitigation for Quantum Reinforcement Learning on a Real-World CVRP Case Study
Sep 16, 2026Shabir Ahmad Sofi, Bisma Majid, Mir Mohammad YousufVariational Quantum Circuits Q-Learning
Department of Information Technology NIT Srinagar J&K, India
Towards Surrogate Based Dequantization of Quantum Reinforcement Learning
Sep 14, 2026Pablo Rodriguez-Grasa, Sofiene Jerbi, Mikel Sanz +1Q-Learning Gradient Quantization
Department of Physical Chemistry, University of the Basque Country UPV/EHU, Apartado 644, 48080 Bilbao, Spain · TECNALIA, Basque Research and Technology Alliance (BRTA), 48160 Derio, Spain · Dahlem Center for Complex Quantum Systems, Freie Universität Berlin, 14195 Berlin, Germany +7
Adaptive Agent Design
Sep 14, 2026Raj Kiriti Velicheti, Subhonmesh Bose, Tamer BaşarQ-Learning Markov Decision Processes
Department of Electrical and Computer Engineering and the Coordinated Science Laboratory at the University of Illinois, Urbana-Champaign, Urbana, IL 61801
SCQ: Stabilizing Conservative Q-Learning with Sigmoid-Bounded Entropy
Sep 14, 2026Xiefeng Wu, Shu Zhang, Zhaojie Chu +1Q-Learning Entropy Regularized Reinforcement Learning
School of Computer Science, Wuhan University, Wuhan, China · School of Electronic Information, Wuhan University, Wuhan, China · School of Internet, Anhui University, Hefei, China
Decentralized Safe Multi-Agent Reinforcement Learning via Predictive Shielding
Sep 7, 2026Yacine El Yamani, Hanna Krasowski, Elena VanneauxMulti-Agent Reinforcement Learning Multi-Robot Systems
ENSTA, IP Paris, Palaiseau, France · UC Berkeley, USA
Revisiting Overestimation Bias Problem of Q-learning: Settling Large Discrete Action Space via Action Intersection
Aug 13, 2026Pu Li, Tao Tan, Hong Xie +2Q-Learning Action Space
Chongqing Institute of Green and Intelligent Technology, Chongqing, China · University of Science and Technology of China, Hefei, Anhui, China
Decentralized Multi-Player Q-Learning in Episodic Markov Decision Processes with Information Asymmetry
Aug 13, 2026Larissa Xu, King Bi, William ChangQ-Learning Multi-Agent Reinforcement Learning
Department of Mathematics University of California, Los Angeles Los Angeles, USA · Department of Computer Science University of California, Los Angeles Los Angeles, USA
Language-Structured Relational Q-Learning for Threat-Aware Control in Safety-Critical Driving
Aug 11, 2026Aditya Humnabadkar, Huaizhong Zhang, Ardhendu BeheraQ-Learning Trajectory-Level Credit
Department of Computer Sciences, Edge Hill University, United Kingdom
Dueling Deep Q-Learning for Intrusion Detection
Aug 11, 2026Logan Luna, Matthew P. Berkowitz, Laxima Niure Kandel +1Intrusion Detection Deep Q-Networks
Georgia Institute of Technology · Department of Electrical Engineering and Computer Science Embry-Riddle Aeronautical University Daytona Beach, USA · School of Computer Science, College of Computing, Georgia Institute of Technology, Atlanta, GA 30332 USA +1
Adaptive Finite-Budget Training for CVaR Risk-Aware Q-Learning
Aug 5, 2026Yifan Wu, Junjie Lei, Wenjie HuangConditional-Value-At-Risk Q-Learning
Department of Data and Systems Engineering The University of Hong Kong Hong Kong, Hong Kong SAR
Revisiting TD Target Aggregation under Uncertainty in Q-Learning
Aug 4, 2026Lipeng Zu, Xiaonan ZhangDeep Q-Networks Q-Learning
Computer Science Department, Florida State University, Tallahassee, Florida, USA
Improved Quantum Algorithms for Reinforcement Learning Under a Generative Model
Aug 3, 2026Joao F. DoriguelloQ-Learning Optimal Policies
HUN-REN Alfr´ed R´enyi Institute of Mathematics, Budapest, Hungary
Upper-Expectile Multi-Step Q-Learning for Off-Policy Reinforcement Learning
Aug 3, 2026Abdelghani Ghanem, Mounir GhoghoQ-Learning Value Functions
College of Computing Mohammed VI Polytechnic University Rabat 11103, Morocco
Gated Q-learning: Add Off-Policy Bias to Taste
Jul 31, 2026Brett DaleyQ-Learning Off-Policy Learning
Meta
Robust Asynchronous Q-Learning under Reward and State Corruption via Batching
Jul 23, 2026Sreejeet Maity, Aritra MitraQ-Learning Adversarial Corruption
Department of Electrical and Computer Engineering, North Carolina State University.
Equilibrium stability as a driver of cooperation among Q-learners
Jul 15, 2026Janusz M. Meylahn, Maximilian SchäferCollusion Cooperation
University of Twente, Department of Applied Mathematics, Drienerloolaan 5, 7522 NB Enschede, The Netherlands. · Institut Mines-Télécom Business School, Department of Data Analytics, Economics, and Finance. 9 Rue Charles Fourier, 91000 Evry, France.
Q-Learning Lab: Teaching Reinforcement Learning Through Learner-Generated Trace Analysis
Jul 12, 2026Ekkachai JuengQ-Learning Offline Reinforcement Learning
Computer Science Program Faculty of Sciences and Liberal Arts Rajamangala University of Technology Isan Nakhon Ratchasima, Thailand
Spectral Analysis of Dueling Q-Learning
Jul 9, 2026Donghwan LeeQ-Learning Deep Q-Networks
Department of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST) Daejeon 34141, South Korea
Hybridizing a Grouping Metaheuristic with Reinforcement Learning for the One-Dimensional Bin Packing Problem
Jul 2, 2026Zitouni Rania, Mostefai Mounir Sofiane, Tati Youcef +3Knapsack Constraint Metaheuristics
Square Two Team – Optimization Module, École Nationale Supérieure d’Informatique (ESI), Algiers, Algeria
DiPS: Dialogue Policy Selection for High-Stakes Persuasion Agents
Jul 2, 2026Tianyi Zhang, Mousumi Das, Abrar Anwar +2Persuasion Large Language Model Policy Optimization
University of Southern California
Accelerating Q-learning through Efficient Value-Sharing across Actions
Jun 29, 2026Prabhat Nagarajan, Brett Daley, Martha White +1Deep Q-Networks Q-Learning
Department of Computing Science, University of Alberta, Edmonton, AB, Canada. · Alberta Machine Intelligence Institute · Meta (independent work, not on Meta’s behalf). +1
Reinforcement Learning in Super Mario Bros: Curriculum, Pedagogy, and Optimal Level Design in World 1-1
Jun 28, 2026Jesse Ponnock, Lucas HoCurriculum Reinforcement Learning Q-Learning
Johns Hopkins University
Heavy-Ball Q-Learning with Residual Weighting Correction
Jun 25, 2026Donghwan LeeQ-Learning Correction
Department of Electrical Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon 34141, South Korea
Mesh-RL: Coupled subgrid reinforcement learning
Jun 24, 2026Behnam Gheshlaghi, Bahador Rashidi, Shahin AtakishiyevTemporal Difference Q-Learning
Independent Researcher · University of Alberta
NASDAQ: Normalized Observation Space Dynamics-Augmented Q-Learning
Jun 19, 2026Xinwei Liu, Junyuan Liang, Zicong Hong +2Model-Free Reinforcement Learning Q-Learning
Sun Yat-sen University, China · EPFL, Switzerland · Purdue University, USA
Robust $Q$ -learning for mean-field control under Wasserstein uncertainty in common noise
Jun 18, 2026Mathieu Laurière, Ariel Neufeld, Kyunghyun ParkStochastic Optimal Control Q-Learning
NYU Shanghai HPC · MOE AcRF Tier 1 Grant RG109/25 · National Research Foundation of Korea
Pareto Q-Learning with Reward Machines
Jun 17, 2026Arnaud Lequen, Clément Legrand-Lixon, Léo SaulièresMulti-Objective Reinforcement Learning Q-Learning
Linköping University, Sweden · Univ. Lille, CNRS, Centrale Lille, UMR 9189 CRIStal, F-59000 Lille, France · Univ. Toulouse, INRAE-MIAT, Toulouse, France
Reinforcement Learning Foundation Models Should Already Be A Thing
Jun 17, 2026Abdelrahman Zighem, Jill-Jênn VieTabular Foundation Models Wireless Foundation Models
Ecole normale supérieure de Paris, PSL University, Paris, France · Soda team, Inria Saclay, Palaiseau, France.
Quantum Annealing Enhanced Reinforcement Learning for Accurate Remaining Useful Lifetime Prediction
Jun 16, 2026Manoranjan Gandhudi, Arunkumar V., G. R. Anil +1Quantum Annealing Useful Life Prediction
Central University of Karnataka, Kalaburagi, India · University College of Engineering, Anna University, Tiruchirappalli, Tamil Nadu, India · AIONOS India Pvt Ltd, Hyderabad, India +1
Reversal Q-Learning
Jun 16, 2026Aditya Oberai, Seohong Park, Sergey LevineFlow Policies Model-Based Reinforcement Learning
University of California, Berkeley.
Deep Q-Learning on Hölder Spaces
Jun 15, 2026Qian QiQ-Learning Deep Q-Networks
Peking University
Diffusion Offline Reinforcement Learning for Fair and Energy-Efficient UAV-Assisted Wireless Networks
Jun 15, 2026Eslam Eldeeb, Hirley AlvesDiffusion-Based Reinforcement Learning Methods Offline Reinforcement Learning
Centre for Wireless Communications (CWC), University of Oulu, Finland
QPILOTS: Efficient Test-Time Q-Steering for Flow Policies
Jun 11, 2026Yifan Ruan, Chenyang Cao, Andreas Burger +7Flow Policies Q-Learning
University of Toronto · Vector Institute · LG Electronics
Geometrically Averaged Hard Target Updates for Linear Q-Learning
Jun 9, 2026Donghwan LeeQ-Learning Linear Quadratic Regulator
School of Electrical Engineering, KAIST · Daejeon, Republic of Korea
Fast and Highly Expressive Policy Learning for Offline Reinforcement Learning via Bootstrapped Flow Q-Learning
Jun 9, 2026Thanh Nguyen, Tri Ton, Hongbin Choe +2Diffusion-Based Reinforcement Learning Methods Flow Policies
Department of Electrical Engineering, KAIST, Daejeon, Korea.
QnRL: Quantum-Native Reinforcement Learning
Jun 6, 2026Alexander DeRieux, Walid SaadQuantum Learning Q-Learning
Bradley Department of Electrical and Computer Engineering, Virginia Tech Institute for Advanced Computing, Alexandria, VA 22305 USA
COP-Q: Safety-First Reinforcement Learning for Robot Control via Cholesky-Ordered Projection
Jun 3, 2026Guopeng Li, Moritz A. Zanger, Matthijs T. J. Spaan +1Safety Constraints Q-Learning
Department of Cognitive Robotics, Delft University of Technology · School of Transportation, Southeast University, China · Department of Intelligent Systems, Delft University of Technology, the Netherlands
Autopilot-Preserving Residual Q-Learning with HJB-Inspired Finite-Action Risk Filtering for Fixed-Wing UAV Command Supervision
May 31, 2026Mehmet Iscan, Batuhan TemizResidual Policy Unmanned Aerial Vehicles
PythaLab, Yildiz Technical University, Istanbul, Turkey · Autopilot Systems Chief Engineer, Turkish Aerospace (TUSAŞ), Ankara, Turkey
Target Updates May Stabilize Linear Q-Learning: Periodic and Soft Dynamics
May 31, 2026Donghwan LeeQ-Learning Linear Quadratic Regulator
School of Electrical Engineering, KAIST, Daejeon, Republic of Korea
Delayed Repression and Emergent Instability in Adaptive Multi-Agent Systems
May 28, 2026Igor ItkinModel-Based Multi-Agent Systems Q-Learning
Independent Researcher, Tel Aviv, Israel
Moment Matching Q-Learning
May 27, 2026Yiyan, Liang, Sifei Liu +1Q-Learning Generative Flow Networks
Edgar · School of Data and Information Science, University of North Carolina at Chapel Hill, Chapel Hill, USA.
Return-to-Go Is More Than a Number: Q-Guided Alignment for Return-Conditioned Supervised Learning
May 27, 2026Yuxiao Yang, Weitong ZhangQ-Learning Sequence Modeling
University of North Carolina at Chapel Hill
Commit to the Bit: Reactive Reinforcement Learning Done Right
May 27, 2026Onno Eberhard, Claire Vernade, Michael MuehlebachQ-Learning Markov Decision Processes
Max Planck Institute for Intelligent Systems, Tübingen, Germany · University of Tübingen · University of Technology Nuremberg.
Trust Region Q Adjoint Matching
May 26, 2026Yonghoon Dong, Kyungmin Lee, Changyeon Kim +2Flow Policies Q-Learning
1KAIST AI · 2Seoul National University · 3RLWRLD
ReversedQ: Opportunities for Faster Q-Learning in Episodic Online Reinforcement Learning
May 20, 2026Sofia R. Miskala-Dinc, Aviva PrinsQ-Learning Model-Free Reinforcement Learning
University of Maryland College Park, MD, United States
Learning to Hand Off: Provably Convergent Workflow Learning under Interface Constraints
May 18, 2026Jiayu Li, Enpei Zhang, Dawei Zhou +2Q-Learning Multi-Agent Workflows
Stern School of Business · New York University · Department of Computer Science +2
TabQL: In-Context Q-Learning with Tabular Foundation Models
May 18, 2026Qisai Liu, Zhanhong Jiang, Timilehin Ayanlade +4Deep Q-Networks Q-Learning
Department of Mechanical Engineering Iowa State University Ames, IA 50011 · Translational AI Center Iowa State University Ames, IA 50011 · Department of Computer Science Iowa State University Ames, IA 50011 +1
On Gaussian approximation for entropy-regularized Q-learning with function approximation
May 17, 2026Artemy Rubtsov, Rahul Singh, Eric Moulines +2Entropy Regularized Reinforcement Learning Q-Learning
HSE University, Moscow, Russia · Mohamed Bin Zayed University of AI, UAE · EPITA, France +1
Sign-Separated Asymmetric Finite-Time Error Analysis of Q-Learning
May 15, 2026Donghwan LeeQ-Learning Value Functions
Department of Electrical Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon 34141, South Korea
Clock-state olfactory search in turbulent flows using Q-learning: The geometry of plume recovery
May 15, 2026Marco Rando, Robin A. Heinonen, Yujia Qi +1Q-Learning Smells
Universit´e Cˆote d’Azur, Inria, CNRS, Laboratoire J.A. Dieudonn´e, 28 avenue Valrose, 06108, Nice, France · Machine Learning Genoa Center & Department of Civil, Chemical and Environmental Engineering, University of Genova Via Montallegro 1, 16145 Genoa, Italy · Machine Learning Genoa Center & Department of Civil, Chemical and Environmental Engineering, University of Genova, Via Montallegro 1, 16145 Genoa, Italy +1
QuantFPFlow: Quantum Amplitude Estimation for Fokker--Planck Policy Optimisation in Continuous Reinforcement Learning
May 14, 2026Abraham Itzhak WeinbergQuantum Annealing Flow Policies
AI-WEINBERG, AI Experts, Tel Aviv, Israel
ROAD: Adaptive Data Mixing for Offline-to-Online Reinforcement Learning via Bi-Level Optimization
May 14, 2026Letian Yang, Xu Liu, Yiqiang Lu +3Q-Learning Model Fine-Tuning
Shanghai Jiao Tong University, Shanghai, China · Ant Group, Shanghai, China
Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration
May 12, 2026Youwei Yu, Jionghao Wang, Zhengming Yu +2Bayesian Experimental Design Exploration
Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA · Department of Computer Science & Engineering, Texas A&M University, College Station, TX, USA
Debiased Model-based Representations for Sample-efficient Continuous Control
May 12, 2026Jiafei Lyu, Zichuan Lin, Scott Fujimoto +5Q-Learning Latent Dynamics
1Tencent Hunyuan · 2McGill University · School of Computer

Oct 1, 2026, cs.AIJ/K move · Enter open · S save

Q-Learning for Reachability in MEC-Free MDPs

Lu-Chin Chang, Suguman Bansal

Georgia Institute of Technology

Q-Learning Markov Decision Processes Reachability Model-Free Offline Reinforcement Learning

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Reinforcement learning (RL) for reachability specifications is fundamental to sequential decision-making. Prior work establishes asymptotic convergence to optimal policies, but only through model-based methods that must explicitly estimate the transition probabilities of the underlying Markov Decision Process (MDP). We present Quasar, the first model-free algorithm with asymptotic guarantees for reachability on the fragment of MDPs free of non-terminal maximal end components (MECs), a building block to which every MDP reduces by the standard MEC quotient. Our algorithm follows the classical Q-learning approach, using temporal-difference updates to converge to an optimal policy without ever learning the transition probabilities. The resulting learner reduces the memory footprint from the O(|S|^2|A|) that model-based methods require to O(|S||A|). On the standardized Quantitative Verification Benchmark Set, our algorithm converges to the optimal policy with orders of magnitude fewer samples than the previous model-based state-of-the-art. Together these results are a concrete step toward the practical deployment of reachability learning and, with it, of specification-guided RL.

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