Function Approximation in RL

RL: Reinforcement Learning

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28 papers3 papers in the last four weeks, level with the four weeks before. 0.0% of all new papers.

  1. Permanent and Transient Representations for Continual Reinforcement Learning

    Nov 5, 2026Nishanth Anand, Doina PrecupComplementary Learning SystemsContinual Learning

    School of Computer Science, McGill University and Mila · School of Computer Science, McGill University, Mila, and Google DeepMind

  2. Finite-Time Convergence and Regret Guarantees for Neural Reinforcement Learning in POMDPs

    Nov 4, 2026Jingjuan Huang, Zhiyao Zhang, Alvaro Velasquez +2Partially Observable RLRegret Minimization in RL

    Ohio State University · University of Colorado at Boulder · Johns Hopkins University

  3. On the Feature Evolution of Temporal Difference Learning

    Nov 3, 2026Shangzhe Li, Dechen Zhang, Difan Zou +1Temporal-Difference LearningRepresentation Learning for RL

    University of North Carolina at Chapel Hill · The University of Hong Kong

  4. Bridging Neural Tangent Kernel Analysis and Reinforcement Learning for Deep Function Approximation

    Nov 2, 2026William St-Arnaud, Margarida Carvalho, Golnoosh FarnadiNeural Tangent KernelNeural Network Training Dynamics

    Mila, Université de Montréal · Mila, McGill University

  5. Minimax Regret for Sobolev Kernel-Based Reinforcement Learning

    Oct 25, 2026Haichen Hu, David Simchi-LeviRegret Minimization in RLReinforcement Learning

    Center for Computational Science and Engineering, MIT · Department of Civil and Environmental Engineering, MIT · Institute for Data, Systems, and Society, MIT

  6. Toward Online Robust Zero-Sum Markov Games with Function Approximation

    Oct 19, 2026Chenhao Zhou, Jiyu WeiRobust RLZero-Sum Games

    College of Computer Science and Technology, Zhejiang University

  7. Sample-Efficient Reinforcement Learning with General Utilities via Neural Dual Critics

    Oct 17, 2026Ankur Naskar, Vaneet AggarwalReinforcement LearningSample-Efficient RL

    Indian Institute of Science, India · Purdue University, USA

  8. Bellman Error Minimization Via Linear Programming Normalization

    Oct 2, 2026Haining YuFunction Approximation in RLDynamic Programming

    Independent Consultant

  9. Last-Iterate Guarantees for Online Reinforcement Learning in Structured Constrained MDPs

    Sep 26, 2026Nam Phuong Tran, Trinh Ha Mai Huynh, Tuyen Pham Le +3Primal-Dual RLFunction Approximation in RL

    Inria centre at the University of Lille Lille, France · Vin Motion Vietnam · Department of Computer Science University of Warwick Coventry, United Kingdom

  10. Interactive Distributionally Robust Multi-Agent Learning with General Function Approximation

    Sep 25, 2026Debamita Ghosh, George K. Atia, Yue WangFunction Approximation in RLReinforcement Learning

    Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816, USA · Department of Computer Science, University of Central Florida, Orlando, FL 32816, USA

  11. Q-Learning with Stable Infinite-Dimensional Linear Function Approximation

    Aug 23, 2026Shengbo WangQ-LearningFunction Approximation in RL

    Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California

  12. Mathematical methods of reinforcement learning

    Jul 8, 2026Denis Belomestny, Alexander Gasnikov, Egor Gladin +5Markov Decision ProcessesReinforcement Learning

    Duisburg-Essen University, HSE University · Innopolis University, MIPT, HSE University · HSE University +1

  13. A Diffusion Approximation for Temporal-Difference Learning with Linear Features under Markovian Noise

    Jun 16, 2026M. Forzo, E. Monzio Compagnoni, A. Russo +1Stochastic Differential EquationsTemporal-Difference Learning

    Technical University of Munich (TUM), Munich, Germany · University of Basel, Basel, Switzerland · Boston University, Boston, USA.

  14. Variance-Adaptive Optimal Algorithm for Reinforcement Learning with Multinomial Logit Function Approximation

    May 27, 2026Wonyoung Kim, Min-Hwan Oh, Garud Iyengar +1Regret Minimization in RLReinforcement Learning

    Chung-Ang University · Seoul National University · Columbia University

  15. Global linear convergence of entropy-regularized softmax policy gradient beyond tabular MDPs

    May 24, 2026Ziyue Chen, David Šiška, Lukasz SzpruchPolicy GradientReinforcement Learning

  16. On Gaussian approximation for entropy-regularized Q-learning with function approximation

    May 17, 2026Artemy Rubtsov, Rahul Singh, Eric Moulines +2Reinforcement LearningStochastic Approximation

    HSE University, Moscow, Russia · Mohamed Bin Zayed University of AI, UAE · EPITA, France +1

  17. Behavior-Aware Auxiliary Corrections for Off-Policy Temporal-Difference Prediction

    May 17, 2026Xingguo Chen, Zhiang He, Yuchen Shen +4Temporal-Difference LearningLinear Function Approximation

    Nanjing University of Posts and Telecommunications, Nanjing, China · Department of Computer Science and Technology, Nanjing University, Nanjing, China · College of Electronic Countermeasure, National University of Defense Technology, Hefei, China

  18. Towards Differentially Private Reinforcement Learning with General Function Approximation

    May 7, 2026Yi He, Xingyu ZhouRegret Minimization in RLReinforcement Learning

    Wayne State University, Detroit, USA

  19. Reinforcement Learning with Markov Risk Measures and Multipattern Risk Approximation

    May 1, 2026Andrzej Ruszczynski, Tiangang ZhangMarkov Decision ProcessesReinforcement Learning

    Department of Management Science and Information Systems, Rutgers University

  20. Provably Efficient Offline-to-Online Value Adaptation with General Function Approximation

    Apr 15, 2026Shangzhe Li, Weitong ZhangOffline-To-Online RLSample-Efficient RL

    UNC Chapel Hill

  21. Contraction-Aligned Analysis of Soft Bellman Residual Minimization with Weighted Lp-Norm for Markov Decision Problem

    Apr 8, 2026Hyukjun Yang, Han-Dong Lim, Donghwan LeeFunction Approximation in RLMarkov Decision Processes

    the Department of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea

  22. End-to-End Efficient RL for Linear Bellman Complete MDPs with Deterministic Transitions

    Mar 24, 2026Zakaria Mhammedi, Alexander Rakhlin, Nneka OkoloReinforcement LearningLinear Markov Decision Processes

    Google Research, NYC · MIT

  23. Impact of Connectivity on Laplacian Representations in Reinforcement Learning

    Mar 9, 2026Tommaso Giorgi, Pierriccardo Olivieri, Keyue Jiang +2Reinforcement LearningRepresentation Learning for RL

    Baker Hughes, Florence, Italy · Politecnico di Milano · University College London, AI Center (UCL) +1

  24. Q-Measure-Learning for Continuous State RL: Efficient Implementation and Convergence

    Mar 3, 2026Shengbo WangReinforcement LearningQ-Learning

    Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California

  25. PAC Guarantees for Reinforcement Learning: Sample Complexity, Coverage, and Structure

    Mar 1, 2026Joshua SteierFunction Approximation in RLReinforcement Learning

  26. Functional Critics for Off-Policy Actor-Critic: From Stability to Exploration

    Sep 26, 2025Qinxun Bai, Yuxuan Han, Yuxiao Wen +2Actor-Critic MethodsOff-Policy RL

    Isara Laboratories. · Stern School of Business, New York University. · Horizon Robotics.

  27. Adaptive Resolving Methods for Markov Decision Processes with Function Approximations

    May 17, 2025Jiashuo Jiang, Yinyu Ye, Yiming ZongMarkov Decision ProcessesReinforcement Learning

    Department of Industrial Engineering & Decision Analytics, The Hong Kong University of Science and Technology · Department of Management Science & Engineering, Stanford University

  28. Statistical Inference for Policy Evaluation with Temporal Difference Learning

    Oct 21, 2024Weichen Wu, Gen Li, Yuting Wei +1Parameter EstimationConfidence Region Estimation

    The Voleon Group, New York, NY, USA · Department of Statistics, The Chinese University of Hong Kong, Hong Kong SAR. · Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA. +1