Function Approximation in RL

RL: Reinforcement Learning

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20 papers3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Bellman Error Minimization Via Linear Programming Normalization
Oct 2, 2026Haining YuFunction Approximation in RL Dynamic Programming
Independent Consultant
Last-Iterate Guarantees for Online Reinforcement Learning in Structured Constrained MDPs
Sep 26, 2026Nam Phuong Tran, Trinh Ha Mai Huynh, Tuyen Pham Le +3Function Approximation in RL Reinforcement Learning
Inria centre at the University of Lille Lille, France · Vin Motion Vietnam · Department of Computer Science University of Warwick Coventry, United Kingdom
Interactive Distributionally Robust Multi-Agent Learning with General Function Approximation
Sep 25, 2026Debamita Ghosh, George K. Atia, Yue WangFunction Approximation in RL Reinforcement 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
Q-Learning with Stable Infinite-Dimensional Linear Function Approximation
Aug 23, 2026Shengbo WangQ-Learning Function Approximation in RL
Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California
Mathematical methods of reinforcement learning
Jul 8, 2026Denis Belomestny, Alexander Gasnikov, Egor Gladin +5Markov Decision Processes Reinforcement Learning
Duisburg-Essen University, HSE University · Innopolis University, MIPT, HSE University · HSE University +1
A Diffusion Approximation for Temporal-Difference Learning with Linear Features under Markovian Noise
Jun 16, 2026M. Forzo, E. Monzio Compagnoni, A. Russo +1Stochastic Differential Equations Temporal-Difference Learning
Technical University of Munich (TUM), Munich, Germany · University of Basel, Basel, Switzerland · Boston University, Boston, USA.
Variance-Adaptive Optimal Algorithm for Reinforcement Learning with Multinomial Logit Function Approximation
May 27, 2026Wonyoung Kim, Min-Hwan Oh, Garud Iyengar +1Regret Minimization in RL Reinforcement Learning
Chung-Ang University · Seoul National University · Columbia University
Global linear convergence of entropy-regularized softmax policy gradient beyond tabular MDPs
May 24, 2026Ziyue Chen, David Šiška, Lukasz SzpruchPolicy Gradient Reinforcement Learning
On Gaussian approximation for entropy-regularized Q-learning with function approximation
May 17, 2026Artemy Rubtsov, Rahul Singh, Eric Moulines +2Reinforcement Learning Stochastic Approximation
HSE University, Moscow, Russia · Mohamed Bin Zayed University of AI, UAE · EPITA, France +1
Behavior-Aware Auxiliary Corrections for Off-Policy Temporal-Difference Prediction
May 17, 2026Xingguo Chen, Zhiang He, Yuchen Shen +4Temporal-Difference Learning Linear 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
Towards Differentially Private Reinforcement Learning with General Function Approximation
May 7, 2026Yi He, Xingyu ZhouRegret Minimization in RL Reinforcement Learning
Wayne State University, Detroit, USA
Reinforcement Learning with Markov Risk Measures and Multipattern Risk Approximation
May 1, 2026Andrzej Ruszczynski, Tiangang ZhangMarkov Decision Processes Reinforcement Learning
Department of Management Science and Information Systems, Rutgers University
Provably Efficient Offline-to-Online Value Adaptation with General Function Approximation
Apr 15, 2026Shangzhe Li, Weitong ZhangOffline-To-Online RL Sample-Efficient RL
UNC Chapel Hill
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 RL Markov Decision Processes
the Department of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea
End-to-End Efficient RL for Linear Bellman Complete MDPs with Deterministic Transitions
Mar 24, 2026Zakaria Mhammedi, Alexander Rakhlin, Nneka OkoloReinforcement Learning Linear Markov Decision Processes
Google Research, NYC · MIT
Impact of Connectivity on Laplacian Representations in Reinforcement Learning
Mar 9, 2026Tommaso Giorgi, Pierriccardo Olivieri, Keyue Jiang +2Reinforcement Learning Representation Learning for RL
Baker Hughes, Florence, Italy · Politecnico di Milano · University College London, AI Center (UCL) +1
Q-Measure-Learning for Continuous State RL: Efficient Implementation and Convergence
Mar 3, 2026Shengbo WangReinforcement Learning Q-Learning
Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California
PAC Guarantees for Reinforcement Learning: Sample Complexity, Coverage, and Structure
Mar 1, 2026Joshua SteierFunction Approximation in RL Reinforcement Learning
Adaptive Resolving Methods for Markov Decision Processes with Function Approximations
May 17, 2025Jiashuo Jiang, Yinyu Ye, Yiming ZongMarkov Decision Processes Reinforcement Learning
Department of Industrial Engineering & Decision Analytics, The Hong Kong University of Science and Technology · Department of Management Science & Engineering, Stanford University
Statistical Inference for Policy Evaluation with Temporal Difference Learning
Oct 21, 2024Weichen Wu, Gen Li, Yuting Wei +1Parameter Estimation Confidence 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

Oct 2, 2026J/K move · Enter open · S save

Bellman Error Minimization Via Linear Programming Normalization

Haining Yu

Independent Consultant

Function Approximation in RL Dynamic Programming Function Approximation Reinforcement Learning Markov Decision Processes

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This paper proposes a new functional approximation approach to reduce Bellman error in high-dimensional dynamic programming and Reinforcement Learning problems. Using a classic dynamic programming problem (network capacity control in revenue management) as the motivational example, the paper illustrates that deep neural networks and linear programming approximation algorithms can be combined to derive approximate solutions to dynamic programming problems. Simulation results show the proposed approximation algorithms achieves competitive performance when compared with benchmark.