Linear Function Approximation

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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.
Fast and Robust Convergence Rate for TD(0) with Linear Function Approximation, Universal Learning Steps and I.I.D. Samples
Jun 4, 2026Ziad Kobeissi, Éloïse BerthierStochastic Approximation Temporal-Difference Learning
L2S · L2S, INRIA Université Paris-Saclay, CentraleSupelec, Gif-sur-Yvette, France · U2IS +1
Optimal Data Acquisition for Reinforcement Learning: A Large Deviations Perspective
May 27, 2026Mingjie Hu, Jian-Qiang Hu, Enlu ZhouReinforcement Learning Sample-Efficient RL
School of Management, Fudan University · H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology
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
A Switching System Theory of Q-Learning with Linear Function Approximation
May 10, 2026Donghwan Lee, Han-Dong LimQ-Learning Linear Function Approximation
Department of Electrical Engineering · Korea Advanced Institute of Science and Technology (KAIST) · Daejeon 34141, South Korea
Taming the Curses of Multiagency in Robust Markov Games with Large State Space through Linear Function Approximation
May 4, 2026Jingchu Gai, Laixi ShiMulti-Agent System Optimization Markov Models
Machine Learning Department, Carnegie Mellon University. · Department of Electrical and Computer Engineering, Johns Hopkins University, MD 21286, USA.
Bridging the Gap Between Average and Discounted TD Learning
May 3, 2026Haoxing Tian, Zaiwei Chen, Ioannis Ch. Paschalidis +1Reinforcement Learning Average-Reward RL
Department of Electrical and Computer Engineering Boston University · Edwardson School of Industrial Engineering Purdue University
Multi-agent imitation learning with function approximation: Linear Markov games and beyond
Feb 26, 2026Luca Viano, Till Freihaut, Emanuele Nevali +3Game Theory Imitation Learning
1EPFL · University of Zurich · 3Earth Species Project
Finite-Time Convergence of Single-Trajectory Chi-Square Robust Q-Learning With Linear Function Approximation
Oct 2, 2025Saptarshi Mandal, Yashaswini Murthy, R. SrikantQ-Learning Distributionally Robust RL
ECE, UIUC · ORIE, UT Austin · ECE, CSL, and the director of NCSA at UIUC
Rethinking the Global Convergence of Softmax Policy Gradient with Linear Function Approximation: The Case of Multi-Armed Bandits
May 6, 2025Max Qiushi Lin, Jincheng Mei, Matin Aghaei +6Multi-Armed Bandits Policy Gradient
Simon Fraser University · Google DeepMind · Google Research +1

Jun 16, 2026, stat.MLJ/K move · Enter open · S save

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

M. Forzo, E. Monzio Compagnoni, A. Russo, A. Pacchiano

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

Stochastic Differential Equations Temporal-Difference Learning Linear Function Approximation

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Temporal difference (TD) learning with linear function approximation is a core method for policy evaluation. Its classical continuous-time description is an ordinary differential equation (ODE), which captures the asymptotic mean dynamics but neglects stochastic fluctuations determining the error floor. We introduce a stochastic differential equation (SDE) approximation for linear TD(0) under Markovian noise. The resulting model distinguishes the contraction dynamics governed by the projected Bellman operator from the influence of Markovian sampling. As a consequence, the model explains the constant-stepsize error floor through the interaction between Markovian long-run covariance and the contraction geometry of the projected Bellman operator.

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