Polyak Ruppert

Polyak-Ruppert averaging is a technique used to improve the convergence and efficiency of stochastic approximation algorithms, particularly in machine learning contexts like temporal difference (TD) learning and Q-learning. Current research focuses on refining finite-time analysis of these averaged iterates, deriving sharper bounds on error and variance, and exploring optimal step-size selection strategies within various algorithm settings (e.g., constant vs. decreasing step sizes, regularized versions). These advancements lead to more accurate and reliable parameter estimation, improved confidence intervals, and enhanced understanding of algorithm performance in both theoretical and practical applications, impacting fields like reinforcement learning and statistical inference.