$Stochastic approximation with cone-contractive operators: Sharp $\ell_\infty$-bounds for $Q$-learning$

v1v2 (latest)

Stochastic approximation with cone-contractive operators: Sharp $\ell_\infty$ -bounds for $Q$ -learning

15 May 2019

Martin J. Wainwright

ArXiv (abs)PDF HTML

Papers citing "Stochastic approximation with cone-contractive operators: Sharp $\ell_\infty$-bounds for $Q$-learning"

8 / 8 papers shown

Title
Rank-One Modified Value Iteration Arman Sharifi Kolarijani Tolga Ok Peyman Mohajerin Esfahani Mohamad Amin Sharif Kolarijani 58 0 0 03 May 2025
Stochastic Halpern iteration in normed spaces and applications to reinforcement learning Mario Bravo Juan Pablo Contreras 76 4 0 19 Mar 2024
Tight Finite Time Bounds of Two-Time-Scale Linear Stochastic Approximation with Markovian Noise Shaan ul Haque S. Khodadadian S. T. Maguluri 105 11 0 31 Dec 2023
A Concentration Bound for TD(0) with Function Approximation Siddharth Chandak Vivek Borkar 91 1 0 16 Dec 2023
Settling the Sample Complexity of Online Reinforcement Learning Zihan Zhang Yuxin Chen Jason D. Lee S. Du OffRL 194 25 0 25 Jul 2023
The Gap Between Model-Based and Model-Free Methods on the Linear Quadratic Regulator: An Asymptotic Viewpoint Stephen Tu Benjamin Recht OffRL 73 151 0 09 Dec 2018
Is Q-learning Provably Efficient? Chi Jin Zeyuan Allen-Zhu Sébastien Bubeck Michael I. Jordan OffRL 78 812 0 10 Jul 2018
From Gauss to Kolmogorov: Localized Measures of Complexity for Ellipses Yuting Wei Billy Fang Martin J. Wainwright 35 7 0 21 Mar 2018