Nearly Minimax Optimal Reinforcement Learning with Linear Function Approximation

23 June 2022

Papers citing "Nearly Minimax Optimal Reinforcement Learning with Linear Function Approximation"

6 / 6 papers shown

Title
A Policy Gradient Primal-Dual Algorithm for Constrained MDPs with Uniform PAC Guarantees Toshinori Kitamura Tadashi Kozuno Masahiro Kato Yuki Ichihara Soichiro Nishimori Akiyoshi Sannai Sho Sonoda Wataru Kumagai Yutaka Matsuo 39 2 0 31 Jan 2024
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice Toshinori Kitamura Tadashi Kozuno Yunhao Tang Nino Vieillard Michal Valko ... Olivier Pietquin M. Geist Csaba Szepesvári Wataru Kumagai Yutaka Matsuo OffRL 30 2 0 22 May 2023
A Theoretical Analysis of Optimistic Proximal Policy Optimization in Linear Markov Decision Processes Han Zhong Tong Zhang 32 26 0 15 May 2023
Near-optimal Policy Identification in Active Reinforcement Learning Xiang Li Viraj Mehta Johannes Kirschner I. Char W. Neiswanger J. Schneider Andreas Krause Ilija Bogunovic OffRL 40 6 0 19 Dec 2022
Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes Jiafan He Heyang Zhao Dongruo Zhou Quanquan Gu OffRL 51 53 0 12 Dec 2022
Optimism in Reinforcement Learning with Generalized Linear Function Approximation Yining Wang Ruosong Wang S. Du A. Krishnamurthy 132 135 0 09 Dec 2019