Offline Reinforcement Learning with Differentiable Function
Approximation is Provably Efficient

Offline Reinforcement Learning with Differentiable Function Approximation is Provably Efficient

3 October 2022

Ming Yin

Papers citing "Offline Reinforcement Learning with Differentiable Function Approximation is Provably Efficient"

8 / 8 papers shown

Title
Policy Finetuning in Reinforcement Learning via Design of Experiments using Offline Data Ruiqi Zhang Andrea Zanette OffRL OnRL 40 7 0 10 Jul 2023
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
VIPeR: Provably Efficient Algorithm for Offline RL with Neural Function Approximation Thanh Nguyen-Tang R. Arora OffRL 46 5 0 24 Feb 2023
Improved Variance-Aware Confidence Sets for Linear Bandits and Linear Mixture MDP Zihan Zhang Jiaqi Yang Xiangyang Ji S. Du 68 36 0 29 Jan 2021
Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems Sergey Levine Aviral Kumar George Tucker Justin Fu OffRL GP 340 1,955 0 04 May 2020
Reward-Free Exploration for Reinforcement Learning Chi Jin A. Krishnamurthy Max Simchowitz Tiancheng Yu OffRL 112 194 0 07 Feb 2020
Optimism in Reinforcement Learning with Generalized Linear Function Approximation Yining Wang Ruosong Wang S. Du A. Krishnamurthy 135 135 0 09 Dec 2019
Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes Nathan Kallus Masatoshi Uehara OffRL 38 181 0 22 Aug 2019