Variance Reduction Methods for Sublinear Reinforcement Learning

26 February 2018

Papers citing "Variance Reduction Methods for Sublinear Reinforcement Learning"

9 / 9 papers shown

Title
Gap-Dependent Bounds for Q-Learning using Reference-Advantage Decomposition Zhong Zheng Haochen Zhang Lingzhou Xue OffRL 78 2 0 10 Oct 2024
MADE: Exploration via Maximizing Deviation from Explored Regions Tianjun Zhang Paria Rashidinejad Jiantao Jiao Yuandong Tian Joseph E. Gonzalez Stuart J. Russell OffRL 51 42 0 18 Jun 2021
Is Q-Learning Provably Efficient? An Extended Analysis Kushagra Rastogi Jonathan Lee Fabrice Harel-Canada Aditya Sunil Joglekar OffRL 19 1 0 22 Sep 2020
Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes Yi Tian Jian Qian S. Sra 24 25 0 24 Jun 2020
Model-Based Reinforcement Learning with Value-Targeted Regression Alex Ayoub Zeyu Jia Csaba Szepesvári Mengdi Wang Lin F. Yang OffRL 62 300 0 01 Jun 2020
Provably Efficient Model-Free Algorithm for MDPs with Peak Constraints Qinbo Bai Vaneet Aggarwal Ather Gattami 44 7 0 11 Mar 2020
Regret Minimization for Reinforcement Learning by Evaluating the Optimal Bias Function Zihan Zhang Xiangyang Ji 21 71 0 12 Jun 2019
Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds Andrea Zanette Emma Brunskill OffRL 56 273 0 01 Jan 2019
Exploration Bonus for Regret Minimization in Undiscounted Discrete and Continuous Markov Decision Processes Jian Qian Ronan Fruit Matteo Pirotta A. Lazaric 14 10 0 11 Dec 2018