Is Q-learning Provably Efficient?

10 July 2018

Papers citing "Is Q-learning Provably Efficient?"

25 / 225 papers shown

Title
Learning Zero-Sum Simultaneous-Move Markov Games Using Function Approximation and Correlated Equilibrium Qiaomin Xie Yudong Chen Zhaoran Wang Zhuoran Yang 41 124 0 17 Feb 2020
Provable Self-Play Algorithms for Competitive Reinforcement Learning Yu Bai Chi Jin SSL 22 148 0 10 Feb 2020
Adaptive Approximate Policy Iteration Botao Hao N. Lazić Yasin Abbasi-Yadkori Pooria Joulani Csaba Szepesvári 18 14 0 08 Feb 2020
Reward-Free Exploration for Reinforcement Learning Chi Jin A. Krishnamurthy Max Simchowitz Tiancheng Yu OffRL 116 194 0 07 Feb 2020
Asymptotically Efficient Off-Policy Evaluation for Tabular Reinforcement Learning Ming Yin Yu Wang OffRL 29 80 0 29 Jan 2020
Online Reinforcement Learning of Optimal Threshold Policies for Markov Decision Processes Arghyadip Roy Vivek Borkar A. Karandikar P. Chaporkar OffRL 30 20 0 21 Dec 2019
Optimism in Reinforcement Learning with Generalized Linear Function Approximation Yining Wang Ruosong Wang S. Du A. Krishnamurthy 137 135 0 09 Dec 2019
Scalable Reinforcement Learning for Multi-Agent Networked Systems Guannan Qu Adam Wierman Na Li 28 33 0 05 Dec 2019
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning Dipendra Kumar Misra Mikael Henaff A. Krishnamurthy John Langford 36 151 0 13 Nov 2019
Explicit Explore-Exploit Algorithms in Continuous State Spaces Mikael Henaff OffRL 22 31 0 01 Nov 2019
Better Exploration with Optimistic Actor-Critic K. Ciosek Q. Vuong R. Loftin Katja Hofmann 29 149 0 28 Oct 2019
On the Sample Complexity of Actor-Critic Method for Reinforcement Learning with Function Approximation Harshat Kumar Alec Koppel Alejandro Ribeiro 104 80 0 18 Oct 2019
Model-free Reinforcement Learning in Infinite-horizon Average-reward Markov Decision Processes Chen-Yu Wei Mehdi Jafarnia-Jahromi Haipeng Luo Hiteshi Sharma R. Jain 107 100 0 15 Oct 2019
Influence-Based Multi-Agent Exploration Tonghan Wang Jianhao Wang Yi Wu Chongjie Zhang 24 137 0 12 Oct 2019
Provably Efficient Reinforcement Learning with Linear Function Approximation Chi Jin Zhuoran Yang Zhaoran Wang Michael I. Jordan 52 543 0 11 Jul 2019
From self-tuning regulators to reinforcement learning and back again Nikolai Matni Alexandre Proutiere Anders Rantzer Stephen Tu 27 88 0 27 Jun 2019
Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies Yonathan Efroni Nadav Merlis Mohammad Ghavamzadeh Shie Mannor OffRL 24 68 0 27 May 2019
Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound Lin F. Yang Mengdi Wang OffRL GP 26 283 0 24 May 2019
$Stochastic approximation with cone-contractive operators: Sharp $\ell_\infty$-bounds for $Q$-learning$ Stochastic approximation with cone-contractive operators: Sharp $\ell_\infty$ -bounds for $Q$ -learning Martin J. Wainwright 19 104 0 15 May 2019
Q-learning with UCB Exploration is Sample Efficient for Infinite-Horizon MDP Kefan Dong Yuanhao Wang Xiaoyu Chen Liwei Wang OffRL 19 95 0 27 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
The Gap Between Model-Based and Model-Free Methods on the Linear Quadratic Regulator: An Asymptotic Viewpoint Stephen Tu Benjamin Recht OffRL 33 150 0 09 Dec 2018
Input Perturbations for Adaptive Control and Learning Mohamad Kazem Shirani Faradonbeh Ambuj Tewari George Michailidis 21 46 0 10 Nov 2018
Policy Certificates: Towards Accountable Reinforcement Learning Christoph Dann Ashutosh Adhikari Wei Wei Jimmy J. Lin OffRL 25 141 0 07 Nov 2018
Unsupervised Basis Function Adaptation for Reinforcement Learning Edward W. Barker C. Ras OffRL 17 3 0 03 Mar 2017