A Finite Time Analysis of Temporal Difference Learning With Linear Function Approximation

6 June 2018

Papers citing "A Finite Time Analysis of Temporal Difference Learning With Linear Function Approximation"

23 / 223 papers shown

Title
Optimization for Reinforcement Learning: From Single Agent to Cooperative Agents Dong-hwan Lee Niao He Parameswaran Kamalaruban V. Cevher 14 88 0 01 Dec 2019
Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms Kaipeng Zhang Zhuoran Yang Tamer Basar 63 1,184 0 24 Nov 2019
Finite-Sample Analysis of Decentralized Temporal-Difference Learning with Linear Function Approximation Jun Sun Gang Wang G. Giannakis Qinmin Yang Zaiyue Yang OffRL 16 20 0 03 Nov 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
Actor-Critic Provably Finds Nash Equilibria of Linear-Quadratic Mean-Field Games Zuyue Fu Zhuoran Yang Yongxin Chen Zhaoran Wang 24 54 0 16 Oct 2019
Two Time-scale Off-Policy TD Learning: Non-asymptotic Analysis over Markovian Samples Tengyu Xu Shaofeng Zou Yingbin Liang 19 73 0 26 Sep 2019
A Multistep Lyapunov Approach for Finite-Time Analysis of Biased Stochastic Approximation Gang Wang Bingcong Li G. Giannakis 31 28 0 10 Sep 2019
Fast Multi-Agent Temporal-Difference Learning via Homotopy Stochastic Primal-Dual Optimization Dongsheng Ding Xiaohan Wei Zhuoran Yang Zhaoran Wang M. Jovanović 11 15 0 07 Aug 2019
Finite-Time Performance of Distributed Temporal Difference Learning with Linear Function Approximation Thinh T. Doan S. T. Maguluri Justin Romberg 30 41 0 25 Jul 2019
Finite-Time Performance Bounds and Adaptive Learning Rate Selection for Two Time-Scale Reinforcement Learning Harsh Gupta R. Srikant Lei Ying 11 85 0 14 Jul 2019
Characterizing the Exact Behaviors of Temporal Difference Learning Algorithms Using Markov Jump Linear System Theory Bin Hu U. Syed 15 58 0 16 Jun 2019
Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator K. Krauth Stephen Tu Benjamin Recht 19 57 0 30 May 2019
Geometric Insights into the Convergence of Nonlinear TD Learning David Brandfonbrener Joan Bruna 17 15 0 29 May 2019
Finite-Sample Analysis of Nonlinear Stochastic Approximation with Applications in Reinforcement Learning Zaiwei Chen Sheng Zhang Thinh T. Doan John-Paul Clarke S. T. Maguluri 17 58 0 27 May 2019
Neural Temporal-Difference and Q-Learning Provably Converge to Global Optima Qi Cai Zhuoran Yang Jason D. Lee Zhaoran Wang 39 29 0 24 May 2019
Target-Based Temporal Difference Learning Donghwan Lee Niao He OOD 19 31 0 24 Apr 2019
Some Limit Properties of Markov Chains Induced by Stochastic Recursive Algorithms Abhishek Gupta Hao Chen Jianzong Pi Gaurav Tendolkar 19 0 0 24 Apr 2019
Finite-Sample Analysis for SARSA with Linear Function Approximation Shaofeng Zou Tengyu Xu Yingbin Liang 32 146 0 06 Feb 2019
Finite-Time Error Bounds For Linear Stochastic Approximation and TD Learning R. Srikant Lei Ying 13 247 0 03 Feb 2019
Non-asymptotic Analysis of Biased Stochastic Approximation Scheme Belhal Karimi B. Miasojedow Eric Moulines Hoi-To Wai 10 90 0 02 Feb 2019
Q-learning with Nearest Neighbors Devavrat Shah Qiaomin Xie OffRL 19 77 0 12 Feb 2018
Concentration bounds for temporal difference learning with linear function approximation: The case of batch data and uniform sampling L. A. Prashanth N. Korda Rémi Munos 40 16 0 11 Jun 2013
A simpler approach to obtaining an O(1/t) convergence rate for the projected stochastic subgradient method Simon Lacoste-Julien Mark W. Schmidt Francis R. Bach 128 259 0 10 Dec 2012