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Efficient Local Planning with Linear Function Approximation

12 August 2021

Papers citing "Efficient Local Planning with Linear Function Approximation"

27 / 27 papers shown

Title
Sample-Efficient Reinforcement Learning Is Feasible for Linearly Realizable MDPs with Limited Revisiting Gen Li Yuxin Chen Yuejie Chi Yuantao Gu Yuting Wei OffRL 54 30 0 17 May 2021
Cautiously Optimistic Policy Optimization and Exploration with Linear Function Approximation Andrea Zanette Ching-An Cheng Alekh Agarwal 80 53 0 24 Mar 2021
An Exponential Lower Bound for Linearly-Realizable MDPs with Constant Suboptimality Gap Yuanhao Wang Ruosong Wang Sham Kakade OffRL 164 43 0 23 Mar 2021
Bilinear Classes: A Structural Framework for Provable Generalization in RL S. Du Sham Kakade Jason D. Lee Shachar Lovett G. Mahajan Wen Sun Ruosong Wang OffRL 168 191 0 19 Mar 2021
Improved Regret Bound and Experience Replay in Regularized Policy Iteration N. Lazić Dong Yin Yasin Abbasi-Yadkori Csaba Szepesvári OffRL 38 18 0 25 Feb 2021
On Query-efficient Planning in MDPs under Linear Realizability of the Optimal State-value Function Gellert Weisz Philip Amortila Barnabás Janzer Yasin Abbasi-Yadkori Nan Jiang Csaba Szepesvári OffRL 47 20 0 03 Feb 2021
Bellman Eluder Dimension: New Rich Classes of RL Problems, and Sample-Efficient Algorithms Chi Jin Qinghua Liu Sobhan Miryoosefi OffRL 91 217 0 01 Feb 2021
Provably Efficient Reinforcement Learning with Linear Function Approximation Under Adaptivity Constraints Chi Jin Zhuoran Yang Zhaoran Wang OffRL 249 167 0 06 Jan 2021
Exponential Lower Bounds for Planning in MDPs With Linearly-Realizable Optimal Action-Value Functions Gellert Weisz Philip Amortila Csaba Szepesvári OffRL 156 80 0 03 Oct 2020
Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation Chen-Yu Wei Mehdi Jafarnia-Jahromi Haipeng Luo Rahul Jain 52 43 0 23 Jul 2020
PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning Alekh Agarwal Mikael Henaff Sham Kakade Wen Sun OffRL 67 109 0 16 Jul 2020
Efficient Planning in Large MDPs with Weak Linear Function Approximation R. Shariff Csaba Szepesvári 62 22 0 13 Jul 2020
Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping Dongruo Zhou Jiafan He Quanquan Gu 71 135 0 23 Jun 2020
Model-Based Reinforcement Learning with Value-Targeted Regression Alex Ayoub Zeyu Jia Csaba Szepesvári Mengdi Wang Lin F. Yang OffRL 89 305 0 01 Jun 2020
Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model Gen Li Yuting Wei Yuejie Chi Yuxin Chen 99 129 0 26 May 2020
Learning Near Optimal Policies with Low Inherent Bellman Error Andrea Zanette A. Lazaric Mykel Kochenderfer Emma Brunskill OffRL 71 222 0 29 Feb 2020
Agnostic Q-learning with Function Approximation in Deterministic Systems: Tight Bounds on Approximation Error and Sample Complexity S. Du Jason D. Lee G. Mahajan Ruosong Wang 43 37 0 17 Feb 2020
Provably Efficient Exploration in Policy Optimization Qi Cai Zhuoran Yang Chi Jin Zhaoran Wang 56 281 0 12 Dec 2019
Optimism in Reinforcement Learning with Generalized Linear Function Approximation Yining Wang Ruosong Wang S. Du A. Krishnamurthy 176 136 0 09 Dec 2019
Comments on the Du-Kakade-Wang-Yang Lower Bounds Benjamin Van Roy Shi Dong 138 38 0 18 Nov 2019
Learning with Good Feature Representations in Bandits and in RL with a Generative Model Tor Lattimore Csaba Szepesvári Gellert Weisz OffRL 158 171 0 18 Nov 2019
Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning? S. Du Sham Kakade Ruosong Wang Lin F. Yang 181 193 0 07 Oct 2019
Model-Based Reinforcement Learning with a Generative Model is Minimax Optimal Alekh Agarwal Sham Kakade Lin F. Yang OffRL 89 172 0 10 Jun 2019
Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound Lin F. Yang Mengdi Wang OffRL GP 62 286 0 24 May 2019
Go-Explore: a New Approach for Hard-Exploration Problems Adrien Ecoffet Joost Huizinga Joel Lehman Kenneth O. Stanley Jeff Clune AI4TS 86 370 0 30 Jan 2019
Contextual Decision Processes with Low Bellman Rank are PAC-Learnable Nan Jiang A. Krishnamurthy Alekh Agarwal John Langford Robert Schapire 147 420 0 29 Oct 2016
On the Sample Complexity of Reinforcement Learning with a Generative Model M. G. Azar Rémi Munos H. Kappen 71 156 0 27 Jun 2012