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What can online reinforcement learning with function approximation benefit from general coverage conditions?

25 April 2023

Papers citing "What can online reinforcement learning with function approximation benefit from general coverage conditions?"

21 / 21 papers shown

Title
Leveraging Offline Data in Online Reinforcement Learning Andrew Wagenmaker Aldo Pacchiano OffRL OnRL 89 40 0 09 Nov 2022
Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient Yuda Song Yi Zhou Ayush Sekhari J. Andrew Bagnell A. Krishnamurthy Wen Sun OffRL OnRL 92 105 0 13 Oct 2022
A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning Zixiang Chen C. J. Li An Yuan Quanquan Gu Michael I. Jordan OffRL 149 27 0 30 Sep 2022
Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation Christoph Dann Yishay Mansour M. Mohri Ayush Sekhari Karthik Sridharan 102 53 0 19 Jun 2022
Offline Reinforcement Learning with Realizability and Single-policy Concentrability Wenhao Zhan Baihe Huang Audrey Huang Nan Jiang Jason D. Lee OffRL 394 112 0 09 Feb 2022
The Statistical Complexity of Interactive Decision Making Dylan J. Foster Sham Kakade Jian Qian Alexander Rakhlin 377 183 0 27 Dec 2021
Bellman-consistent Pessimism for Offline Reinforcement Learning Tengyang Xie Ching-An Cheng Nan Jiang Paul Mineiro Alekh Agarwal OffRL LRM 192 279 0 13 Jun 2021
An Exponential Lower Bound for Linearly-Realizable MDPs with Constant Suboptimality Gap Yuanhao Wang Ruosong Wang Sham Kakade OffRL 181 43 0 23 Mar 2021
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism Paria Rashidinejad Banghua Zhu Cong Ma Jiantao Jiao Stuart J. Russell OffRL 244 290 0 22 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 193 191 0 19 Mar 2021
Is Pessimism Provably Efficient for Offline RL? Ying Jin Zhuoran Yang Zhaoran Wang OffRL 191 360 0 30 Dec 2020
Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes Dongruo Zhou Quanquan Gu Csaba Szepesvári 88 209 0 15 Dec 2020
Exponential Lower Bounds for Batch Reinforcement Learning: Batch RL can be Exponentially Harder than Online RL Andrea Zanette OffRL 181 71 0 14 Dec 2020
Logarithmic Regret for Reinforcement Learning with Linear Function Approximation Jiafan He Dongruo Zhou Quanquan Gu 55 95 0 23 Nov 2020
Variational Policy Gradient Method for Reinforcement Learning with General Utilities Junyu Zhang Alec Koppel Amrit Singh Bedi Csaba Szepesvári Mengdi Wang 77 140 0 04 Jul 2020
AWAC: Accelerating Online Reinforcement Learning with Offline Datasets Ashvin Nair Abhishek Gupta Murtaza Dalal Sergey Levine OffRL OnRL 133 618 0 16 Jun 2020
Model-Based Reinforcement Learning with Value-Targeted Regression Alex Ayoub Zeyu Jia Csaba Szepesvári Mengdi Wang Lin F. Yang OffRL 101 305 0 01 Jun 2020
Provably Efficient Reinforcement Learning with Linear Function Approximation Chi Jin Zhuoran Yang Zhaoran Wang Michael I. Jordan 111 560 0 11 Jul 2019
Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound Lin F. Yang Mengdi Wang OffRL GP 93 288 0 24 May 2019
Information-Theoretic Considerations in Batch Reinforcement Learning Jinglin Chen Nan Jiang OOD OffRL 167 378 0 01 May 2019
Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations Aravind Rajeswaran Vikash Kumar Abhishek Gupta Giulia Vezzani John Schulman E. Todorov Sergey Levine 146 1,101 0 28 Sep 2017