Offline Policy Evaluation for Reinforcement Learning with Adaptively
Collected Data

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Offline Policy Evaluation for Reinforcement Learning with Adaptively Collected Data

24 June 2023

Ming Yin

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Papers citing "Offline Policy Evaluation for Reinforcement Learning with Adaptively Collected Data"

13 / 13 papers shown

Title
Policy learning "without" overlap: Pessimism and generalized empirical Bernstein's inequality Ying Jin Zhimei Ren Zhuoran Yang Zhaoran Wang OffRL 103 26 0 19 Dec 2022
Off-policy estimation of linear functionals: Non-asymptotic theory for semi-parametric efficiency Wenlong Mou Martin J. Wainwright Peter L. Bartlett OffRL 110 11 0 26 Sep 2022
Offline Reinforcement Learning with Differential Privacy Dan Qiao Yu Wang OffRL 100 23 0 02 Jun 2022
Is Pessimism Provably Efficient for Offline RL? Ying Jin Zhuoran Yang Zhaoran Wang OffRL 187 360 0 30 Dec 2020
Optimal Off-Policy Evaluation from Multiple Logging Policies Nathan Kallus Yuta Saito Masatoshi Uehara OffRL 69 40 0 21 Oct 2020
Batch Value-function Approximation with Only Realizability Tengyang Xie Nan Jiang OffRL 400 121 0 11 Aug 2020
Deep Reinforcement Learning for Autonomous Driving: A Survey B. R. Kiran Ibrahim Sobh V. Talpaert Patrick Mannion A. A. Sallab S. Yogamani P. Pérez 358 1,693 0 02 Feb 2020
Reinforcement Learning in Healthcare: A Survey Chao Yu Jiming Liu S. Nemati LM&MA OffRL 192 575 0 22 Aug 2019
Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling Tengyang Xie Yifei Ma Yu Wang OffRL 110 181 0 08 Jun 2019
Are sample means in multi-armed bandits positively or negatively biased? Jaehyeok Shin Aaditya Ramdas Alessandro Rinaldo 74 37 0 27 May 2019
Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning Christoph Dann Tor Lattimore Emma Brunskill 83 311 0 22 Mar 2017
Real-Time Bidding by Reinforcement Learning in Display Advertising Han Cai Kan Ren Weinan Zhang Kleanthis Malialis Jun Wang Yong Yu Defeng Guo 64 247 0 10 Jan 2017
Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning Philip S. Thomas Emma Brunskill OffRL 432 577 0 04 Apr 2016