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We propose a novel model-based offline Reinforcement Learning (RL) framework, called Adversarial Model for Offline Reinforcement Learning (ARMOR), which can robustly learn policies to improve upon an arbitrary reference policy regardless of data coverage.
Empirical Processes in M-estimation , volume 6
Sara A van de Geer · 2000
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Sham M Kakade · 2001
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Tom Silver, Kelsey Allen, Josh Tenenbaum, and Leslie Kaelbling · 2018
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Jinglin Chen and Nan Jiang · 2019
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Scott Fujimoto, David Meger, and Doina Precup · 2019
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Aviral Kumar, Justin Fu, Matthew Soh, George Tucker, and Sergey Levine · 2019
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Romain Laroche, Paul Trichelair, and Remi Tachet Des Combes · 2019
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Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine · 2020
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Paria Rashidinejad, Banghua Zhu, Cong Ma, Jiantao Jiao, and Stuart Russell · 2021
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Pessimistic model-based offline reinforcement learning under partial coverage
Masatoshi Uehara and Wen Sun · 2021
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On the optimality of batch policy optimization algorithms
Chenjun Xiao, Yifan Wu, Jincheng Mei, Bo Dai, Tor Lattimore, Lihong Li, Csaba Szepesvari, and Dale Schuurmans · 2021
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Batch value-function approximation with only realizability
Tengyang Xie and Nan Jiang · 2021
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Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Y Zou, Sergey Levine, Chelsea Finn, and Tengyu Ma · 2020
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Provable benefits of actor-critic methods for offline reinforcement learning
Andrea Zanette, Martin J Wainwright, and Emma Brunskill · 2021
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Adversarial counterfactual environment model learning
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