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We propose a new model-based offline RL framework, called Adversarial Models for Offline Reinforcement Learning (ARMOR), which can robustly learn policies to improve upon an arbitrary baseline policy regardless of data coverage.
Empirical Processes in M-estimation , volume 6
Sara A van de Geer · 2000
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From ε \varepsilon -entropy to kl-entropy: Analysis of minimum information complexity density estimation
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Adversarially trained actor critic for offline reinforcement learning
Ching-An Cheng, Tengyang Xie, Nan Jiang, and Alekh Agarwal · 2022
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Qinghua Liu, Alan Chung, Csaba Szepesvári, and Chi Jin · 2022
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Bellman-consistent pessimism for offline reinforcement learning
Tengyang Xie, Ching-An Cheng, Nan Jiang, Paul Mineiro, and Alekh Agarwal
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Policy finetuning: Bridging sample-efficient offline and online reinforcement learning
Tengyang Xie, Nan Jiang, Huan Wang, Caiming Xiong, and Yu Bai
Cited in the paper.