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We consider a challenging theoretical problem in offline reinforcement learning (RL): obtaining sample-efficiency guarantees with a dataset lacking sufficient coverage, under only realizability-type assumptions for the function approximators.
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Richard S Sutton and Andrew G Barto · 2018
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Mengjiao Yang, Ofir Nachum, Bo Dai, Lihong Li, and Dale Schuurmans · 2020
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Simon Du, Sham Kakade, Jason Lee, Shachar Lovett, Gaurav Mahajan, Wen Sun, and Ruosong Wang · 2021
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Offline reinforcement learning: Fundamental barriers for value function approximation
Dylan J Foster, Akshay Krishnamurthy, David Simchi-Levi, and Yunzong Xu · 2021
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Logarithmic regret for reinforcement learning with linear function approximation
Jiafan He, Dongruo Zhou, and Quanquan Gu · 2021
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Non-asymptotic gap-dependent regret bounds for tabular mdps
Max Simchowitz and Kevin G Jamieson · 2019
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Introduction to multi-armed bandits
Aleksandrs Slivkins et al · 2019
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Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches
Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, and John Langford · 2019
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Minimax value interval for off-policy evaluation and policy optimization
Nan Jiang and Jiawei Huang · 2020
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Bandit algorithms
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Provably good batch reinforcement learning without great exploration
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Fast rates for the regret of offline reinforcement learning
Yichun Hu, Nathan Kallus, and Masatoshi Uehara · 2021
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Model-free representation learning and exploration in low-rank mdps
Aditya Modi, Jinglin Chen, Akshay Krishnamurthy, Nan Jiang, and Alekh Agarwal · 2021
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Reinforcement learning in linear mdps: Constant regret and representation selection
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Bridging offline reinforcement learning and imitation learning: A tale of pessimism
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Gap-dependent unsupervised exploration for reinforcement learning
Jingfeng Wu, Vladimir Braverman, and Lin F Yang · 2021
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Batch value-function approximation with only realizability
Tengyang Xie and Nan Jiang · 2021
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Q-learning with logarithmic regret
Kunhe Yang, Lin Yang, and Simon Du · 2021
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Near-optimal offline reinforcement learning via double variance reduction
Ming Yin, Yu Bai, and Yu-Xiang Wang · 2021
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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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Offline reinforcement learning with realizability and single-policy concentrability
Wenhao Zhan, Baihe Huang, Audrey Huang, Nan Jiang, and Jason D Lee · 2022
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