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Coverage conditions -- which assert that the data logging distribution adequately covers the state space -- play a fundamental role in determining the sample complexity of offline reinforcement learning.
Error bounds for approximate policy iteration
Rémi Munos · 2003
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Performance bounds in ℓ p \ell_{p} -norm for approximate value iteration
Rémi Munos · 2007
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Learning near-optimal policies with bellman-residual minimization based fitted policy iteration and a single sample path
András Antos, Csaba Szepesvári, and Rémi Munos · 2008
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Finite-time bounds for fitted value iteration
Rémi Munos and Csaba Szepesvári · 2008
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Error propagation for approximate policy and value iteration
Amir-massoud Farahmand, Csaba Szepesvári, and Rémi Munos · 2010
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Contextual bandit learning with predictable rewards
Alekh Agarwal, Miroslav Dudík, Satyen Kale, John Langford, and Robert E Schapire · 2012
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Reinforcement learning in robotics: A survey
Jens Kober, J Andrew Bagnell, and Jan Peters · 2013
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Eluder dimension and the sample complexity of optimistic exploration
Daniel Russo and Benjamin Van Roy · 2013
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Taming the monster: A fast and simple algorithm for contextual bandits
Alekh Agarwal, Daniel Hsu, Satyen Kale, John Langford, Lihong Li, and Robert Schapire · 2014
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Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2015
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Making contextual decisions with low technical debt
Alekh Agarwal, Sarah Bird, Markus Cozowicz, Luong Hoang, John Langford, Stephen Lee, Jiaji Li, Dan Melamed, Gal Oshri, Oswaldo Ribas, Siddhartha Sen, and Aleksandrs Slivkins · 2016
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PAC reinforcement learning with rich observations
Akshay Krishnamurthy, Alekh Agarwal, and John Langford · 2016
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Deep reinforcement learning for dialogue generation
Jiwei Li, Will Monroe, Alan Ritter, Dan Jurafsky, Michel Galley, and Jianfeng Gao · 2016
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Contextual decision processes with low Bellman rank are PAC-learnable
Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, and Robert E Schapire · 2017
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From ads to interventions: Contextual bandits in mobile health
Ambuj Tewari and Susan A. Murphy · 2017
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Open problem: The dependence of sample complexity lower bounds on planning horizon
Nan Jiang and Alekh Agarwal · 2018
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Scalable deep reinforcement learning for vision-based robotic manipulation
Dmitry Kalashnikov, Alex Irpan, Peter Pastor, Julian Ibarz, Alexander Herzog, Eric Jang, Deirdre Quillen, Ethan Holly, Mrinal Kalakrishnan, Vincent Vanhoucke, et al · 2018
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Yasin Abbasi-Yadkori, Nevena Lazic, Csaba Szepesvari, and Gellert Weisz · 2019
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Information-theoretic considerations in batch reinforcement learning
Jinglin Chen and Nan Jiang · 2019
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The statistical complexity of interactive decision making
Dylan J Foster, Sham M Kakade, Jian Qian, and Alexander Rakhlin · 2021
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Bridging offline reinforcement learning and imitation learning: A tale of pessimism
Paria Rashidinejad, Banghua Zhu, Cong Ma, Jiantao Jiao, and Stuart Russell · 2021
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Batch value-function approximation with only realizability
Tengyang Xie and Nan Jiang · 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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Zihan Zhang, Xiangyang Ji, and Simon Du · 2021
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Simon Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudik, and John Langford · 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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Task-agnostic exploration in reinforcement learning
Xuezhou Zhang, Yuzhe Ma, and Adish Singla · 2020
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On the statistical efficiency of reward-free exploration in non-linear rl
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Offline reinforcement learning with realizability and single-policy concentrability
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