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Offline reinforcement learning (RL) algorithms can acquire effective policies by utilizing previously collected experience, without any online interaction.
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Martin L Puterman · 1994
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Error bounds for approximate policy iteration
Rémi Munos · 2003
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Error bounds for approximate value iteration
Rémi Munos · 2005
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Maximum entropy inverse reinforcement learning
B. D. Ziebart, A. Maas, J. A. Bagnell, and A. K. Dey · 2008
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Empirical bernstein bounds and sample variance penalization
Andreas Maurer and Massimiliano Pontil · 2009
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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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Efficient reductions for imitation learning
Stéphane Ross and Drew Bagnell · 2010
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Informing sequential clinical decision-making through reinforcement learning: an empirical study
Susan M Shortreed, Eric Laber, Daniel J Lizotte, T Scott Stroup, Joelle Pineau, and Susan A Murphy · 2011
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Batch reinforcement learning
Sascha Lange, Thomas Gabel, and Martin A. Riedmiller · 2012
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The arcade learning environment: An evaluation platform for general agents
Marc G. Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2013
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Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
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Prioritized experience replay
Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver · 2015
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Batch learning from logged bandit feedback through counterfactual risk minimization
Adith Swaminathan and Thorsten Joachims · 2015
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End to end learning for self-driving cars
Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, et al · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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IMPALA: scalable distributed deep-rl with importance weighted actor-learner architectures
Lasse Espeholt, Hubert Soyer, Rémi Munos, Karen Simonyan, Volodymyr Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, Shane Legg, and Koray Kavukcuoglu · 2018
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Off-policy deep reinforcement learning without exploration
Scott Fujimoto, David Meger, and Doina Precup · 2018
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Sfv: Reinforcement learning of physical skills from videos
Xue Bin Peng, Angjoo Kanazawa, Jitendra Malik, Pieter Abbeel, and Sergey Levine · 2018
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Learning complex dexterous manipulation with deep reinforcement learning and demonstrations
Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, Giulia Vezzani, John Schulman, Emanuel Todorov, and Sergey Levine · 2018
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Supervised reinforcement learning with recurrent neural network for dynamic treatment recommendation
L. Wang, Wei Zhang, Xiaofeng He, and H. Zha · 2018
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Information-theoretic considerations in batch reinforcement learning
Jinglin Chen and Nan Jiang · 2019
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Top-k off-policy correction for a reinforce recommender system
Minmin Chen, Alex Beutel, Paul Covington, Sagar Jain, Francois Belletti, and Ed H Chi · 2019
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Diagnosing bottlenecks in deep Q-learning algorithms
Justin Fu, Aviral Kumar, Matthew Soh, and Sergey Levine · 2019
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Stabilizing off-policy q-learning via bootstrapping error reduction
Aviral Kumar, Justin Fu, Matthew Soh, George Tucker, and Sergey Levine · 2019
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Safe policy improvement with baseline bootstrapping
Romain Laroche, Paul Trichelair, and Remi Tachet Des Combes · 2019
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Off-policy policy gradient with state distribution correction
Yao Liu, Adith Swaminathan, Alekh Agarwal, and Emma Brunskill · 2019
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Algaedice: Policy gradient from arbitrary experience
Ofir Nachum, Bo Dai, Ilya Kostrikov, Yinlam Chow, Lihong Li, and Dale Schuurmans · 2019
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Safe policy improvement with soft baseline bootstrapping
Kimia Nadjahi, Romain Laroche, and Rémi Tachet des Combes · 2019
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Mopo: Model-based offline policy optimization
Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Zou, Sergey Levine, Chelsea Finn, and Tengyu Ma · 2020
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Andrea Zanette · 2020
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Can temporal-difference and q-learning learn representation? a mean-field theory
Yufeng Zhang, Qi Cai, Zhuoran Yang, Yongxin Chen, and Zhaoran Wang · 2020
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On the theory of policy gradient methods: Optimality, approximation, and distribution shift
Alekh Agarwal, Sham M Kakade, Jason D Lee, and Gaurav Mahajan · 2021
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Advantage-weighted regression: Simple and scalable off-policy reinforcement learning
Xue Bin Peng, Aviral Kumar, Grace Zhang, and Sergey Levine · 2019
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Habitat: A Platform for Embodied AI Research
Manolis Savva, Abhishek Kadian, Oleksandr Maksymets, Yili Zhao, Erik Wijmans, Bhavana Jain, Julian Straub, Jia Liu, Vladlen Koltun, Jitendra Malik, Devi Parikh, and Dhruv Batra · 2019
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Behavior regularized offline reinforcement learning
Yifan Wu, George Tucker, and Ofir Nachum · 2019
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D4rl: Datasets for deep data-driven reinforcement learning, 2020
Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine · 2020
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Human-centric dialog training via offline reinforcement learning
Natasha Jaques, Judy Hanwen Shen, Asma Ghandeharioun, Craig Ferguson, Agata Lapedriza, Noah Jones, Shixiang Shane Gu, and Rosalind Picard · 2020
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Morel: Model-based offline reinforcement learning
Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, and Thorsten Joachims · 2020
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Conservative q-learning for offline reinforcement learning
Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine · 2020
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David Brandfonbrener, William F Whitney, Rajesh Ranganath, and Joan Bruna · 2021
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Decision transformer: Reinforcement learning via sequence modeling
Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, and Igor Mordatch · 2021
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Implicit behavioral cloning
Pete Florence, Corey Lynch, Andy Zeng, Oscar A Ramirez, Ayzaan Wahid, Laura Downs, Adrian Wong, Johnny Lee, Igor Mordatch, and Jonathan Tompson · 2021
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A minimalist approach to offline reinforcement learning
Scott Fujimoto and Shixiang Shane Gu · 2021
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No rl, no simulation: Learning to navigate without navigating
Meera Hahn, Devendra Chaplot, Shubham Tulsiani, Mustafa Mukadam, James M. Rehg, and Abhinav Gupta · 2021
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Is pessimism provably efficient for offline rl?
Ying Jin, Zhuoran Yang, and Zhaoran Wang · 2021
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Mt-opt: Continuous multi-task robotic reinforcement learning at scale
Dmitry Kalashnikov, Jacob Varley, Yevgen Chebotar, Benjamin Swanson, Rico Jonschkowski, Chelsea Finn, Sergey Levine, and Karol Hausman · 2021
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Offline reinforcement learning with fisher divergence critic regularization
Ilya Kostrikov, Jonathan Tompson, Rob Fergus, and Ofir Nachum · 2021
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What matters in learning from offline human demonstrations for robot manipulation
Ajay Mandlekar, Danfei Xu, Josiah Wong, Soroush Nasiriany, Chen Wang, Rohun Kulkarni, Fei-Fei Li, Silvio Savarese, Yuke Zhu, and Roberto Martín-Martín · 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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Nearly horizon-free offline reinforcement learning
Tongzheng Ren, Jialian Li, Bo Dai, Simon S Du, and Sujay Sanghavi · 2021
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Ving: Learning open-world navigation with visual goals
Dhruv Shah, Benjamin Eysenbach, Gregory Kahn, Nicholas Rhinehart, and Sergey Levine · 2021
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S4RL: Surprisingly simple self-supervision for offline reinforcement learning in robotics
Samarth Sinha, Ajay Mandlekar, and Animesh Garg · 2021
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What are the statistical limits of offline {rl} with linear function approximation?
Ruosong Wang, Dean Foster, and Sham M. Kakade · 2021
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Bellman-consistent pessimism for offline reinforcement learning
Tengyang Xie, Ching-An Cheng, Nan Jiang, Paul Mineiro, and Alekh Agarwal · 2021
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Combo: Conservative offline model-based policy optimization
Tianhe Yu, Aviral Kumar, Rafael Rafailov, Aravind Rajeswaran, Sergey Levine, and Chelsea Finn · 2021
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Is reinforcement learning more difficult than bandits? a near-optimal algorithm escaping the curse of horizon
Zihan Zhang, Xiangyang Ji, and Simon Du · 2021
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Showing your offline reinforcement learning work: Online evaluation budget matters
Vladislav Kurenkov and Sergey Kolesnikov · 2022
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