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Offline reinforcement learning (RL) learns policies entirely from static datasets, thereby avoiding the challenges associated with online data collection.
Stabilizing off-policy q-learning via bootstrapping error reduction
Aviral Kumar, Justin Fu, George Tucker, and Sergey Levine · 1906
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Accelerating online reinforcement learning with offline datasets
Ashvin Nair, Murtaza Dalal, Abhishek Gupta, and Sergey Levine · 2006
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Accelerating online reinforcement learning with offline datasets
Ashvin Nair, Murtaza Dalal, Abhishek Gupta, and Sergey Levine · 2006
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Batch reinforcement learning
Sascha Lange, Thomas Gabel, and Martin Riedmiller · 2012
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A comprehensive survey on safe reinforcement learning
Javier Garcıa and Fernando Fernández · 2015
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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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Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
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Constrained policy optimization
Joshua Achiam, David Held, Aviv Tamar, and Pieter Abbeel · 2017
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Safe policy improvement with baseline bootstrapping
Romain Laroche, Paul Trichelair, and Rémi Tachet des Combes · 2017
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Dopamine: A Research Framework for Deep Reinforcement Learning
Pablo Samuel Castro, Subhodeep Moitra, Carles Gelada, Saurabh Kumar, and Marc G. Bellemare · 2018
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Soft actor-critic algorithms and applications
Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen, George Tucker, Sehoon Ha, Jie Tan, Vikash Kumar, Henry Zhu, Abhishek Gupta, Pieter Abbeel, and Sergey Levine · 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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Reinforcement learning and control as probabilistic inference: Tutorial and review
Sergey Levine · 2018
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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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Way off-policy batch deep reinforcement learning of implicit human preferences in dialog
Natasha Jaques, Asma Ghandeharioun, Judy Hanwen Shen, Craig Ferguson, Agata Lapedriza, Noah Jones, Shixiang Gu, and Rosalind Picard · 2019
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Data-driven deep reinforcement learning
Aviral Kumar · 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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Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction
Aviral Kumar, Justin Fu, George Tucker, and Sergey Levine · 2019
Cited alongside, same era.
Algaedice: Policy gradient from arbitrary experience
Ofir Nachum, Bo Dai, Ilya Kostrikov, Yinlam Chow, Lihong Li, and Dale Schuurmans · 2019
Cited alongside, same era.
Advantage-weighted regression: Simple and scalable off-policy reinforcement learning
Xue Bin Peng, Aviral Kumar, Grace Zhang, and Sergey Levine · 2019
Cited alongside, same era.
Behavior regularized offline reinforcement learning
Yifan Wu, George Tucker, and Ofir Nachum · 2019
Cited alongside, same era.
An optimistic perspective on offline reinforcement learning
Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble
Gaon An, Seungyong Moon, Jang-Hyun Kim, and Hyun Oh Song · 2021
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Offline RL without off-policy evaluation
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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Bridge data: Boosting generalization of robotic skills with cross-domain datasets
Frederik Ebert, Yanlai Yang, Karl Schmeckpeper, Bernadette Bucher, Georgios Georgakis, Kostas Daniilidis, Chelsea Finn, and Sergey Levine · 2021
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Rishabh Agarwal, Dale Schuurmans, and Mohammad Norouzi · 2020
Cited alongside, same era.
Arthur Argenson and Gabriel Dulac-Arnold · 2020
Cited alongside, same era.
Heteroskedastic and imbalanced deep learning with adaptive regularization
Kaidi Cao, Yining Chen, Junwei Lu, Nikos Arechiga, Adrien Gaidon, and Tengyu Ma · 2020
Cited alongside, same era.
D4rl: Datasets for deep data-driven reinforcement learning
Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine · 2020
Cited alongside, same era.
Rl unplugged: Benchmarks for offline reinforcement learning
Caglar Gulcehre, Ziyu Wang, Alexander Novikov, Tom Le Paine, Sergio Gómez Colmenarejo, Konrad Zolna, Rishabh Agarwal, Josh Merel, Daniel Mankowitz, Cosmin Paduraru, et al · 2020
Cited alongside, same era.
Is pessimism provably efficient for offline rl?
Ying Jin, Zhuoran Yang, and Zhaoran Wang · 2020
Cited alongside, same era.
Morel: Model-based offline reinforcement learning
Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, and Thorsten Joachims · 2020
Cited alongside, same era.
Conservative q-learning for offline reinforcement learning
Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine · 2020
Cited alongside, same era.
Large scale interactive motion forecasting for autonomous driving: The waymo open motion dataset
Scott Ettinger, Shuyang Cheng, Benjamin Caine, Chenxi Liu, Hang Zhao, Sabeek Pradhan, Yuning Chai, Ben Sapp, Charles R Qi, Yin Zhou, et al · 2021
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Benchmarks for deep off-policy evaluation
Justin Fu, Mohammad Norouzi, Ofir Nachum, George Tucker, ziyu wang, Alexander Novikov, Mengjiao Yang, Michael R Zhang, Yutian Chen, Aviral Kumar, Cosmin Paduraru, Sergey Levine, and Thomas Paine · 2021
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Emaq: Expected-max q-learning operator for simple yet effective offline and online rl
Seyed Kamyar Seyed Ghasemipour, Dale Schuurmans, and Shixiang Shane Gu · 2021
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Implicit under-parameterization inhibits data-efficient deep reinforcement learning
Aviral Kumar, Rishabh Agarwal, Dibya Ghosh, and Sergey Levine · 2021
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Representation balancing offline model-based reinforcement learning
Byung-Jun Lee, Jongmin Lee, and Kee-Eung Kim · 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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Offline reinforcement learning from images with latent space models
Rafael Rafailov, Tianhe Yu, A. Rajeswaran, and Chelsea Finn · 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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Offline reinforcement learning as anti-exploration
Shideh Rezaeifar, Robert Dadashi, Nino Vieillard, Léonard Hussenot, Olivier Bachem, Olivier Pietquin, and Matthieu Geist · 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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Should i run offline reinforcement learning or behavioral cloning?
Aviral Kumar, Joey Hong, Anikait Singh, and Sergey Levine · 2022
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Don’t change the algorithm, change the data: Exploratory data for offline reinforcement learning
Denis Yarats, David Brandfonbrener, Hao Liu, Michael Laskin, Pieter Abbeel, Alessandro Lazaric, and Lerrel Pinto · 2022
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