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Offline reinforcement learning (RL) methods can generally be categorized into two types: RL-based and Imitation-based.
Alvinn: An autonomous land vehicle in a neural network
Dean A Pomerleau · 1989
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Q-learning
Christopher JCH Watkins and Peter Dayan · 1992
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Introduction to reinforcement learning
Richard S Sutton, Andrew G Barto, et al · 1998
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A natural policy gradient
Sham M. Kakade · 2001
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Stochastic neighbor embedding
Geoffrey E. Hinton and Sam T. Roweis · 2002
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Reinforcement learning by reward-weighted regression for operational space control
Jan Peters and Stefan Schaal · 2007
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Batch reinforcement learning
Sascha Lange, Thomas Gabel, and Martin Riedmiller · 2012
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Deterministic policy gradient algorithms
David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, and Martin A. Riedmiller · 2014
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael I. Jordan, and Philipp Moritz · 2015
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Layer normalization
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Transfer from simulation to real world through learning deep inverse dynamics model
Paul Christiano, Zain Shah, Igor Mordatch, Jonas Schneider, Trevor Blackwell, Joshua Tobin, Pieter Abbeel, and Wojciech Zaremba · 2016
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Curiosity-driven exploration by self-supervised prediction
Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, and Trevor Darrell · 2017
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Automatic goal generation for reinforcement learning agents
Carlos Florensa, David Held, Xinyang Geng, and Pieter Abbeel · 2018
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Addressing function approximation error in actor-critic methods
Scott Fujimoto, Herke van Hoof, and David Meger · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
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Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
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Behavioral cloning from observation
Faraz Torabi, Garrett Warnell, and Peter Stone · 2018
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Deep reinforcement learning and the deadly triad
Hado Van Hasselt, Yotam Doron, Florian Strub, Matteo Hessel, Nicolas Sonnerat, and Joseph Modayil · 2018
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Lipschitz regularity of deep neural networks: analysis and efficient estimation
Aladin Virmaux and Kevin Scaman · 2018
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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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Reward-conditioned policies
Aviral Kumar, Xue Bin Peng, and Sergey Levine · 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
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.
Training agents using upside-down reinforcement learning
Rupesh Kumar Srivastava, Pranav Shyam, Filipe Mutz, Wojciech Jaśkowski, and Jürgen Schmidhuber · 2019
Cited alongside, same era.
Behavior regularized offline reinforcement learning
Yifan Wu, George Tucker, and Ofir Nachum · 2019
Cited alongside, same era.
BAIL: best-action imitation learning for batch deep reinforcement learning
Xinyue Chen, Zijian Zhou, Zheng Wang, Che Wang, Yanqiu Wu, and Keith W. Ross · 2020
Cited alongside, same era.
D4rl: Datasets for deep data-driven reinforcement learning
Learning to reach goals via iterated supervised learning
Dibya Ghosh, Abhishek Gupta, Ashwin Reddy, Justin Fu, Coline Manon Devin, Benjamin Eysenbach, and Sergey Levine · 2021
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Regularized behavior value estimation
Caglar Gulcehre, Sergio Gómez Colmenarejo, Ziyu Wang, Jakub Sygnowski, Thomas Paine, Konrad Zolna, Yutian Chen, Matthew Hoffman, Razvan Pascanu, and Nando de Freitas · 2021
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Offline reinforcement learning as one big sequence modeling problem
Michael Janner, Qiyang Li, and Sergey Levine · 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, Rob Fergus, Jonathan Tompson, and Ofir Nachum · 2021
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Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine · 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.
CURL: contrastive unsupervised representations for reinforcement learning
Michael Laskin, Aravind Srinivas, and Pieter Abbeel · 2020
Cited alongside, same era.
Offline reinforcement learning: Tutorial, review, and perspectives on open problems
Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu · 2020
Cited alongside, same era.
Learning latent plans from play
Corey Lynch, Mohi Khansari, Ted Xiao, Vikash Kumar, Jonathan Tompson, Sergey Levine, and Pierre Sermanet · 2020
Cited alongside, same era.
Accelerating online reinforcement learning with offline datasets
Ashvin Nair, Murtaza Dalal, Abhishek Gupta, and Sergey Levine · 2020
Cited alongside, same era.
Ilya Kostrikov, Ashvin Nair, and Sergey Levine · 2021
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Model selection for offline reinforcement learning: Practical considerations for healthcare settings
Shengpu Tang and Jenna Wiens · 2021
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Uncertainty weighted actor-critic for offline reinforcement learning
Yue Wu, Shuangfei Zhai, Nitish Srivastava, Joshua M. Susskind, Jian Zhang, Ruslan Salakhutdinov, and Hanlin Goh · 2021
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Offline reinforcement learning with soft behavior regularization
Haoran Xu, Xianyuan Zhan, Jianxiong Li, and Honglei Yin · 2021
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Learning invariant representations for reinforcement learning without reconstruction
Amy Zhang, Rowan Thomas McAllister, Roberto Calandra, Yarin Gal, and Sergey Levine · 2021
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When does return-conditioned supervised learning work for offline reinforcement learning?
David Brandfonbrener, Alberto Bietti, Jacob Buckman, Romain Laroche, and Joan Bruna · 2022
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Imitating past successes can be very suboptimal
Benjamin Eysenbach, Soumith Udatha, Sergey Levine, and Ruslan Salakhutdinov · 2022
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Learning smooth neural functions via lipschitz regularization
Hsueh-Ti Derek Liu, Francis Williams, Alec Jacobson, Sanja Fidler, and Or Litany · 2022
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Plan your target and learn your skills: Transferable state-only imitation learning via decoupled policy optimization
Minghuan Liu, Zhengbang Zhu, Yuzheng Zhuang, Weinan Zhang, Jianye Hao, Yong Yu, and Jun Wang · 2022
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Offline reinforcement learning with value-based episodic memory
Xiaoteng Ma, Yiqin Yang, Hao Hu, Qihan Liu, Jun Yang, Chongjie Zhang, Qianchuan Zhao, and Bin Liang · 2022
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Discriminator-weighted offline imitation learning from suboptimal demonstrations
Haoran Xu, Xianyuan Zhan, Honglei Yin, and Huiling Qin · 2022
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Constraints penalized q-learning for safe offline reinforcement learning
Haoran Xu, Xianyuan Zhan, and Xiangyu Zhu · 2022
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Rethinking goal-conditioned supervised learning and its connection to offline rl
Rui Yang, Yiming Lu, Wenzhe Li, Hao Sun, Meng Fang, Yali Du, Xiu Li, Lei Han, and Chongjie Zhang · 2022
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How to leverage unlabeled data in offline reinforcement learning
Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Karol Hausman, Chelsea Finn, and Sergey Levine · 2022
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Deepthermal: Combustion optimization for thermal power generating units using offline reinforcement learning
Xianyuan Zhan, Haoran Xu, Yue Zhang, Xiangyu Zhu, Honglei Yin, and Yu Zheng · 2022
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Model-based offline planning with trajectory pruning
Xianyuan Zhan, Xiangyu Zhu, and Haoran Xu · 2022
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Offline rl with no ood actions: In-sample learning via implicit value regularization
Haoran Xu, Li Jiang, Jianxiong Li, Zhuoran Yang, Zhaoran Wang, Victor Wai Kin Chan, and Xianyuan Zhan · 2023
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