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Offline reinforcement learning (RL) allows for the training of competent agents from offline datasets without any interaction with the environment.
ALVINN: An Autonomous Land Vehicle in a Neural Network
Dean A. Pomerleau · 1989
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Is imitation learning the route to humanoid robots?
Stefan Schaal · 1999
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Fitted Q-iteration by Advantage Weighted Regression
Gerhard Neumann and Jan Peters · 2008
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Batch Reinforcement Learning
Sascha Lange, Thomas Gabel, and Martin Riedmiller · 2012
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MuJoCo: A Physics Engine for Model-based Control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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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 · 2016
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Maximum a Posteriori Policy Optimisation
Abbas Abdolmaleki, Jost Tobias Springenberg, Yuval Tassa, Remi Munos, Nicolas Heess, and Martin Riedmiller · 2018
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JAX: Composable Transformations of Python+NumPy Programs, 2018
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang · 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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Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards
Mel Vecerik, Todd Hester, Jonathan Scholz, Fumin Wang, Olivier Pietquin, Bilal Piot, Nicolas Heess, Thomas Rothörl, Thomas Lampe, and Martin Riedmiller · 2018
Cited alongside, same era.
Off-Policy Deep Reinforcement Learning without Exploration
Scott Fujimoto, David Meger, and Doina Precup · 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.
The DeepMind JAX Ecosystem, 2020
Igor Babuschkin, Kate Baumli, Alison Bell, Surya Bhupatiraju, Jake Bruce, Peter Buchlovsky, David Budden, Trevor Cai, Aidan Clark, Ivo Danihelka, Claudio Fantacci, Jonathan Godwin, Chris Jones, Tom Hennigan, Matteo Hessel, Steven Kapturowski, Thomas Keck, Iurii Kemaev, Michael King, Lena Martens, Vladimir Mikulik, Tamara Norman, John Quan, George Papamakarios, Roman Ring, Francisco Ruiz, Alvaro Sanchez, Rosalia Schneider, Eren Sezener, Stephen Spencer, Srivatsan Srinivasan, Wojciech Stokowiec, and Fabio Viola · 2020
Cited alongside, same era.
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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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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D4RL: Datasets for Deep Data-Driven Reinforcement Learning
Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine · 2021
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A Minimalist Approach to Offline Reinforcement Learning
Scott Fujimoto and Shixiang Gu · 2021
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Offline-to-Online Reinforcement Learning via Balanced Replay and Pessimistic Q-Ensemble
Seunghyun Lee, Younggyo Seo, Kimin Lee, Pieter Abbeel, and Jinwoo Shin · 2021
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Matt Hoffman, Bobak Shahriari, John Aslanides, Gabriel Barth-Maron, Feryal Behbahani, Tamara Norman, Abbas Abdolmaleki, Albin Cassirer, Fan Yang, Kate Baumli, Sarah Henderson, Alex Novikov, Sergio Gómez Colmenarejo, Serkan Cabi, Caglar Gulcehre, Tom Le Paine, Andrew Cowie, Ziyu Wang, Bilal Piot, and Nando de Freitas · 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.
Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems
Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu · 2020
Cited alongside, same era.
Offline Reinforcement Learning Hands-On
Louis Monier, Jakub Kmec, Alexandre Laterre, Thomas Pierrot, Valentin Courgeau, Olivier Sigaud, and Karim Beguir · 2020
Cited alongside, same era.
Critic Regularized Regression
Ziyu Wang, Alexander Novikov, Konrad Zolna, Jost Tobias Springenberg, Scott Reed, Bobak Shahriari, Noah Siegel, Josh Merel, Caglar Gulcehre, Nicolas Heess, and Nando de Freitas · 2020
Cited alongside, same era.
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AW-Opt: Learning Robotic Skills with Imitation and Reinforcement at Scale
Yao Lu, Karol Hausman, Yevgen Chebotar, Mengyuan Yan, Eric Jang, Alexander Herzog, Ted Xiao, Alex Irpan, Mohi Khansari, Dmitry Kalashnikov, and Sergey Levine · 2021
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AWAC: Accelerating Online Reinforcement Learning with Offline Datasets
Ashvin Nair, Abhishek Gupta, Murtaza Dalal, and Sergey Levine · 2021
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Offline Reinforcement Learning with In-sample Q-Learning
Ilya Kostrikov, Ashvin Nair, and Sergey Levine · 2022
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Qinqing Zheng, Amy Zhang, and Aditya Grover · 2022
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