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Offline reinforcement learning has shown great promise in leveraging large pre-collected datasets for policy learning, allowing agents to forgo often-expensive online data collection.
Reinforcement Learning
Richard S. Sutton and Andrew G. Barto · 1992
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Estimating the mean and variance of the target probability distribution
D.A. Nix and A.S. Weigend · 1994
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A framework for behavioural cloning
Michael Bain and Claude Sammut · 1995
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Behavioural cloning: Phenomena, results and problems
Ivan Bratko, Tanja Urbančič, and Claude Sammut · 1995
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Tree-based batch mode reinforcement learning
Damien Ernst, Pierre Geurts, and Louis Wehenkel · 2005
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Mastering Atari with discrete world models
Danijar Hafner, Timothy Lillicrap, Mohammad Norouzi, and Jimmy Ba · 2010
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ViNG: Learning Open-World Navigation with Visual Goals
Dhruv Shah, Benjamin Eysenbach, Gregory Kahn, Nicholas Rhinehart, and Sergey Levine · 2012
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Empirical evaluation of gated recurrent neural networks on sequence modeling, 2014
Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio · 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 · 2016
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1 year, 1000 km: The oxford RobotCar dataset
Will Maddern, Geoffrey Pascoe, Chris Linegar, and Paul Newman · 2016
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Robust and efficient transfer learning with hidden parameter markov decision processes
Taylor W Killian, Samuel Daulton, George Konidaris, and Finale Doshi-Velez · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Understanding disentangling in β \beta -vae
Christopher P. Burgess, Irina Higgins, Arka Pal, Loïc Matthey, Nick Watters, Guillaume Desjardins, and Alexander Lerchner · 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 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, and Sergey Levine · 2018
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Learning to drive in a day, 2018
Alex Kendall, Jeffrey Hawke, David Janz, Przemyslaw Mazur, Daniele Reda, John-Mark Allen, Vinh-Dieu Lam, Alex Bewley, and Amar Shah · 2018
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State representation learning for control: An overview
Timothée Lesort, Natalia Díaz-Rodríguez, Jean-Francois Goudou, and David Filliat · 2018
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Off-policy deep reinforcement learning without exploration
Scott Fujimoto, David Meger, and Doina Precup · 2019
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Learning latent dynamics for planning from pixels
Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and James Davidson · 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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Disentangling disentanglement in variational autoencoders
Emile Mathieu, Tom Rainforth, N Siddharth, and Yee Whye Teh · 2019
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To tune or not to tune? adapting pretrained representations to diverse tasks
Matthew E. Peters, Sebastian Ruder, and Noah A. Smith · 2019
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Efficient off-policy meta-reinforcement learning via probabilistic context variables
Kate Rakelly, Aurick Zhou, Chelsea Finn, Sergey Levine, and Deirdre Quillen · 2019
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Behavior regularized offline reinforcement learning, 2019
Yifan Wu, George Tucker, and Ofir Nachum · 2019
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Pre-training on grayscale ImageNet improves medical image classification
Yiting Xie and David Richmond · 2019
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An optimistic perspective on offline reinforcement learning
Rishabh Agarwal, Dale Schuurmans, and Mohammad Norouzi · 2020
Actionable models: Unsupervised offline reinforcement learning of robotic skills
Yevgen Chebotar, Karol Hausman, Yao Lu, Ted Xiao, Dmitry Kalashnikov, Jacob Varley, Alex Irpan, Benjamin Eysenbach, Ryan C Julian, Chelsea Finn, and Sergey Levine · 2021
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Decision transformer: Reinforcement learning via sequence modeling
Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Misha 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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D4RL: Datasets for deep data-driven reinforcement learning, 2021
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 Shane Gu · 2021
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Scaling data-driven robotics with reward sketching and batch reinforcement learning
Serkan Cabi, Sergio Gómez Colmenarejo, Alexander Novikov, Ksenia Konyushova, Scott Reed, Rae Jeong, Konrad Zolna, Yusuf Aytar, David Budden, Mel Vecerik, Oleg Sushkov, David Barker, Jonathan Scholz, Misha Denil, Nando de Freitas, and Ziyu Wang · 2020
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Rl unplugged: Benchmarks for offline reinforcement learning, 2020
Caglar Gulcehre, Ziyu Wang, Alexander Novikov, Tom Le Paine, Sergio Gómez Colmenarejo, Konrad Zolna, Rishabh Agarwal, Josh Merel, Daniel Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matt Hoffman, Ofir Nachum, George Tucker, Nicolas Heess, and Nando deFreitas · 2020
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Array programming with NumPy
Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, Robert Kern, Matti Picus, Stephan Hoyer, Marten H. van Kerkwijk, Matthew Brett, Allan Haldane, Jaime Fernández del Río, Mark Wiebe, Pearu Peterson, Pierre Gérard-Marchant, Kevin Sheppard, Tyler Reddy, Warren Weckesser, Hameer Abbasi, Christoph Gohlke, and Travis E. Oliphant · 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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Reinforcement learning with augmented data
Misha Laskin, Kimin Lee, Adam Stooke, Lerrel Pinto, Pieter Abbeel, and Aravind Srinivas · 2020
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Learning robust dynamics through variational sparse gating
Arnav Kumar Jain, Shiva Kanth Sujit, Shruti Joshi, Vincent Michalski, Danijar Hafner, and Samira Ebrahimi Kahou · 2021
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Offline reinforcement learning with implicit q-learning, 2021
Ilya Kostrikov, Ashvin Nair, and Sergey Levine · 2021
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Should i run offline reinforcement learning or behavioral cloning?
Aviral Kumar, Joey Hong, Anikait Singh, and Sergey Levine · 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, Li Fei-Fei, Silvio Savarese, Yuke Zhu, and Roberto Martín-Martín · 2021
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Temporal predictive coding for model-based planning in latent space
Tung D Nguyen, Rui Shu, Tuan Pham, Hung Bui, and Stefano Ermon · 2021
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Dreaming: Model-based reinforcement learning by latent imagination without reconstruction
Masashi Okada and Tadahiro Taniguchi · 2021
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Offline reinforcement learning from images with latent space models
Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, and Chelsea Finn · 2021
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Decoupling value and policy for generalization in reinforcement learning
Roberta Raileanu and Rob Fergus · 2021
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MTEnv - environment interface for mulit-task reinforcement learning
Shagun Sodhani, Ludovic Denoyer, Pierre-Alexandre Kamienny, and Olivier Delalleau · 2021
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The distracting control suite – a challenging benchmark for reinforcement learning from pixels
Austin Stone, Oscar Ramirez, Kurt Konolige, and Rico Jonschkowski · 2021
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COMBO: Conservative offline model-based policy optimization, 2021
Tianhe Yu, Aviral Kumar, Rafael Rafailov, Aravind Rajeswaran, Sergey Levine, and Chelsea Finn · 2021
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Learning robust state abstractions for hidden-parameter block MDPs
Amy Zhang, Shagun Sodhani, Khimya Khetarpal, and Joelle Pineau · 2021
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VariBAD: Variational Bayes-adaptive deep RL via meta-learning
Luisa Zintgraf, Sebastian Schulze, Cong Lu, Leo Feng, Maximilian Igl, Kyriacos Shiarlis, Yarin Gal, Katja Hofmann, and Shimon Whiteson · 2021
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In defense of the unitary scalarization for deep multi-task learning, 2022
Vitaly Kurin, Alessandro De Palma, Ilya Kostrikov, Shimon Whiteson, and M. Pawan Kumar · 2022
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Revisiting design choices in offline model based reinforcement learning
Cong Lu, Philip J. Ball, Jack Parker-Holder, Michael A. Osborne, and Stephen J. Roberts · 2022
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