Fetching the paper…
Reading the bibliography…
Offline reinforcement learning (RL) allows agents to learn effective, return-maximizing policies from a static dataset.
Aviral Kumar, Xue Bin Peng, and Sergey Levine · 1912
Earlier work this paper cites.
Reinforcement learning upside down: Don’t predict rewards - just map them to actions
Jürgen Schmidhuber · 1912
Earlier work this paper cites.
Markov decision processes
Martin L Puterman · 1990
Earlier work this paper cites.
A framework for behavioural cloning
Michael Bain and Claude Sammut · 1995
Earlier work this paper cites.
Tree-based batch mode reinforcement learning
Damien Ernst, Pierre Geurts, and Louis Wehenkel · 2005
Earlier work this paper cites.
Batch reinforcement learning
Sascha Lange, Thomas Gabel, and Martin A. Riedmiller · 2012
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin A. Riedmiller, Andreas Kirkeby Fidjeland, Georg Ostrovski, Stig Petersen, Charlie Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015
Earlier work this paper cites.
Generative adversarial imitation learning
Jonathan Ho and Stefano Ermon · 2016
Earlier work this paper cites.
A distributional perspective on reinforcement learning
Marc G. Bellemare, Will Dabney, and Rémi Munos · 2017
Earlier work this paper cites.
Imitation learning: A survey of learning methods
Ahmed Hussein, Mohamed Medhat Gaber, Eyad Elyan, and Chrisina Jayne · 2017
Earlier work this paper cites.
Curiosity-driven exploration by self-supervised prediction
Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, and Trevor Darrell · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Addressing function approximation error in actor-critic methods
Scott Fujimoto, Herke Hoof, and David Meger · 2018
Earlier work this paper cites.
Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
Earlier work this paper cites.
Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
Earlier work this paper cites.
Off-policy deep reinforcement learning without exploration
Scott Fujimoto, David Meger, and Doina Precup · 2019
Earlier work this paper cites.
Are sixteen heads really better than one?
Paul Michel, Omer Levy, and Graham Neubig · 2019
Earlier work this paper cites.
Training agents using upside-down reinforcement learning
Rupesh Kumar Srivastava, Pranav Shyam, Filipe Wall Mutz, Wojciech Jaśkowski, and Jürgen Schmidhuber · 2019
Cited alongside, same era.
An optimistic perspective on offline reinforcement learning
Rishabh Agarwal, Dale Schuurmans, and Mohammad Norouzi · 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.
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.
Human-to-robot imitation in the wild
Shikhar Bahl, Abhinav Gupta, and Deepak Pathak · 2022
Later among the works it cites.
When does return-conditioned supervised learning work for offline reinforcement learning?
David Brandfonbrener, Alberto Bietti, Jacob Buckman, Romain Laroche, and Joan Bruna · 2022
Later among the works it cites.
A survey of embodied ai: From simulators to research tasks
Jiafei Duan, Samson Yu, Hui Li Tan, Hongyuan Zhu, and Cheston Tan · 2022
Later among the works it cites.
Rvs: What is essential for offline RL via supervised learning?
Scott Emmons, Benjamin Eysenbach, Ilya Kostrikov, and Sergey Levine · 2022
Later among the works it cites.
Eliciting compatible demonstrations for multi-human imitation learning
Kanishk Gandhi, Siddharth Karamcheti, Madeline Liao, and Dorsa Sadigh · 2022
Later among the works it cites.
Know your boundaries: The necessity of explicit behavioral cloning in offline rl
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu · 2020
Cited alongside, same era.
Mopo: Model-based offline policy optimization
Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Y Zou, Sergey Levine, Chelsea Finn, and Tengyu Ma · 2020
Cited alongside, same era.
Model-based offline planning
Arthur Argenson and Gabriel Dulac-Arnold · 2021
Cited alongside, same era.
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
Cited alongside, same era.
A minimalist approach to offline reinforcement learning
Scott Fujimoto and Shixiang (Shane) Gu · 2021
Cited alongside, same era.
Mastering atari with discrete world models
Danijar Hafner, Timothy P Lillicrap, Mohammad Norouzi, and Jimmy Ba · 2021
Cited alongside, same era.
Offline reinforcement learning as one big sequence modeling problem
Michael Janner, Qiyang Li, and Sergey Levine · 2021
Cited alongside, same era.
Wonjoon Goo and Scott Niekum · 2022
Later among the works it cites.
Training compute-optimal large language models
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al · 2022
Later among the works it cites.
Planning with diffusion for flexible behavior synthesis
Michael Janner, Yilun Du, Joshua Tenenbaum, and Sergey Levine · 2022
Later among the works it cites.
Multi-game decision transformers
Kuang-Huei Lee, Ofir Nachum, Sherry Yang, Lisa Lee, C. Daniel Freeman, Sergio Guadarrama, Ian Fischer, Winnie Xu, Eric Jang, Henryk Michalewski, and Igor Mordatch · 2022
Later among the works it cites.
Yiren Lu, Justin Fu, George Tucker, Xinlei Pan, Eli Bronstein, Becca Roelofs, Benjamin Sapp, Brandyn White, Aleksandra Faust, Shimon Whiteson, Dragomir Anguelov, and Sergey Levine · 2022
Later among the works it cites.
Data-efficient pipeline for offline reinforcement learning with limited data
Allen Nie, Yannis Flet-Berliac, Deon Jordan, William Steenbergen, and Emma Brunskill · 2022
Later among the works it cites.
You can’t count on luck: Why decision transformers and rvs fail in stochastic environments
Keiran Paster, Sheila McIlraith, and Jimmy Ba · 2022
Later among the works it cites.
CORL: Research-oriented deep offline reinforcement learning library
Denis Tarasov, Alexander Nikulin, Dmitry Akimov, Vladislav Kurenkov, and Sergey Kolesnikov · 2022
Later among the works it cites.
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
Later among the works it cites.
Offline q-learning on diverse multi-task data both scales and generalizes
Aviral Kumar, Rishabh Agarwal, Xinyang Geng, George Tucker, and Sergey Levine · 2023
Closest in time.
Bridging rl theory and practice with the effective horizon, 2023
Cassidy Laidlaw, Stuart Russell, and Anca Dragan · 2023
Closest in time.
A survey on offline reinforcement learning: Taxonomy, review, and open problems
Rafael Figueiredo Prudencio, Marcos ROA Maximo, and Esther Luna Colombini · 2023
Closest in time.
Mimicplay: Long-horizon imitation learning by watching human play
Chen Wang, Linxi Fan, Jiankai Sun, Ruohan Zhang, Li Fei-Fei, Danfei Xu, Yuke Zhu, and Anima Anandkumar · 2023
Closest in time.