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Recent work has shown that Large Language Models (LLMs) can be incredibly effective for offline reinforcement learning (RL) by representing the traditional RL problem as a sequence modelling problem (Chen et al., 2021; Janner et al., 2021).
Efficient exploration via state marginal matching
Lisa Lee, Benjamin Eysenbach, Emilio Parisotto, Eric P. Xing, Sergey Levine, and Ruslan Salakhutdinov · 1906
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Generating diverse high-fidelity images with VQ-VAE-2
Ali Razavi, Aäron van den Oord, and Oriol Vinyals · 1906
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Dynamics-aware unsupervised discovery of skills
Archit Sharma, Shixiang Gu, Sergey Levine, Vikash Kumar, and Karol Hausman · 1907
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Learning to reach goals without reinforcement learning
Dibya Ghosh, Abhishek Gupta, Justin Fu, Ashwin Reddy, Coline Devin, Benjamin Eysenbach, and Sergey Levine · 1912
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Explore, discover and learn: Unsupervised discovery of state-covering skills
Víctor Campos, Alexander Trott, Caiming Xiong, Richard Socher, Xavier Giró-i-Nieto, and Jordi Torres · 2002
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Conservative q-learning for offline reinforcement learning
Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine · 2006
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OPAL: offline primitive discovery for accelerating offline reinforcement learning
Anurag Ajay, Aviral Kumar, Pulkit Agrawal, Sergey Levine, and Ofir Nachum · 2010
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Taming transformers for high-resolution image synthesis
Patrick Esser, Robin Rombach, and Björn Ommer · 2012
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Karol Gregor, Danilo Jimenez Rezende, and Daan Wierstra · 2016
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Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, Pieter Abbeel, and Wojciech Zaremba · 2017
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Deep reinforcement learning: An overview
Yuxi Li · 2017
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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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Paulo E. Rauber, Filipe Mutz, and Jürgen Schmidhuber · 2017
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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Generalized decision transformer for offline hindsight information matching
Hiroki Furuta, Yutaka Matsuo, and Shixiang Shane Gu · 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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Offline reinforcement learning with implicit q-learning
Ilya Kostrikov, Ashvin Nair, and Sergey Levine · 2021
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Aäron van den Oord, Oriol Vinyals, and Koray Kavukcuoglu · 2017
Cited alongside, same era.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Diversity is all you need: Learning skills without a reward function
Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, and Sergey Levine · 2018
Cited alongside, same era.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever · 2018
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D4rl: Datasets for deep data-driven reinforcement learning, 2020
Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine · 2020
Cited alongside, same era.
Sherjil Ozair, Yazhe Li, Ali Razavi, Ioannis Antonoglou, Aäron van den Oord, and Oriol Vinyals · 2021
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Learning more skills through optimistic exploration
DJ Strouse, Kate Baumli, David Warde-Farley, Vlad Mnih, and Steven Hansen · 2021
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Goal-conditioned generators of deep policies, 2022
Francesco Faccio, Vincent Herrmann, Aditya Ramesh, Louis Kirsch, and Jürgen Schmidhuber · 2022
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Robust vector quantized-variational autoencoder
Chieh-Hsin Lai, Dongmian Zou, and Gilad Lerman · 2022
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Online decision transformer, 2022
Qinqing Zheng, Amy Zhang, and Aditya Grover · 2022
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