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Training autonomous agents that can learn new tasks from only a handful of demonstrations is a long-standing problem in machine learning.
Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-… hook
Jürgen Schmidhuber · 1987
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2001
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Automatic data augmentation for generalization in deep reinforcement learning
Roberta Raileanu, Maxwell Goldstein, Denis Yarats, Ilya Kostrikov, and Rob Fergus · 2006
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A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey Gordon, and Drew Bagnell · 2011
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Rl$ˆ2$: Fast reinforcement learning via slow reinforcement learning
Yan Duan, John Schulman, Xi Chen, Peter L. Bartlett, Ilya Sutskever, and Pieter Abbeel · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Task-embedded control networks for few-shot imitation learning
Stephen James, Michael Bloesch, and Andrew J Davison · 2018
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Revisiting the arcade learning environment: Evaluation protocols and open problems for general agents
Marlos C Machado, Marc G Bellemare, Erik Talvitie, Joel Veness, Matthew Hausknecht, and Michael Bowling · 2018
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Backplay:” man muss immer umkehren”
Cinjon Resnick, Roberta Raileanu, Sanyam Kapoor, Alexander Peysakhovich, Kyunghyun Cho, and Joan Bruna · 2018
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Learning montezuma’s revenge from a single demonstration
Tim Salimans and Richard Chen · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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Generalization in reinforcement learning with selective noise injection and information bottleneck
Maximilian Igl, Kamil Ciosek, Yingzhen Li, Sebastian Tschiatschek, Cheng Zhang, Sam Devlin, and Katja Hofmann · 2019
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Language models are few-shot learners, 2020
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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Leveraging procedural generation to benchmark reinforcement learning, 2020
Karl Cobbe, Christopher Hesse, Jacob Hilton, and John Schulman · 2020
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Conservative q-learning for offline reinforcement learning, 2020
Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine · 2020
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The nethack learning environment
Heinrich Küttler, Nantas Nardelli, Alexander H. Miller, Roberta Raileanu, Marco Selvatici, Edward Grefenstette, and Tim Rocktäschel · 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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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 as one big sequence modeling problem
Michael Janner, Qiyang Li, and Sergey Levine · 2021
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Offline meta-reinforcement learning with advantage weighting
Eric Mitchell, Rafael Rafailov, Xue Bin Peng, Sergey Levine, 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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Minihack the planet: A sandbox for open-ended reinforcement learning research
Mikayel Samvelyan, Robert Kirk, Vitaly Kurin, Jack Parker-Holder, Minqi Jiang, Eric Hambro, Fabio Petroni, Heinrich Kuttler, Edward Grefenstette, and Tim Rocktäschel · 2021
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An explanation of in-context learning as implicit bayesian inference
Sang Michael Xie, Aditi Raghunathan, Percy Liang, and Tengyu Ma · 2021
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Is conditional generative modeling all you need for decision-making?
Anurag Ajay, Yilun Du, Abhi Gupta, Joshua Tenenbaum, Tommi Jaakkola, and Pulkit Agrawal · 2022
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What learning algorithm is in-context learning? investigations with linear models
Offline meta-reinforcement learning with online self-supervision
Vitchyr H Pong, Ashvin V Nair, Laura M Smith, Catherine Huang, and Sergey Levine · 2022
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Scott Reed, Konrad Zolna, Emilio Parisotto, Sergio Gomez Colmenarejo, Alexander Novikov, Gabriel Barth-Maron, Mai Gimenez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, et al · 2022
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Can wikipedia help offline reinforcement learning?
Machel Reid, Yutaro Yamada, and Shixiang Shane Gu · 2022
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Michael S Ryoo, Keerthana Gopalakrishnan, Kumara Kahatapitiya, Ted Xiao, Kanishka Rao, Austin Stone, Yao Lu, Julian Ibarz, and Anurag Arnab · 2022
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Ekin Akyürek, Dale Schuurmans, Jacob Andreas, Tengyu Ma, and Denny Zhou · 2022
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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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Transdreamer: Reinforcement learning with transformer world models
Chang Chen, Yi-Fu Wu, Jaesik Yoon, and Sungjin Ahn · 2022
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Hierarchical decision transformer
André Correia and Luís A Alexandre · 2022
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Why can gpt learn in-context? language models secretly perform gradient descent as meta optimizers
Damai Dai, Yutao Sun, Li Dong, Yaru Hao, Zhifang Sui, and Furu Wei · 2022
Cited alongside, same era.
Predictability and surprise in large generative models
Deep Ganguli, Danny Hernandez, Liane Lovitt, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, Nova Dassarma, Dawn Drain, Nelson Elhage, Sheer El Showk, Stanislav Fort, Zac Hatfield-Dodds, Tom Henighan, Scott Johnston, Andy Jones, Nicholas Joseph, Jackson Kernian, Shauna Kravec, Ben Mann, Neel Nanda, Kamal Ndousse, Catherine Olsson, Daniela Amodei, Tom Brown, Jared Kaplan, Sam McCandlish, Christopher Olah, Dario Amodei, and Jack Clark · 2022
Cited alongside, same era.
What can transformers learn in-context? A case study of simple function classes
Shivam Garg, Dimitris Tsipras, Percy Liang, and Gregory Valiant · 2022
Cited alongside, same era.
Exploration via elliptical episodic bonuses
Mikael Henaff, Roberta Raileanu, Minqi Jiang, and Tim Rocktäschel · 2022
Cited alongside, same era.
Max Siebenborn, Boris Belousov, Junning Huang, and Jan Peters · 2022
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Transformers learn in-context by gradient descent
Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo, João Sacramento, Alexander Mordvintsev, Andrey Zhmoginov, and Max Vladymyrov · 2022
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Emergent abilities of large language models, 2022
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus · 2022
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Prompting decision transformer for few-shot policy generalization, 2022
Mengdi Xu, Yikang Shen, Shun Zhang, Yuchen Lu, Ding Zhao, Joshua B. Tenenbaum, and Chuang Gan · 2022
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Taku Yamagata, Ahmed Khalil, and Raul Santos-Rodriguez · 2022
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Online decision transformer
Qinqing Zheng, Amy Zhang, and Aditya Grover · 2022
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A survey of meta-reinforcement learning
Jacob Beck, Risto Vuorio, Evan Zheran Liu, Zheng Xiong, Luisa Zintgraf, Chelsea Finn, and Shimon Whiteson · 2023
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A theory of emergent in-context learning as implicit structure induction
Michael Hahn and Navin Goyal · 2023
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Decision transformer under random frame dropping
Kaizhe Hu, Ray Chen Zheng, Yang Gao, and Huazhe Xu · 2023
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Supervised pretraining can learn in-context reinforcement learning, 2023
Jonathan N. Lee, Annie Xie, Aldo Pacchiano, Yash Chandak, Chelsea Finn, Ofir Nachum, and Emma Brunskill · 2023
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Contextual transformer for offline reinforcement learning, 2023
Runji Lin, Ye Li, Xidong Feng, Zhaowei Zhang, Xian Hong Wu Fung, Haifeng Zhang, Jun Wang, Yali Du, and Yaodong Yang · 2023
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A unified analysis of adagrad with weighted aggregation and momentum acceleration, 2023
Li Shen, Congliang Chen, Fangyu Zou, Zequn Jie, Ju Sun, and Wei Liu · 2023
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Shyam Sudhakaran and Sebastian Risi · 2023
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Smart: Self-supervised multi-task pretraining with control transformers
Yanchao Sun, Shuang Ma, Ratnesh Madaan, Rogerio Bonatti, Furong Huang, and Ashish Kapoor · 2023
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Hyper-decision transformer for efficient online policy adaptation
Mengdi Xu, Yuchen Lu, Yikang Shen, Shun Zhang, Ding Zhao, and Chuang Gan · 2023
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