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Following the success of the in-context learning paradigm in large-scale language and computer vision models, the recently emerging field of in-context reinforcement learning is experiencing a rapid growth.
The arcade learning environment: An evaluation platform for general agents
M. G. Bellemare, Y. Naddaf, J. Veness, and M. Bowling · 2013
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On the properties of neural machine translation: Encoder-decoder approaches
Kyunghyun Cho, Bart Van Merriënboer, Dzmitry Bahdanau, and Yoshua Bengio · 2014
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Rl 2 : 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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Learning to reinforcement learn
Jane X Wang, Zeb Kurth-Nelson, Dhruva Tirumala, Hubert Soyer, Joel Z Leibo, Remi Munos, Charles Blundell, Dharshan Kumaran, and Matt Botvinick · 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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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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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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Leveraging procedural generation to benchmark reinforcement learning
Karl Cobbe, Christopher Hesse, Jacob Hilton, and John Schulman · 2019
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Varibad: A very good method for bayes-adaptive deep rl via meta-learning
Luisa Zintgraf, Kyriacos Shiarlis, Maximilian Igl, Sebastian Schulze, Yarin Gal, Katja Hofmann, and Shimon Whiteson · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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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
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Babyai 1.1, 2020
David Yu-Tung Hui, Maxime Chevalier-Boisvert, Dzmitry Bahdanau, and Yoshua Bengio · 2020
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Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters
Jeff Rasley, Samyam Rajbhandari, Olatunji Ruwase, and Yuxiong He · 2020
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Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Karol Hausman, Chelsea Finn, and Sergey Levine · 2020
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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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Rl unplugged: A suite of benchmarks for offline reinforcement learning, 2021
Caglar Gulcehre, Ziyu Wang, Alexander Novikov, Tom Le Paine, Sergio Gomez 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 de Freitas · 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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Transformers can do bayesian inference
Samuel Müller, Noah Hollmann, Sebastian Pineda Arango, Josif Grabocka, and Frank Hutter · 2021
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Open-ended learning leads to generally capable agents
Open Ended Learning Team, Adam Stooke, Anuj Mahajan, Catarina Barros, Charlie Deck, Jakob Bauer, Jakub Sygnowski, Maja Trebacz, Max Jaderberg, Michael Mathieu, et al · 2021
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Data distributional properties drive emergent in-context learning in transformers
Stephanie Chan, Adam Santoro, Andrew Lampinen, Jane Wang, Aaditya Singh, Pierre Richemond, James McClelland, and Felix Hill · 2022
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On transforming reinforcement learning by transformer: The development trajectory
Shengchao Hu, Li Shen, Ya Zhang, Yixin Chen, and Dacheng Tao · 2022
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General-purpose in-context learning by meta-learning transformers
Louis Kirsch, James Harrison, Jascha Sohl-Dickstein, and Luke Metz · 2022
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In-context reinforcement learning with algorithm distillation
Michael Laskin, Luyu Wang, Junhyuk Oh, Emilio Parisotto, Stephen Spencer, Richie Steigerwald, DJ Strouse, Steven Hansen, Angelos Filos, Ethan Brooks, et al · 2022
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Multi-game decision transformers
Kuang-Huei Lee, Ofir Nachum, Mengjiao Sherry Yang, Lisa Lee, Daniel Freeman, Sergio Guadarrama, Ian Fischer, Winnie Xu, Eric Jang, Henryk Michalewski, et al · 2022
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Transformers are meta-reinforcement learners
Luckeciano C Melo · 2022
Cited alongside, same era.
Train short, test long: Attention with linear biases enables input length extrapolation, 2022
Ofir Press, Noah A. Smith, and Mike Lewis · 2022
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A generalist agent, 2022
Scott Reed, Konrad Zolna, Emilio Parisotto, Sergio Gomez Colmenarejo, Alexander Novikov, Gabriel Barth-Maron, Mai Gimenez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, Tom Eccles, Jake Bruce, Ali Razavi, Ashley Edwards, Nicolas Heess, Yutian Chen, Raia Hadsell, Oriol Vinyals, Mahyar Bordbar, and Nando de Freitas · 2022
Cited alongside, same era.
Transformers in reinforcement learning: a survey
Pranav Agarwal, Aamer Abdul Rahman, Pierre-Luc St-Charles, Simon JD Prince, and Samira Ebrahimi Kahou · 2023
Cited alongside, same era.
D5rl: Diverse datasets for data-driven deep reinforcement learning
Rafael Rafailov, Kyle Beltran Hatch, Anikait Singh, Aviral Kumar, Laura Smith, Ilya Kostrikov, Philippe Hansen-Estruch, Victor Kolev, Philip J Ball, Jiajun Wu, et al · 2023
Later among the works it cites.
Generalization to new sequential decision making tasks with in-context learning
Sharath Chandra Raparthy, Eric Hambro, Robert Kirk, Mikael Henaff, and Roberta Raileanu · 2023
Later among the works it cites.
In-context reinforcement learning for variable action spaces
Viacheslav Sinii, Alexander Nikulin, Vladislav Kurenkov, Ilya Zisman, and Sergey Kolesnikov · 2023
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Human-timescale adaptation in an open-ended task space
Adaptive Agent Team, Jakob Bauer, Kate Baumli, Satinder Baveja, Feryal Behbahani, Avishkar Bhoopchand, Nathalie Bradley-Schmieg, Michael Chang, Natalie Clay, Adrian Collister, et al · 2023
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Sequential modeling enables scalable learning for large vision models
Yutong Bai, Xinyang Geng, Karttikeya Mangalam, Amir Bar, Alan Yuille, Trevor Darrell, Jitendra Malik, and Alexei A Efros · 2023
Cited alongside, same era.
Understanding in-context learning in transformers and llms by learning to learn discrete functions
Satwik Bhattamishra, Arkil Patel, Phil Blunsom, and Varun Kanade · 2023
Cited alongside, same era.
Maxime Chevalier-Boisvert, Bolun Dai, Mark Towers, Rodrigo de Lazcano, Lucas Willems, Salem Lahlou, Suman Pal, Pablo Samuel Castro, and Jordan Terry · 2023
Cited alongside, same era.
Amago: Scalable in-context reinforcement learning for adaptive agents
Jake Grigsby, Linxi Fan, and Yuke Zhu · 2023
Cited alongside, same era.
Pre-training to learn in context
Yuxian Gu, Li Dong, Furu Wei, and Minlie Huang · 2023
Cited alongside, same era.
Dungeons and data: A large-scale nethack dataset, 2023
Eric Hambro, Roberta Raileanu, Danielle Rothermel, Vegard Mella, Tim Rocktäschel, Heinrich Küttler, and Naila Murray · 2023
Cited alongside, same era.
Towards general-purpose in-context learning agents
Louis Kirsch, James Harrison, C. Freeman, Jascha Sohl-Dickstein, and Jürgen Schmidhuber · 2023
Cited alongside, same era.
Johannes Von Oswald, Eyvind Niklasson, Ettore Randazzo, João Sacramento, Alexander Mordvintsev, Andrey Zhmoginov, and Max Vladymyrov · 2023
Later among the works it cites.
Emergence of in-context reinforcement learning from noise distillation
Ilya Zisman, Vladislav Kurenkov, Alexander Nikulin, Viacheslav Sinii, and Sergey Kolesnikov · 2023
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In-context language learning: Arhitectures and algorithms
Ekin Akyürek, Bailin Wang, Yoon Kim, and Jacob Andreas · 2024
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Recurrent hypernetworks are surprisingly strong in meta-rl
Jacob Beck, Risto Vuorio, Zheng Xiong, and Shimon Whiteson · 2024
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FlashAttention-2: Faster attention with better parallelism and work partitioning
Tri Dao · 2024
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Towards multimodal in-context learning for vision & language models
Sivan Doveh, Shaked Perek, M Jehanzeb Mirza, Amit Alfassy, Assaf Arbelle, Shimon Ullman, and Leonid Karlinsky · 2024
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Jack of All Trades, Master of Some, a Multi-Purpose Transformer Agent
Quentin Gallouédec, Edward Beeching, Clément Romac, and Emmanuel Dellandréa · 2024
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Is mamba capable of in-context learning?
Riccardo Grazzi, Julien Siems, Simon Schrodi, Thomas Brox, and Frank Hutter · 2024
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Katakomba: Tools and benchmarks for data-driven nethack
Vladislav Kurenkov, Alexander Nikulin, Denis Tarasov, and Sergey Kolesnikov · 2024
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Position: Foundation agents as the paradigm shift for decision making
Xiaoqian Liu, Xingzhou Lou, Jianbin Jiao, and Junge Zhang · 2024
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Structured state space models for in-context reinforcement learning
Chris Lu, Yannick Schroecker, Albert Gu, Emilio Parisotto, Jakob Foerster, Satinder Singh, and Feryal Behbahani · 2024
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The generalization gap in offline reinforcement learning, 2024
Ishita Mediratta, Qingfei You, Minqi Jiang, and Roberta Raileanu · 2024
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Can mamba learn how to learn? a comparative study on in-context learning tasks
Jongho Park, Jaeseung Park, Zheyang Xiong, Nayoung Lee, Jaewoong Cho, Samet Oymak, Kangwook Lee, and Dimitris Papailiopoulos · 2024
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Hierarchical transformers are efficient meta-reinforcement learners
Gresa Shala, André Biedenkapp, and Josif Grabocka · 2024
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Cross-episodic curriculum for transformer agents
Lucy Xiaoyang Shi, Yunfan Jiang, Jake Grigsby, Linxi Fan, and Yuke Zhu · 2024
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Visual autoregressive modeling: Scalable image generation via next-scale prediction
Keyu Tian, Yi Jiang, Zehuan Yuan, Bingyue Peng, and Liwei Wang · 2024
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William L Tong and Cengiz Pehlevan · 2024
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Linear transformers are versatile in-context learners
Max Vladymyrov, Johannes von Oswald, Mark Sandler, and Rong Ge · 2024
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Transformers learn temporal difference methods for in-context reinforcement learning
Jiuqi Wang, Ethan Blaser, Hadi Daneshmand, and Shangtong Zhang · 2024
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