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Imitation Learning (IL) is one of the most widely used methods in machine learning.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis · 2016
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Deep learning scaling is predictable, empirically
Joel Hestness, Sharan Narang, Newsha Ardalani, Gregory Diamos, Heewoo Jun, Hassan Kianinejad, Md Patwary, Mostofa Ali, Yang Yang, and Yanqi Zhou · 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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Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures
Lasse Espeholt, Hubert Soyer, Remi Munos, Karen Simonyan, Vlad Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, et al · 2018
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Behavioral cloning from observation
Faraz Torabi, Garrett Warnell, and Peter Stone · 2018
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Causal confusion in imitation learning
Pim De Haan, Dinesh Jayaraman, and Sergey Levine · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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A constructive prediction of the generalization error across scales
Jonathan S Rosenfeld, Amir Rosenfeld, Yonatan Belinkov, and Nir Shavit · 2019
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Scaling laws for autoregressive generative modeling
Tom Henighan, Jared Kaplan, Mor Katz, Mark Chen, Christopher Hesse, Jacob Jackson, Heewoo Jun, Tom B Brown, Prafulla Dhariwal, Scott Gray, et al · 2020
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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 · 2020
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The nethack learning environment
Heinrich Küttler, Nantas Nardelli, Alexander Miller, Roberta Raileanu, Marco Selvatici, Edward Grefenstette, and Tim Rocktäschel · 2020
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Fighting copycat agents in behavioral cloning from observation histories
Chuan Wen, Jierui Lin, Trevor Darrell, Dinesh Jayaraman, and Yang Gao · 2020
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Scaling laws for acoustic models
Jasha Droppo and Oguz Elibol · 2021
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First return, then explore
Adrien Ecoffet, Joost Huizinga, Joel Lehman, Kenneth O. Stanley, and Jeff Clune · 2021
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Insights from the neurips 2021 nethack challenge
Eric Hambro, Sharada Mohanty, Dmitrii Babaev, Minwoo Byeon, Dipam Chakraborty, Edward Grefenstette, Minqi Jiang, Jo Daejin, Anssi Kanervisto, Jongmin Kim, et al · 2021
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Danny Hernandez, Jared Kaplan, Tom Henighan, and Sam McCandlish · 2021
Cited alongside, same era.
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
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Modeling strong and human-like gameplay with kl-regularized search
Athul Paul Jacob, David J Wu, Gabriele Farina, Adam Lerer, Hengyuan Hu, Anton Bakhtin, Jacob Andreas, and Noam Brown · 2022
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Improving intrinsic exploration with language abstractions
Jesse Mu, Victor Zhong, Roberta Raileanu, Minqi Jiang, Noah Goodman, Tim Rocktäschel, and Edward Grefenstette · 2022
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Scaling laws for generative mixed-modal language models
Armen Aghajanyan, Lili Yu, Alexis Conneau, Wei-Ning Hsu, Karen Hambardzumyan, Susan Zhang, Stephen Roller, Naman Goyal, Omer Levy, and Luke Zettlemoyer · 2023
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Atari-5: Distilling the arcade learning environment down to five games
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Stable-baselines3: Reliable reinforcement learning implementations
Antonin Raffin, Ashley Hill, Adam Gleave, Anssi Kanervisto, Maximilian Ernestus, and Noah Dormann · 2021
Cited alongside, same era.
Data-efficient reinforcement learning with self-predictive representations
Max Schwarzer, Ankesh Anand, Rishab Goel, R Devon Hjelm, Aaron Courville, and Philip Bachman · 2021
Cited alongside, same era.
Video pretraining (vpt): Learning to act by watching unlabeled online videos
Bowen Baker, Ilge Akkaya, Peter Zhokov, Joost Huizinga, Jie Tang, Adrien Ecoffet, Brandon Houghton, Raul Sampedro, and Jeff Clune · 2022
Cited alongside, same era.
Imitation learning by estimating expertise of demonstrators
Mark Beliaev, Andy Shih, Stefano Ermon, Dorsa Sadigh, and Ramtin Pedarsani · 2022
Cited alongside, same era.
Ethan Caballero, Kshitij Gupta, Irina Rish, and David Krueger · 2022
Cited alongside, same era.
Reproducible scaling laws for contrastive language-image learning
Mehdi Cherti, Romain Beaumont, Ross Wightman, Mitchell Wortsman, Gabriel Ilharco, Cade Gordon, Christoph Schuhmann, Ludwig Schmidt, and Jenia Jitsev · 2022
Cited alongside, same era.
Unified scaling laws for routed language models
Aidan Clark, Diego de Las Casas, Aurelia Guy, Arthur Mensch, Michela Paganini, Jordan Hoffmann, Bogdan Damoc, Blake A. Hechtman, Trevor Cai, Sebastian Borgeaud, George van den Driessche, Eliza Rutherford, T. W. Hennigan, Matthew G. Johnson, Katie Millican, Albin Cassirer, Chris Jones, Elena Buchatskaya, David Budden, L. Sifre, Simon Osindero, Oriol Vinyals, Jack W. Rae, Erich Elsen, Koray Kavukcuoglu, and Karen Simonyan · 2022
Cited alongside, same era.
Matthew Aitchison, Penny Sweetser, and Marcus Hutter · 2023
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Learning about progress from experts
Jake Bruce, Ankit Anand, Bogdan Mazoure, and Rob Fergus · 2023
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Scaling laws for single-agent reinforcement learning
Jacob Hilton, Jie Tang, and John Schulman · 2023
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Motif: Intrinsic motivation from artificial intelligence feedback
Martin Klissarov, Pierluca D’Oro, Shagun Sodhani, Roberta Raileanu, Pierre-Luc Bacon, Pascal Vincent, Amy Zhang, and Mikael Henaff · 2023
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Accelerating exploration and representation learning with offline pre-training
Bogdan Mazoure, Jake Bruce, Doina Precup, Rob Fergus, and Ankit Anand · 2023
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Scaling data-constrained language models
Niklas Muennighoff, Alexander Rush, Boaz Barak, Teven Le Scao, Nouamane Tazi, Aleksandra Piktus, Sampo Pyysalo, Thomas Wolf, and Colin A Raffel · 2024
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Fine-tuning reinforcement learning models is secretly a forgetting mitigation problem
Maciej Wolczyk, Bartłomiej Cupiał, Mateusz Ostaszewski, Michał Bortkiewicz, Michał Zając, Razvan Pascanu, Łukasz Kuciński, and Piotr Miłoś · 2024
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