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imitation provides open-source implementations of imitation and reward learning algorithms in PyTorch.
Dota 2 with large scale deep reinforcement learning, 2019
Christopher Berner, Greg Brockman, Brooke Chan, Vicki Cheung, Przemysław Dębiak, Christy Dennison, David Farhi, Quirin Fischer, Shariq Hashme, Chris Hesse, Rafal Józefowicz, Scott Gray, Catherine Olsson, Jakub Pachocki, Michael Petrov, Henrique P. d. O. Pinto, Jonathan Raiman, Tim Salimans, Jeremy Schlatter, Jonas Schneider, Szymon Sidor, Ilya Sutskever, Jie Tang, Filip Wolski, and Susan Zhang · 1912
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Modeling interaction via the principle of maximum causal entropy
Brian D. Ziebart, J. Andrew Bagnell, and Anind K. Dey · 2010
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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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Generative adversarial imitation learning
Jonathan Ho and Stefano Ermon · 2016
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Generative adversarial imitation learning
Jonathan Ho and Christopher Hesse · 2016
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Deep reinforcement learning from human preferences
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei · 2017
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The Sacred infrastructure for computational research
Klaus Greff, Aaron Klein, Martin Chovanec, Frank Hutter, and Jürgen Schmidhuber · 2017
Cited alongside, same era.
Inverse RL
Justin Fu · 2018
Cited alongside, same era.
Learning robust rewards with adverserial inverse reinforcement learning
Justin Fu, Katie Luo, and Sergey Levine · 2018
Cited alongside, same era.
Discriminator-actor-critic: Addressing sample inefficiency and reward bias in adversarial imitation learning
Ilya Kostrikov, Kumar Krishna Agrawal, Debidatta Dwibedi, Sergey Levine, and Jonathan Tompson · 2019
Cited alongside, same era.
Grandmaster level in StarCraft II using multi-agent reinforcement learning
Oriol Vinyals, Igor Babuschkin, Wojciech M Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H Choi, Richard Powell, Timo Ewalds, Petko Georgiev, et al · 2019
Cited alongside, same era.
Implementation matters in deep RL: A case study on PPO and TRPO
Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, and Aleksander Madry · 2020
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seals: Suite of environments for algorithms that learn specifications
Adam Gleave, Pedro Freire, Steven Wang, and Sam Toyer · 2020
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APReL: A library for active preference-based reward learning algorithms, 2021
Erdem Bıyık, Aditi Talati, and Dorsa Sadigh · 2021
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What matters for adversarial imitation learning?, 2021
Manu Orsini, Anton Raichuk, Léonard Hussenot, Damien Vincent, Robert Dadashi, Sertan Girgin, Matthieu Geist, Olivier Bachem, Olivier Pietquin, and Marcin Andrychowicz · 2021
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Imitation learning
Antonin Raffin, Ashley Hill, Maximilian Ernestus, Adam Gleave, Anssi Kanervisto, and Noah Dormann · 2021
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