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Classically, imitation learning algorithms have been developed for idealized situations, e.g., the demonstrations are often required to be collected in the exact same environment and usually include the demonstrator's actions.
A framework for behavioral cloning
Michael Bain and Claude Sammut · 1995
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Learning from demonstration
Stefan Schaal · 1997
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 1998
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A framework for behavioural claning
Michael Bain and Claude Sommut · 1999
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Apprenticeship learning via inverse reinforcement learning
Pieter Abbeel and Andrew Y Ng · 2004
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Boosting structured prediction for imitation learning
JA Bagnell, Joel Chestnutt, David M Bradley, and Nathan D Ratliff · 2007
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A survey of robot learning from demonstration
Brenna D Argall, Sonia Chernova, Manuela Veloso, and Brett Browning · 2009
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Action understanding as inverse planning
Chris L Baker, Rebecca Saxe, and Joshua B Tenenbaum · 2009
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A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey J Gordon, and Drew Bagnell · 2011
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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
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OpenAI Gym, 2016
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Learning transferable policies for monocular reactive mav control
Shreyansh Daftry, J Andrew Bagnell, and Martial Hebert · 2016
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Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
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Generative adversarial imitation learning
Jonathan Ho and Stefano Ermon · 2016
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Shuffle and learn: unsupervised learning using temporal order verification
Ishan Misra, C Lawrence Zitnick, and Martial Hebert · 2016
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Online goal recognition through mirroring: Humans and agents
Mor Vered, Gal A Kaminka, and Sivan Biham · 2016
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Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Pybullet, a python module for physics simulation for games, robotics and machine learning
Erwin Coumans and Yunfei Bai · 2017
Time-contrastive networks: Self-supervised learning from multi-view observation
Pierre Sermanet, Corey Lynch, Jasmine Hsu, and Sergey Levine · 2018
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Behavioral cloning from observation
Faraz Torabi, Garrett Warnell, and Peter Stone · 2018
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Towards online goal recognition combining goal mirroring and landmarks
Mor Vered, Ramon Fraga Pereira, Maurício C Magnaguagno, Gal A Kaminka, and Felipe Meneguzzi · 2018
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Imitating latent policies from observation
Ashley D Edwards, Himanshu Sahni, Yannick Schroeker, and Charles L Isbell · 2019
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One-shot learning of multi-step tasks from observation via activity localization in auxiliary video
Wonjoon Goo and Scott Niekum · 2019
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Cited alongside, same era.
Learning invariant feature spaces to transfer skills with reinforcement learning
Abhishek Gupta, Coline Devin, YuXuan Liu, Pieter Abbeel, and Sergey Levine · 2017
Cited alongside, same era.
Learning human behaviors from motion capture by adversarial imitation
Josh Merel, Yuval Tassa, Sriram Srinivasan, Jay Lemmon, Ziyu Wang, Greg Wayne, and Nicolas Heess · 2017
Cited alongside, same era.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Cited alongside, same era.
Third-person imitation learning
Bradly C Stadie, Pieter Abbeel, and Ilya Sutskever · 2017
Cited alongside, same era.
Playing hard exploration games by watching youtube
Yusuf Aytar, Tobias Pfaff, David Budden, Thomas Paine, Ziyu Wang, and Nando de Freitas · 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.
Ilya Kostrikov, Kumar Krishna Agrawal, Debidatta Dwibedi, Sergey Levine, and Jonathan Tompson · 2019
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Ridm: Reinforced inverse dynamics modeling for learning from a single observed demonstration
Brahma Pavse, Faraz Torabi, Josiah Hanna, Garrett Warnell, and Peter Stone · 2019
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Adversarial imitation via variational inverse reinforcement learning
Ahmed H. Qureshi, Byron Boots, and Michael C. Yip · 2019
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Sample efficient imitation learning for continuous control
Fumihiro Sasaki, Tetsuya Yohira, and Atsuo Kawaguchi · 2019
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Adversarial imitation learning from state-only demonstrations
Faraz Torabi, Garrett Warnell, and Peter Stone · 2019
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Generative adversarial imitation from observation
Faraz Torabi, Garrett Warnell, and Peter Stone · 2019
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Imitation learning from video by leveraging proprioception
Faraz Torabi, Garrett Warnell, and Peter Stone · 2019
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Recent advances in imitation learning from observation
Faraz Torabi, Garrett Warnell, and Peter Stone · 2019
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