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Imitation learning (IL) aims to mimic the behavior of an expert policy in a sequential decision-making problem given only demonstrations.
Statistical methods in markov chains
Patrick Billingsley · 1961
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Rates of convergence of minimum distance estimators and kolmogorov’s entropy
Yannis G. Yatracos · 1985
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The “automatic” robustness of minimum distance functionals
David L. Donoho and Richard C. Liu · 1988
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Concentration inequalities for the missing mass and for histogram rule error
David Mcallester, Luis Ortiz, Ralf Herbrich, and Thore Graepel · 2003
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Apprenticeship learning via inverse reinforcement learning
Pieter Abbeel and Andrew Y. Ng · 2004
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Error limiting reductions between classification tasks
Alina Beygelzimer, Varsha Dani, Thomas P. Hayes, John Langford, and Bianca Zadrozny · 2005
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Maximum margin planning
Nathan D Ratliff, J Andrew Bagnell, and Martin A Zinkevich · 2006
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An application of reinforcement learning to aerobatic helicopter flight
Pieter Abbeel, Adam Coates, Morgan Quigley, and Andrew Y. Ng · 2007
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A survey of robot learning from demonstration
Brenna D. Argall, Sonia Chernova, Manuela Veloso, and Brett Browning · 2008
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Apprenticeship learning using linear programming
Umar Syed, Michael Bowling, and Robert E Schapire · 2008
Earlier work this paper cites.
Maximum entropy inverse reinforcement learning
Brian D Ziebart, Andrew L Maas, J Andrew Bagnell, and Anind K Dey · 2008
Earlier work this paper cites.
Efficient reductions for imitation learning
Stephane Ross and Drew Bagnell · 2010
Cited alongside, same era.
A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey J. Gordon, and Drew Bagnell · 2011
Cited alongside, same era.
Concentration inequalities - a nonasymptotic theory of independence
Stéphane Boucheron, Gábor Lugosi, and Pascal Massart · 2013
Cited alongside, same era.
Reinforcement and imitation learning via interactive no-regret learning
Stéphane Ross and J. Andrew Bagnell · 2014
Cited alongside, same era.
Guided cost learning: Deep inverse optimal control via policy optimization
Chelsea Finn, Sergey Levine, and Pieter Abbeel · 2016
Cited alongside, same era.
Generative adversarial imitation learning
Reward learning from human preferences and demonstrations in atari
Borja Ibarz, Jan Leike, Tobias Pohlen, Geoffrey Irving, Shane Legg, and Dario Amodei · 2018
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Behavioral cloning from observation
Faraz Torabi, Garrett Warnell, and Peter Stone · 2018
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Reinforcement and imitation learning for diverse visuomotor skills, 2018
Yuke Zhu, Ziyu Wang, Josh Merel, Andrei Rusu, Tom Erez, Serkan Cabi, Saran Tunyasuvunakool, János Kramár, Raia Hadsell, Nando de Freitas, and Nicolas Heess · 2018
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Nature , 575(7782):350–354, 2019
Grandmaster level in StarCraft II using multi-agent reinforcement learning · 2019
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Imitation learning as f f -divergence minimization
Liyiming Ke, Matt Barnes, Wen Sun, Gilwoo Lee, Sanjiban Choudhury, and Siddhartha Srinivasa · 2019
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Jonathan Ho and Stefano Ermon · 2016
Cited alongside, same era.
Learning human behaviors from motion capture by adversarial imitation
Josh Merel, Yuval Tassa, TB Dhruva, Sriram Srinivasan, Jay Lemmon, Ziyu Wang, Greg Wayne, and Nicolas Manfred Otto Heess · 2017
Cited alongside, same era.
Combining self-supervised learning and imitation for vision-based rope manipulation
Ashvin Nair, Dian Chen, Pulkit Agrawal, Phillip Isola, Pieter Abbeel, Jitendra Malik, and Sergey Levine · 2017
Cited alongside, same era.
Agile autonomous driving using end-to-end deep imitation learning
Yunpeng Pan, Ching-An Cheng, Kamil Saigol, Keuntaek Lee, Xinyan Yan, Evangelos Theodorou, and Byron Boots · 2017
Cited alongside, same era.
Deeply aggrevated: Differentiable imitation learning for sequential prediction
Wen Sun, Arun Venkatraman, Geoffrey J Gordon, Byron Boots, and J Andrew Bagnell · 2017
Cited alongside, same era.
Deep q-learning from demonstrations
Todd Hester, Matej Vecerík, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Dan Horgan, John Quan, Andrew Sendonaris, Ian Osband, Gabriel Dulac-Arnold, John Agapiou, Joel Z. Leibo, and Audrunas Gruslys · 2018
Cited alongside, same era.
Provably efficient imitation learning from observation alone
Wen Sun, Anirudh Vemula, Byron Boots, and Drew Bagnell · 2019
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Provable representation learning for imitation learning via bi-level optimization
Sanjeev Arora, Simon S Du, Sham Kakade, Yuping Luo, and Nikunj Saunshi · 2020
Closest in time.
Disagreement-regularized imitation learning
Kiante Brantley, Wen Sun, and Mikael Henaff · 2020
Closest in time.
Learning self-correctable policies and value functions from demonstrations with negative sampling
Yuping Luo, Huazhe Xu, and Tengyu Ma · 2020
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Imitation learning for agile autonomous driving
Yunpeng Pan, Ching-An Cheng, Kamil Saigol, Keuntaek Lee, Xinyan Yan, Evangelos A Theodorou, and Byron Boots · 2020
Closest in time.