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We study the problem of inverse reinforcement learning (IRL) with the added twist that the learner is assisted by a helpful teacher.
On the complexity of teaching
Sally A Goldman and Michael J Kearns · 1995
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Learning from demonstration
Stefan Schaal · 1997
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Learning agents for uncertain environments
Stuart Russell · 1998
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A framework for hehavioural claning
Michael Bain and Claude Sommut · 1999
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Policy invariance under reward transformations: Theory and application to reward shaping
Andrew Y Ng, Daishi Harada, and Stuart Russell · 1999
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Algorithms for inverse reinforcement learning
Andrew Y Ng and Stuart J Russell · 2000
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Apprenticeship learning via inverse reinforcement learning
Pieter Abbeel and Andrew Y Ng · 2004
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Maximum margin planning
Nathan D Ratliff, J Andrew Bagnell, and Martin A Zinkevich · 2006
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Robot programming by demonstration
Aude Billard, Sylvain Calinon, Ruediger Dillmann, and Stefan Schaal · 2008
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Learning to solve problems from exercises
Prasad Tadepalli · 2008
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Maximum entropy inverse reinforcement learning
Brian D Ziebart, Andrew L Maas, J Andrew Bagnell, and Anind K Dey · 2008
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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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Policy teaching through reward function learning
Haoqi Zhang, David C Parkes, and Yiling Chen · 2009
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Feature construction for inverse reinforcement learning
Sergey Levine, Zoran Popovic, and Vladlen Koltun · 2010
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Reward design via online gradient ascent
Jonathan Sorg, Satinder P. Singh, and Richard L. Lewis · 2010
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Modeling purposeful adaptive behavior with the principle of maximum causal entropy
Brian D Ziebart · 2010
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Relative entropy inverse reinforcement learning
Abdeslam Boularias, Jens Kober, and Jan Peters · 2011
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Children’s imitation of causal action sequences is influenced by statistical and pedagogical evidence
Daphna Buchsbaum, Alison Gopnik, Thomas L Griffiths, and Patrick Shafto · 2011
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Algorithmic and human teaching of sequential decision tasks
Maya Cakmak and Manuel Lopes · 2012
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Dynamic teaching in sequential decision making environments
Thomas J. Walsh and Sergiu Goschin · 2012
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On actively teaching the crowd to classify
Adish Singla, Ilija Bogunovic, G Bartók, A Karbasi, and A Krause · 2013
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Teaching on a budget: Agents advising agents in reinforcement learning
Lisa Torrey and Matthew Taylor · 2013
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Eliciting good teaching from humans for machine learners
Maya Cakmak and Andrea L Thomaz · 2014
Faster teaching via pomdp planning
Anna N Rafferty, Emma Brunskill, Thomas L Griffiths, and Patrick Shafto · 2016
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Repeated inverse reinforcement learning
Kareem Amin, Nan Jiang, and Satinder P. Singh · 2017
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Active preference-based learning of reward functions
Anca D Dragan Dorsa Sadigh, Shankar Sastry, and Sanjit A Seshia · 2017
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Iterative machine teaching
Weiyang Liu, Bo Dai, Ahmad Humayun, Charlene Tay, Chen Yu, Linda B. Smith, James M. Rehg, and Le Song · 2017
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First-person activity forecasting with online inverse reinforcement learning
Nicholas Rhinehart and Kris M Kitani · 2017
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Understanding the role of adaptivity in machine teaching: The case of version space learners
Yuxin Chen, Adish Singla, Oisin Mac Aodha, Pietro Perona, and Yisong Yue · 2018
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Cited alongside, same era.
Robot learning from human teachers
Sonia Chernova and Andrea L Thomaz · 2014
Cited alongside, same era.
Optimal teaching for limited-capacity human learners
Kaustubh R Patil, Xiaojin Zhu, Łukasz Kopeć, and Bradley C Love · 2014
Cited alongside, same era.
A rational account of pedagogical reasoning: Teaching by, and learning from, examples
Patrick Shafto, Noah D Goodman, and Thomas L Griffiths · 2014
Cited alongside, same era.
Near-optimally teaching the crowd to classify
Adish Singla, Ilija Bogunovic, Gábor Bartók, Amin Karbasi, and Andreas Krause · 2014
Cited alongside, same era.
Maximum entropy deep inverse reinforcement learning
Markus Wulfmeier, Peter Ondruska, and Ingmar Posner · 2015
Cited alongside, same era.
Interactive teaching strategies for agent training
Ofra Amir, Ece Kamar, Andrey Kolobov, and Barbara J. Grosz · 2016
Cited alongside, same era.
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Teaching Inverse Reinforcement Learners via Features and Demonstrations
Luis Haug, Sebastian Tschiatschek, and Adish Singla · 2018
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Teaching multiple concepts to a forgetful learner
Anette Hunziker, Yuxin Chen, Oisin Mac Aodha, Manuel Gomez-Rodriguez, Andreas Krause, Pietro Perona, Yisong Yue, and Adish Singla · 2018
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Towards black-box iterative machine teaching
Weiyang Liu, Bo Dai, Xingguo Li, Zhen Liu, James Rehg, and Le Song · 2018
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Interactive optimal teaching with unknown learners
Francisco S Melo, Carla Guerra, and Manuel Lopes · 2018
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An algorithmic perspective on imitation learning
Takayuki Osa, Joni Pajarinen, Gerhard Neumann, J Andrew Bagnell, Pieter Abbeel, Jan Peters, et al · 2018
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Wen Sun, Geoffrey J Gordon, Byron Boots, and J Andrew Bagnell · 2018
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Infinite time horizon maximum causal entropy inverse reinforcement learning
Zhengyuan Zhou, Michael Bloem, and Nicholas Bambos · 2018
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An overview of machine teaching
Xiaojin Zhu, Adish Singla, Sandra Zilles, and Anna N. Rafferty · 2018
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Machine teaching for inverse reinforcement learning: Algorithms and applications
Daniel S. Brown and Scott Niekum · 2019
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Iterative classroom teaching
Teresa Yeo, Parameswaran Kamalaruban, Adish Singla, Arpit Merchant, Thibault Asselborn, Louis Faucon, Pierre Dillenbourg, and Volkan Cevher · 2019
Closest in time.