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Imitation Learning (IL) methods seek to match the behavior of an agent with that of an expert.
Efficient training of artificial neural networks for autonomous navigation
Dean A Pomerleau · 1991
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Temporal difference learning and td-gammon
Gerald Tesauro · 1995
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Learning agents for uncertain environments
Stuart Russell · 1998
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Algorithms for inverse reinforcement learning
Andrew Y Ng, Stuart J Russell, et al · 2000
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Apprenticeship learning via inverse reinforcement learning
Pieter Abbeel and Andrew Y Ng · 2004
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Metrics for finite markov decision processes
Norm Ferns, Prakash Panangaden, and Doina Precup · 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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Proto-value functions: A laplacian framework for learning representation and control in markov decision processes
Sridhar Mahadevan and Mauro Maggioni · 2007
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Optimal transport: old and new
Cédric Villani · 2008
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Efficient reductions for imitation learning
Stéphane Ross and Drew Bagnell · 2010
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Displacement interpolation using lagrangian mass transport
Nicolas Bonneel, Michiel Van De Panne, Sylvain Paris, and Wolfgang Heidrich · 2011
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Relative entropy inverse reinforcement learning
Abdeslam Boularias, Jens Kober, and Jan Peters · 2011
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Inverse reinforcement learning through structured classification
Edouard Klein, Matthieu Geist, Bilal Piot, and Olivier Pietquin · 2012
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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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A cascaded supervised learning approach to inverse reinforcement learning
Edouard Klein, Bilal Piot, Matthieu Geist, and Olivier Pietquin · 2013
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Learning from demonstrations: Is it worth estimating a reward function?
Bilal Piot, Matthieu Geist, and Olivier Pietquin · 2013
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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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Boosted and reward-regularized classification for apprenticeship learning
Bilal Piot, Matthieu Geist, and Olivier Pietquin · 2014
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Markov decision processes: discrete stochastic dynamic programming
Martin L Puterman · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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Mujoco haptix: A virtual reality system for hand manipulation
V. Kumar and E. Todorov · 2015
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
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Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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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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Fast and accurate deep network learning by exponential linear units (elus)
Djork-Arné Clevert, Thomas Unterthiner, and Sepp Hochreiter · 2016
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Guided cost learning: Deep inverse optimal control via policy optimization
Chelsea Finn, Sergey Levine, and Pieter Abbeel · 2016
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Generative adversarial imitation learning
Jonathan Ho and Stefano Ermon · 2016
Learning complex dexterous manipulation with deep reinforcement learning and demonstrations
Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, Giulia Vezzani, John Schulman, Emanuel Todorov, and Sergey Levine · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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Behavioral cloning from observation
Faraz Torabi, Garrett Warnell, and Peter Stone · 2018
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Maximum entropy inverse reinforcement learning
Brian D Ziebart, Andrew L Maas, J Andrew Bagnell, and Anind K Dey · 2018
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Extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations
Daniel Brown, Wonjoon Goo, Prabhat Nagarajan, and Scott Niekum · 2019
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Temporal cycle-consistency learning
Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, and Andrew Zisserman · 2019
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Cited alongside, same era.
Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2016
Cited alongside, same era.
Bridging the gap between imitation learning and inverse reinforcement learning
Bilal Piot, Matthieu Geist, and Olivier Pietquin · 2016
Cited alongside, same era.
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, et al · 2016
Cited alongside, same era.
Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Cited alongside, same era.
A distributional perspective on reinforcement learning
Marc G Bellemare, Will Dabney, and Rémi Munos · 2017
Cited alongside, same era.
Pot python optimal transport library
Rémi Flamary and Nicolas Courty · 2017
Cited alongside, same era.
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Imitating latent policies from observation
Ashley Edwards, Himanshu Sahni, Yannick Schroecker, and Charles Isbell · 2019
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A divergence minimization perspective on imitation learning methods
Seyed Kamyar Seyed Ghasemipour, Richard Zemel, and Shixiang Gu · 2019
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Learning from a learner
Alexis Jacq, Matthieu Geist, Ana Paiva, and Olivier Pietquin · 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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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
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Risk-sensitive generative adversarial imitation learning
Jonathan Lacotte, Mohammad Ghavamzadeh, Yinlam Chow, and Marco Pavone · 2019
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Computational optimal transport
Gabriel Peyré, Marco Cuturi, et al · 2019
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Random expert distillation: Imitation learning via expert policy support estimation
Ruohan Wang, Carlo Ciliberto, Pierluigi Vito Amadori, and Yiannis Demiris · 2019
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Wasserstein adversarial imitation learning
Huang Xiao, Michael Herman, Joerg Wagner, Sebastian Ziesche, Jalal Etesami, and Thai Hong Linh · 2019
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Learning dexterous in-hand manipulation
OpenAI: Marcin Andrychowicz, Bowen Baker, Maciek Chociej, Rafal Jozefowicz, Bob McGrew, Jakub Pachocki, Arthur Petron, Matthias Plappert, Glenn Powell, Alex Ray, et al · 2020
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Disagreement-regularized imitation learning
Kianté Brantley, Wen Sun, and Mikael Henaff · 2020
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Acme: A research framework for distributed reinforcement learning
Matt Hoffman, Bobak Shahriari, John Aslanides, Gabriel Barth-Maron, Feryal Behbahani, Tamara Norman, Abbas Abdolmaleki, Albin Cassirer, Fan Yang, Kate Baumli, et al · 2020
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State alignment-based imitation learning
Fangchen Liu, Zhan Ling, Tongzhou Mu, and Hao Su · 2020
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Learning to score behaviors for guided policy optimization
Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Krzysztof Choromanski, Anna Choromanska, and Michael Jordan · 2020
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Sqil: imitation learning via regularized behavioral cloning
Siddharth Reddy, Anca D Dragan, and Sergey Levine · 2020
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