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This paper addresses the problem of inverse reinforcement learning (IRL) -- inferring the reward function of an agent from observing its behavior.
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Donghun Lee, Srivatsan Srinivasan, and Finale Doshi-Velez · 1903
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ML Puterman · 1994
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Michael Bain and Claude Sammut · 1996
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Andrew Y. Ng and Stuart Russell · 2000
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Maximum entropy inverse reinforcement learning
Brian D. Ziebart, Andrew Maas, J. Andrew Bagnell, and Anind K. Dey · 2008
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Feature construction for inverse reinforcement learning
Sergey Levine, Zoran Popovic, and Vladlen Koltun · 2010
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Stéphane Ross, Geoffrey J. Gordon, and J. Andrew Bagnell · 2010
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Nonlinear inverse reinforcement learning with gaussian processes
Sergey Levine, Zoran Popovic, and Vladlen Koltun · 2011
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Model-based adversarial imitation learning, 2016
Nir Baram, Oron Anschel, and Shie Mannor · 2016
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Distance minimization for reward learning from scored trajectories
Benjamin Burchfiel, Carlo Tomasi, and Ronald Parr · 2016
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Cooperative inverse reinforcement learning, 2016
Dylan Hadfield-Menell, Anca Dragan, Pieter Abbeel, and Stuart Russell · 2016
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Generative adversarial imitation learning
Jonathan Ho and Stefano Ermon · 2016
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Socially adaptive path planning in human environments using inverse reinforcement learning
Beomjoon Kim and Joelle Pineau · 2016
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Inverse reinforcement learning from failure
Kyriacos Shiarlis, Joao Messias, and Shimon Whiteson · 2016
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Maximum entropy deep inverse reinforcement learning, 2016
Markus Wulfmeier, Peter Ondruska, and Ingmar Posner · 2016
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Observational learning by reinforcement learning, 2017
Diana Borsa, Bilal Piot, Rémi Munos, and Olivier Pietquin · 2017
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Reinforcement learning with deep energy-based policies
Tuomas Haarnoja, Haoran Tang, Pieter Abbeel, and Sergey Levine · 2017
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Inverse reinforcement learning via deep gaussian process, 2017
Ming Jin, Andreas Damianou, Pieter Abbeel, and Costas Spanos · 2017
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Advantages and limitations of using successor features for transfer in reinforcement learning, 2017
Lucas Lehnert, Stefanie Tellex, and Michael L. Littman · 2017
Time-contrastive networks: Self-supervised learning from video, 2018
Pierre Sermanet, Corey Lynch, Yevgen Chebotar, Jasmine Hsu, Eric Jang, Stefan Schaal, and Sergey Levine · 2018
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Behavioral cloning from observation, 2018
Faraz Torabi, Garrett Warnell, and Peter Stone · 2018
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Extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations, 2019
Daniel S. Brown, Wonjoon Goo, Prabhat Nagarajan, and Scott Niekum · 2019
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Successor uncertainties: Exploration and uncertainty in temporal difference learning
David Janz, Jiri Hron, Przemysł aw Mazur, Katja Hofmann, José Miguel Hernández-Lobato, and Sebastian Tschiatschek · 2019
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Theory of mind as inverse reinforcement learning
Julian Jara-Ettinger · 2019
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Learning a prior over intent via meta-inverse reinforcement learning, 2019
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Visual semantic planning using deep successor representations, 2017
Yuke Zhu, Daniel Gordon, Eric Kolve, Dieter Fox, Li Fei-Fei, Abhinav Gupta, Roozbeh Mottaghi, and Ali Farhadi · 2017
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Successor features for transfer in reinforcement learning, 2018
André Barreto, Will Dabney, Rémi Munos, Jonathan J. Hunt, Tom Schaul, Hado van Hasselt, and David Silver · 2018
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Universal successor features approximators
Diana Borsa, André Barreto, John Quan, Daniel J. Mankowitz, Rémi Munos, Hado van Hasselt, David Silver, and Tom Schaul · 2018
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Minimalistic gridworld environment for openai gym
Maxime Chevalier-Boisvert, Lucas Willems, and Suman Pal · 2018
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Learning how pedestrians navigate: A deep inverse reinforcement learning approach
Muhammad Fahad, Zhuo Chen, and Yi Guo · 2018
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Learning robust rewards with adverserial inverse reinforcement learning
Justin Fu, Katie Luo, and Sergey Levine · 2018
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Kelvin Xu, Ellis Ratner, Anca Dragan, Sergey Levine, and Chelsea Finn · 2019
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Meta-inverse reinforcement learning with probabilistic context variables, 2019
Lantao Yu, Tianhe Yu, Chelsea Finn, and Stefano Ermon · 2019
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Fast reinforcement learning with generalized policy updates
André Barreto, Shaobo Hou, Diana Borsa, David Silver, and Doina Precup · 2020
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Fast task inference with variational intrinsic successor features, 2020
Steven Hansen, Will Dabney, Andre Barreto, Tom Van de Wiele, David Warde-Farley, and Volodymyr Mnih · 2020
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Strictly batch imitation learning by energy-based distribution matching
Daniel Jarrett, Ioana Bica, and Mihaela van der Schaar · 2020
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Deep inverse q-learning with constraints, 2020
Gabriel Kalweit, Maria Huegle, Moritz Werling, and Joschka Boedecker · 2020
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Imitation learning via off-policy distribution matching
Ilya Kostrikov, Ofir Nachum, and Jonathan Tompson · 2020
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Count-based exploration with the successor representation
Marlos C. Machado, Marc G. Bellemare, and Michael Bowling · 2020
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Scalable bayesian inverse reinforcement learning, 2021
Alex J. Chan and Mihaela van der Schaar · 2021
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Psiphi-learning: Reinforcement learning with demonstrations using successor features and inverse temporal difference learning, 2021
Angelos Filos, Clare Lyle, Yarin Gal, Sergey Levine, Natasha Jaques, and Gregory Farquhar · 2021
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IQ-learn: Inverse soft-q learning for imitation
Divyansh Garg, Shuvam Chakraborty, Chris Cundy, Jiaming Song, and Stefano Ermon · 2021
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Driving behavior modeling using naturalistic human driving data with inverse reinforcement learning, 2021
Zhiyu Huang, Jingda Wu, and Chen Lv · 2021
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Modeling human intention inference in continuous 3d domains by inverse planning and body kinematics, 2021
Yingdong Qian, Marta Kryven, Tao Gao, Hanbyul Joo, and Josh Tenenbaum · 2021
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