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Integration of reinforcement learning and imitation learning is an important problem that has been studied for a long time in the field of intelligent robotics.
Programming robots using reinforcement learning and teaching
Long Ji Lin · 1991
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Efficient training of artificial neural networks for autonomous navigation
Dean A Pomerleau · 1991
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Self-improving reactive agents based on reinforcement learning, planning and teaching
Long-Ji Lin · 1992
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A framework for behavioural claning
Michael Bain and Claude Sommut · 1999
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Planning by probabilistic inference
Hagai Attias · 2003
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Learning from demonstration and adaptation of biped locomotion
Jun Nakanishi, Jun Morimoto, Gen Endo, Gordon Cheng, Stefan Schaal, and Mitsuo Kawato · 2004
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Linearly-solvable markov decision problems
Emanuel Todorov · 2007
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Effective integration of imitation learning and reinforcement learning by generating internal reward
Keita Hamahata, Tadahiro Taniguchi, Kazutoshi Sakakibara, Ikuko Nishikawa, Kazuma Tabuchi, and Tetsuo Sawaragi · 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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Robot trajectory optimization using approximate inference
Marc Toussaint · 2009
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Learning locomotion over rough terrain using terrain templates
Mrinal Kalakrishnan, Jonas Buchli, Peter Pastor, and Stefan Schaal · 2009
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Modeling interaction via the principle of maximum causal entropy
Brian D Ziebart, J Andrew Bagnell, and Anind K Dey · 2010
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Optimal control as a graphical model inference problem
Hilbert J Kappen, Vicenç Gómez, and Manfred Opper · 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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Guided policy search
Sergey Levine and Vladlen Koltun · 2013
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On stochastic optimal control and reinforcement learning by approximate inference
Konrad Rawlik, Marc Toussaint, and Sethu Vijayakumar · 2013
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Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 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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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Reinforcement learning from demonstration through shaping
Tim Brys, Anna Harutyunyan, Halit Bener Suay, Sonia Chernova, Matthew E Taylor, and Ann Nowé · 2015
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Human-level control through deep reinforcement learning
Deeply aggrevated: Differentiable imitation learning for sequential prediction
Wen Sun, Arun Venkatraman, Geoffrey J Gordon, Byron Boots, and J Andrew Bagnell · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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End-to-end differentiable adversarial imitation learning
Nir Baram, Oron Anschel, Itai Caspi, and Shie Mannor · 2017
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Infogail: Interpretable imitation learning from visual demonstrations
Yunzhu Li, Jiaming Song, and Stefano Ermon · 2017
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Multi-modal imitation learning from unstructured demonstrations using generative adversarial nets
Karol Hausman, Yevgen Chebotar, Stefan Schaal, Gaurav Sukhatme, and Joseph J Lim · 2017
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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
Cited alongside, same era.
Maximum entropy deep inverse reinforcement learning
Markus Wulfmeier, Peter Ondruska, and Ingmar Posner · 2015
Cited alongside, same era.
Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
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.
Generative adversarial imitation learning
Jonathan Ho and Stefano Ermon · 2016
Cited alongside, same era.
End to end learning for self-driving cars
Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, et al · 2016
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.
Yuke Zhu, Ziyu Wang, Josh Merel, Andrei Rusu, Tom Erez, Serkan Cabi, Saran Tunyasuvunakool, János Kramár, Raia Hadsell, Nando de Freitas, et al · 2018
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Hierarchical imitation and reinforcement learning
Nan Jiang Alekh Agarwal Miroslav DudÃk Yisong Yue Le, Hoang Minh and Hal Daumé · 2018
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Overcoming exploration in reinforcement learning with demonstrations
Ashvin Nair, Bob McGrew, Marcin Andrychowicz, Wojciech Zaremba, and Pieter Abbeel · 2018
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Deep q-learning from demonstrations
Todd Hester, Matej Vecerik, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Dan Horgan, John Quan, Andrew Sendonaris, Ian Osband, et al · 2018
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Deep imitative models for flexible inference, planning, and control
Nicholas Rhinehart, Rowan McAllister, and Sergey Levine · 2018
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Reinforcement learning and control as probabilistic inference: Tutorial and review
Sergey Levine · 2018
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Maximum a posteriori policy optimisation
Abbas Abdolmaleki, Jost Tobias Springenberg, Yuval Tassa, Remi Munos, Nicolas Heess, and Martin Riedmiller · 2018
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Variational inverse control with events: A general framework for data-driven reward definition
Justin Fu, Avi Singh, Dibya Ghosh, Larry Yang, and Sergey Levine · 2018
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Learning robust rewards with adversarial inverse reinforcement learning
Justin Fu, Katie Luo, and Sergey Levine · 2018
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Acgail: Imitation learning about multiple intentions with auxiliary classifier gans
Jiahao Lin and Zongzhang Zhang · 2018
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