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Model-free deep reinforcement learning (RL) has demonstrated its superiority on many complex sequential decision-making problems.
Is imitation learning the route to humanoid robots?
Stefan Schaal · 1999
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Apprenticeship learning via inverse reinforcement learning
Pieter Abbeel and Andrew Y Ng · 2004
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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 game-theoretic approach to apprenticeship learning
Umar Syed and Robert E Schapire · 2008
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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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A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey Gordon, and Drew Bagnell · 2011
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Learning from limited demonstrations
Beomjoon Kim, Amir-massoud Farahmand, Joelle Pineau, and Doina Precup · 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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Deterministic policy gradient algorithms
David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, and Martin Riedmiller · 2014
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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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Generative adversarial imitation learning
Jonathan Ho and Stefano Ermon · 2016
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f-gan: Training generative neural samplers using variational divergence minimization
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka · 2016
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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
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Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 2017
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Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates
Shixiang Gu, Ethan Holly, Timothy Lillicrap, and Sergey Levine · 2017
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Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards
Truncated horizon policy search: Combining reinforcement learning & imitation learning
Wen Sun, J Andrew Bagnell, and Byron Boots · 2018
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Addressing function approximation error in actor-critic methods
Scott Fujimoto, Herke Van Hoof, and David Meger · 2018
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Policy optimization with demonstrations
Bingyi Kang, Zequn Jie, and Jiashi Feng · 2018
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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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Stabilizing off-policy q-learning via bootstrapping error reduction
Aviral Kumar, Justin Fu, Matthew Soh, George Tucker, and Sergey Levine · 2019
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Matej Večerík, Todd Hester, Jonathan Scholz, Fumin Wang, Olivier Pietquin, Bilal Piot, Nicolas Heess, Thomas Rothörl, Thomas Lampe, and Martin Riedmiller · 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.
Learning robust rewards with adversarial inverse reinforcement learning
Justin Fu, Katie Luo, and Sergey Levine · 2017
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 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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Overcoming exploration in reinforcement learning with demonstrations
Ashvin Nair, Bob McGrew, Marcin Andrychowicz, Wojciech Zaremba, and Pieter Abbeel · 2018
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Disagreement-regularized imitation learning
Kianté Brantley, Wen Sun, and Mikael Henaff
Cited in the paper.
Yueh-Hua Wu, Nontawat Charoenphakdee, Han Bao, Voot Tangkaratt, and Masashi Sugiyama · 2019
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Sample efficient imitation learning for continuous control
Fumihiro Sasaki, Tetsuya Yohira, and Atsuo Kawaguchi · 2019
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Exploration by random network distillation
Yuri Burda, Harrison Edwards, Amos Storkey, and Oleg Klimov · 2019
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Random expert distillation: Imitation learning via expert policy support estimation
Ruohan Wang, Carlo Ciliberto, Pierluigi Amadori, and Yiannis Demiris · 2019
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Imitation learning via off-policy distribution matching
Ilya Kostrikov, Ofir Nachum, and Jonathan Tompson · 2020
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