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A desirable property of autonomous agents is the ability to both solve long-horizon problems and generalize to unseen tasks.
Advantage-weighted regression: Simple and scalable off-policy reinforcement learning
Xue Bin Peng, Aviral Kumar, Grace Zhang, and Sergey Levine · 1910
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Behavior regularized offline reinforcement learning
Yifan Wu, George Tucker, and Ofir Nachum · 1911
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ALVINN: an autonomous land vehicle in a neural network
Dean Pomerleau · 1988
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Algorithms for inverse reinforcement learning
Andrew Y. Ng and Stuart J. Russell · 2000
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Pattern recognition and machine learning
Christopher M Bishop · 2006
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Survey: Robot programming by demonstration
Aude Billard, Sylvain Calinon, Ruediger Dillmann, and Stefan Schaal · 2008
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A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey J. Gordon, and Drew Bagnell · 2011
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A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey J. Gordon, and Drew Bagnell · 2011
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Robot skill learning: From reinforcement learning to evolution strategies
Freek Stulp and Olivier Sigaud · 2013
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Robots that can adapt like animals
Antoine Cully, Jeff Clune, Danesh Tarapore, and Jean-Baptiste Mouret · 2015
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Siamese neural networks for one-shot image recognition
Gregory Koch, Richard Zemel, and Ruslan Salakhutdinov · 2015
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Rl 2 : Fast reinforcement learning via slow reinforcement learning
Yan Duan, John Schulman, Xi Chen, Peter L. Bartlett, Ilya Sutskever, and Pieter Abbeel · 2016
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beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2016
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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Tim Lillicrap, Koray Kavukcuoglu, and Daan Wierstra · 2016
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One-shot imitation learning
Yan Duan, Marcin Andrychowicz, Bradly C. Stadie, Jonathan Ho, Jonas Schneider, Ilya Sutskever, Pieter Abbeel, and Wojciech Zaremba · 2017
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One-shot visual imitation learning via meta-learning
Chelsea Finn, Tianhe Yu, Tianhao Zhang, Pieter Abbeel, and Sergey Levine · 2017
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An algorithmic perspective on imitation learning
Takayuki Osa, Joni Pajarinen, Gerhard Neumann, J Andrew Bagnell, Pieter Abbeel, and Jan Peters · 2018
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Bayesian optimization with automatic prior selection for data-efficient direct policy search
Rémi Pautrat, Konstantinos Chatzilygeroudis, and Jean-Baptiste Mouret · 2018
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Sparse graphical memory for robust planning
Scott Emmons, Ajay Jain, Michael Laskin, Thanard Kurutach, Pieter Abbeel, and Deepak Pathak · 2020
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D4rl: Datasets for deep data-driven reinforcement learning
Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Hallucinative topological memory for zero-shot visual planning
Kara Liu, Thanard Kurutach, Christine Tung, Pieter Abbeel, and Aviv Tamar · 2020
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Nikolay Savinov, Alexey Dosovitskiy, and Vladlen Koltun · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Deep imitation learning for complex manipulation tasks from virtual reality teleoperation
Tianhao Zhang, Zoe McCarthy, Owen Jow, Dennis Lee, Xi Chen, Ken Goldberg, and Pieter Abbeel · 2018
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A survey on policy search algorithms for learning robot controllers in a handful of trials
Konstantinos Chatzilygeroudis, Vassilis Vassiliades, Freek Stulp, Sylvain Calinon, and Jean-Baptiste Mouret · 2019
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Relay policy learning: Solving long-horizon tasks via imitation and reinforcement learning
Abhishek Gupta, Vikash Kumar, Corey Lynch, Sergey Levine, and Karol Hausman · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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Neural dynamic policies for end-to-end sensorimotor learning
Shikhar Bahl, Mustafa Mukadam, Abhinav Gupta, and Deepak Pathak · 2020
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Awac: Accelerating online reinforcement learning with offline datasets, 2020
Ashvin Nair, Abhishek Gupta, Murtaza Dalal, and Sergey Levine · 2020
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Accelerating reinforcement learning with learned skill priors
Karl Pertsch, Youngwoon Lee, and Joseph J. Lim · 2020
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Ving: Learning open-world navigation with visual goals
Dhruv Shah, Benjamin Eysenbach, Gregory Kahn, Nicholas Rhinehart, and Sergey Levine · 2020
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Parrot: Data-driven behavioral priors for reinforcement learning
Avi Singh, Huihan Liu, Gaoyue Zhou, Albert Yu, Nicholas Rhinehart, and Sergey Levine · 2020
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Generalizing from a few examples: A survey on few-shot learning
Yaqing Wang, Quanming Yao, James T. Kwok, and Lionel M. Ni · 2020
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{OPAL}: Offline primitive discovery for accelerating offline reinforcement learning
Anurag Ajay, Aviral Kumar, Pulkit Agrawal, Sergey Levine, and Ofir Nachum · 2021
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Learning transferable visual models from natural language supervision, 2021
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever · 2021
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