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The successful application of general reinforcement learning algorithms to real-world robotics applications is often limited by their high data requirements.
Anna Harutyunyan, Will Dabney, Diana Borsa, Nicolas Heess, Rémi Munos, and Doina Precup · 1902
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Learning to predict by the methods of temporal differences
Richard S Sutton · 1988
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The transfer of cognitive skill
Mark K Singley and John Robert Anderson · 1989
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Feudal reinforcement learning
Peter Dayan and Geoffrey E Hinton · 1993
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Mixture density networks
Christopher M Bishop · 1994
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Multitask learning
Rich Caruana · 1997
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Catastrophic forgetting in connectionist networks
Robert M French · 1999
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Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning
Richard S Sutton, Doina Precup, and Satinder Singh · 1999
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Temporal abstraction in reinforcement learning
Doina Precup · 2000
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To transfer or not to transfer
Michael T Rosenstein, Zvika Marx, Leslie Pack Kaelbling, and Thomas G Dietterich · 2005
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Transfer learning for reinforcement learning domains: A survey
Matthew E Taylor and Peter Stone · 2009
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A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 2010
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Transfer learning
Lisa Torrey and Jude Shavlik · 2010
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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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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
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Learning continuous control policies by stochastic value gradients
Nicolas Heess, Gregory Wayne, David Silver, Tim Lillicrap, Tom Erez, and Yuval Tassa · 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, Philipp Moritz, Michael Jordan, and Pieter Abbeel · 2015
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Hierarchical relative entropy policy search
Christian Daniel, Gerhard Neumann, Oliver Kroemer, and Jan Peters · 2016
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Learning and transfer of modulated locomotor controllers
Nicolas Heess, Greg Wayne, Yuval Tassa, Timothy Lillicrap, Martin Riedmiller, and David Silver · 2016
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Reinforcement learning with unsupervised auxiliary tasks
Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z Leibo, David Silver, and Koray Kavukcuoglu · 2016
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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2016
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Information asymmetry in kl-regularized rl
Alexandre Galashov, Siddhant M Jayakumar, Leonard Hasenclever, Dhruva Tirumala, Jonathan Schwarz, Guillaume Desjardins, Wojciech M Czarnecki, Yee Whye Teh, Razvan Pascanu, and Nicolas Heess · 2018
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Latent space policies for hierarchical reinforcement learning
Tuomas Haarnoja, Kristian Hartikainen, Pieter Abbeel, and Sergey Levine · 2018
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When waiting is not an option: Learning options with a deliberation cost
Jean Harb, Pierre-Luc Bacon, Martin Klissarov, and Doina Precup · 2018
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Learning an embedding space for transferable robot skills
Karol Hausman, Jost Tobias Springenberg, Ziyu Wang, Nicolas Heess, and Martin Riedmiller · 2018
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Learning dexterous in-hand manipulation
OpenAI, Marcin Andrychowicz, Bowen Baker, Maciek Chociej, Rafal Józefowicz, Bob McGrew, Jakub W. Pachocki, Jakub Pachocki, Arthur Petron, Matthias Plappert, Glenn Powell, Alex Ray, Jonas Schneider, Szymon Sidor, Josh Tobin, Peter Welinder, Lilian Weng, and Wojciech Zaremba · 2018
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The concrete distribution: A continuous relaxation of discrete random variables
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Safe and efficient off-policy reinforcement learning
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Andrei A Rusu, Neil C Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell · 2016
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