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Reinforcement learning provides a general framework for learning robotic skills while minimizing engineering effort.
Curious model-building control systems
Jürgen Schmidhuber · 1991
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Introduction to reinforcement learning , volume 135
Richard S Sutton, Andrew G Barto, et al · 1998
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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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Algorithms for inverse reinforcement learning
Andrew Y Ng, Stuart J Russell, et al · 2000
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Eligibility traces for off-policy policy evaluation
Doina Precup · 2000
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Learning options in reinforcement learning
Martin Stolle and Doina Precup · 2002
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Apprenticeship learning via inverse reinforcement learning
Pieter Abbeel and Andrew Y Ng · 2004
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Policy gradient reinforcement learning for fast quadrupedal locomotion
Nate Kohl and Peter Stone · 2004
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All else being equal be empowered
Alexander S Klyubin, Daniel Polani, and Chrystopher L Nehaniv · 2005
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Neural fitted q iteration–first experiences with a data efficient neural reinforcement learning method
Martin Riedmiller · 2005
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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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Policy search for motor primitives in robotics
Jens Kober and Jan R Peters · 2009
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What is intrinsic motivation? a typology of computational approaches
Pierre-Yves Oudeyer and Frederic Kaplan · 2009
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Reinforcement learning for robot soccer
Martin Riedmiller, Thomas Gabel, Roland Hafner, and Sascha Lange · 2009
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Formal theory of creativity, fun, and intrinsic motivation (1990–2010)
Jürgen Schmidhuber · 2010
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Empowerment for continuous agent—environment systems
Tobias Jung, Daniel Polani, and Peter Stone · 2011
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Autonomous skill acquisition on a mobile manipulator
George Konidaris, Scott Kuindersma, Roderic Grupen, and Andrew Barto · 2011
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Hierarchical relative entropy policy search
Christian Daniel, Gerhard Neumann, and Jan Peters · 2012
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Active learning of inverse models with intrinsically motivated goal exploration in robots
Adrien Baranes and Pierre-Yves Oudeyer · 2013
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Reinforcement learning in robotics: A survey
Jens Kober, J Andrew Bagnell, and Jan Peters · 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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Doubly robust off-policy value evaluation for reinforcement learning
Nan Jiang and Lihong Li · 2015
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Inverse reward design
Dylan Hadfield-Menell, Smitha Milli, Pieter Abbeel, Stuart J Russell, and Anca Dragan · 2017
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Learning multi-level hierarchies with hindsight
Andrew Levy, George Konidaris, Robert Platt, and Kate Saenko · 2017
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Variational option discovery algorithms
Joshua Achiam, Harrison Edwards, Dario Amodei, and Pieter Abbeel · 2018
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Curious: intrinsically motivated modular multi-goal reinforcement learning
Cédric Colas, Pierre Fournier, Olivier Sigaud, Mohamed Chetouani, and Pierre-Yves Oudeyer · 2018
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Provably efficient maximum entropy exploration
Elad Hazan, Sham M Kakade, Karan Singh, and Abby Van Soest · 2018
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Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2015
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Variational information maximisation for intrinsically motivated reinforcement learning
Shakir Mohamed and Danilo Jimenez Rezende · 2015
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Unifying count-based exploration and intrinsic motivation
Marc Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, and Remi Munos · 2016
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Openai gym, 2016
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Continuous deep q-learning with model-based acceleration
Shixiang Gu, Timothy Lillicrap, Ilya Sutskever, and Sergey Levine · 2016
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Optimal control with learned local models: Application to dexterous manipulation
Vikash Kumar, Emanuel Todorov, and Sergey Levine · 2016
Cited alongside, same era.
Safe and efficient off-policy reinforcement learning
Rémi Munos, Tom Stepleton, Anna Harutyunyan, and Marc Bellemare · 2016
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Qt-opt: Scalable deep reinforcement learning for vision-based robotic manipulation
Dmitry Kalashnikov, Alex Irpan, Peter Pastor, Julian Ibarz, Alexander Herzog, Eric Jang, Deirdre Quillen, Ethan Holly, Mrinal Kalakrishnan, Vincent Vanhoucke, et al · 2018
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Benchmarking reinforcement learning algorithms on real-world robots
A Rupam Mahmood, Dmytro Korenkevych, Gautham Vasan, William Ma, and James Bergstra · 2018
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Data-efficient hierarchical reinforcement learning
Ofir Nachum, Shixiang Shane Gu, Honglak Lee, and Sergey Levine · 2018
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Robel: Robotics benchmarks for learning with low-cost robots
Michael Ahn, Henry Zhu, Kristian Hartikainen, Hugo Ponte, Abhishek Gupta, Sergey Levine, and Vikash Kumar · 2019
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Challenges of real-world reinforcement learning
Gabriel Dulac-Arnold, Daniel Mankowitz, and Todd Hester · 2019
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A divergence minimization perspective on imitation learning methods
Seyed Kamyar Seyed Ghasemipour, Richard Zemel, and Shixiang Gu · 2019
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Efficient exploration via state marginal matching
Lisa Lee, Benjamin Eysenbach, Emilio Parisotto, Eric Xing, Sergey Levine, and Ruslan Salakhutdinov · 2019
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Deep dynamics models for learning dexterous manipulation
Anusha Nagabandi, Kurt Konoglie, Sergey Levine, and Vikash Kumar · 2019
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Skew-fit: State-covering self-supervised reinforcement learning
Vitchyr H Pong, Murtaza Dalal, Steven Lin, Ashvin Nair, Shikhar Bahl, and Sergey Levine · 2019
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Dynamics-aware unsupervised discovery of skills
Archit Sharma, Shixiang Gu, Sergey Levine, Vikash Kumar, and Karol Hausman · 2020
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The ingredients of real world robotic reinforcement learning
Henry Zhu, Justin Yu, Abhishek Gupta, Dhruv Shah, Kristian Hartikainen, Avi Singh, Vikash Kumar, and Sergey Levine · 2020
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