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Our understanding of reinforcement learning (RL) has been shaped by theoretical and empirical results that were obtained decades ago using tabular representations and linear function approximators.
Neuronlike adaptive elements that can solve difficult learning control problems
Andrew G Barto, Richard S Sutton, and Charles W Anderson · 1983
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Learning to predict by the methods of temporal differences
Richard S. Sutton · 1988
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Learning from delayed rewards
Christopher J. C. H. Watkins · 1989
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Efficient memory-based learning for robot control
Andrew William Moore · 1990
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Q-learning
Christopher J. C. H. Watkins and Peter Dayan · 1992
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J. Williams · 1992
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Generalization in reinforcement learning: Successful examples using sparse coarse coding
Richard S. Sutton · 1995
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Incremental multi-step Q-learning
Jing Peng and Ronald J. Williams · 1996
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The arcade learning environment: An evaluation platform for general agents
Marc G. Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2013
Cited alongside, same era.
Guided policy search
Sergey Levine and Vladlen Koltun · 2013
Cited alongside, same era.
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, Stig Petersen, Charles Beattie, Amir Sadik, et al · 2015
Cited alongside, same era.
Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael I. Jordan, and Philipp Moritz · 2015
Cited alongside, same era.
Compress and control
Joel Veness, Marc G Bellemare, Marcus Hutter, Alvin Chua, Guillaume Desjardins, et al · 2015
Cited alongside, same era.
ViZDoom: A Doom-based AI research platform for visual reinforcement learning
Michał Kempka, Marek Wydmuch, Grzegorz Runc, Jakub Toczek, and Wojciech Jaśkowski · 2016
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Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
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Dueling network architectures for deep reinforcement learning
Ziyu Wang, Tom Schaul, Matteo Hessel, Hado van Hasselt, Marc Lanctot, and Nando de Freitas · 2016
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Learning to act by predicting the future
Alexey Dosovitskiy and Vladlen Koltun · 2017
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Deep reinforcement learning that matters
Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger · 2017
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Charles Blundell, Benigno Uria, Alexander Pritzel, Yazhe Li, Avraham Ruderman, Joel Z. Leibo, Jack Rae, Daan Wierstra, and Demis Hassabis · 2016
Cited alongside, same era.
Benchmarking deep reinforcement learning for continuous control
Yan Duan, Xi Chen, Rein Houthooft, John Schulman, and Pieter Abbeel · 2016
Cited alongside, same era.
Building machines that learn and think like people
Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum, and Samuel J. Gershman · 2017
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Reinforcement Learning: An Introduction
Richard S. Sutton and Andrew G. Barto · 2017
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