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Bootstrapping is a core mechanism in Reinforcement Learning (RL).
Learning to predict by the methods of temporal differences
Richard S Sutton · 1988
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Q-learning
Christopher JCH Watkins and Peter Dayan · 1992
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Reinforcement Learning Through Gradient Descent
Leemon C Baird III · 1999
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Leverage the Average: an Analysis of Regularization in RL
Nino Vieillard, Tadashi Kozuno, Bruno Scherrer, Olivier Pietquin, Rémi Munos, and Matthieu Geist · 2003
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Convex optimization
Stephen Boyd and Lieven Vandenberghe · 2004
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Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy
Brian D Ziebart · 2010
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Action-gap phenomenon in reinforcement learning
Amir-massoud Farahmand · 2011
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Dynamic policy programming
Mohammad Gheshlaghi Azar, Vicenç Gómez, and Hilbert J Kappen · 2012
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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
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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, Dandelion 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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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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Approximate modified policy iteration and its application to the game of Tetris
Bruno Scherrer, Mohammad Ghavamzadeh, Victor Gabillon, Boris Lesner, and Matthieu Geist · 2015
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Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
Cited alongside, same era.
Increasing the action gap: New operators for reinforcement learning
Marc G Bellemare, Georg Ostrovski, Arthur Guez, Philip S Thomas, and Rémi Munos · 2016
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Taming the noise in reinforcement learning via soft updates
Roy Fox, Ari Pakman, and Naftali Tishby · 2016
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Prioritized experience replay
Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver · 2016
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A distributional perspective on reinforcement learning
Marc G Bellemare, Will Dabney, and Rémi Munos · 2017
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Reinforcement Learning with Deep Energy-Based Policies
Tuomas Haarnoja, Haoran Tang, Pieter Abbeel, and Sergey Levine · 2017
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Rainbow: Combining improvements in deep reinforcement learning
Matteo Hessel, Joseph Modayil, Hado Van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, and David Silver · 2018
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Recurrent experience replay in distributed reinforcement learning
Steven Kapturowski, Georg Ostrovski, John Quan, Remi Munos, and Will Dabney · 2018
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Revisiting the arcade learning environment: Evaluation protocols and open problems for general agents
Marlos C Machado, Marc G Bellemare, Erik Talvitie, Joel Veness, Matthew Hausknecht, and Michael Bowling · 2018
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A Theory of Regularized Markov Decision Processes
Matthieu Geist, Bruno Scherrer, and Olivier Pietquin · 2019
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Theoretical Analysis of Efficiency and Robustness of Softmax and Gap-Increasing Operators in Reinforcement Learning
Tadashi Kozuno, Eiji Uchibe, and Kenji Doya · 2019
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Deep dynamic policy programming for robot control with raw images
Yoshihisa Tsurumine, Yunduan Cui, Eiji Uchibe, and Takamitsu Matsubara · 2017
Cited alongside, same era.
Maximum a posteriori policy optimisation
Abbas Abdolmaleki, Jost Tobias Springenberg, Yuval Tassa, Remi Munos, Nicolas Heess, and Martin Riedmiller · 2018
Cited alongside, same era.
Dopamine: A research framework for deep reinforcement learning
Pablo Samuel Castro, Subhodeep Moitra, Carles Gelada, Saurabh Kumar, and Marc G Bellemare · 2018
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Implicit quantile networks for distributional reinforcement learning
Will Dabney, Georg Ostrovski, David Silver, and Rémi Munos · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
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Baron Munchhausen’s Narrative of his Marvellous Travels and Campaigns in Russia, 1785
Rudolf Erich Raspe
Cited in the paper.
A comparative analysis of expected and distributional reinforcement learning
Clare Lyle, Marc G Bellemare, and Pablo Samuel Castro · 2019
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Revisiting the Softmax Bellman Operator: New Benefits and New Perspective
Zhao Song, Ron Parr, and Lawrence Carin · 2019
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Using a logarithmic mapping to enable lower discount factors in reinforcement learning
Harm Van Seijen, Mehdi Fatemi, and Arash Tavakoli · 2019
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Fully parameterized quantile function for distributional reinforcement learning
Derek Yang, Li Zhao, Zichuan Lin, Tao Qin, Jiang Bian, and Tie-Yan Liu · 2019
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Agent57: Outperforming the atari human benchmark
Adrià Puigdomènech Badia, Bilal Piot, Steven Kapturowski, Pablo Sprechmann, Alex Vitvitskyi, Daniel Guo, and Charles Blundell · 2020
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SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference
Lasse Espeholt, Raphael Marinier, Piotr Stanczyk, Ke Wang, and Marcin Michalski · 2020
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