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We adapt the optimization's concept of momentum to reinforcement learning.
An algorithm for quadratic programming
Marguerite Frank and Philip Wolfe · 1956
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Markov decision processes: discrete stochastic dynamic programming
Martin L Puterman · 1994
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On the generation of markov decision processes
TW Archibald, KIM McKinnon, and LC Thomas · 1995
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Neuro dynamic programming
Dimitri P Bertsekas and John N Tsitsiklis · 1996
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On the momentum term in gradient descent learning algorithms
Ning Qian · 1999
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Natural actor–critic algorithms
Shalabh Bhatnagar, Richard S Sutton, Mohammad Ghavamzadeh, and Mark Lee · 2009
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Speedy q-learning
Mohammad G Azar, Mohammad Ghavamzadeh, Hilbert J Kappen, and Rémi Munos · 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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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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Softened approximate policy iteration for markov games
Julien Pérolat, Bilal Piot, Matthieu Geist, Bruno Scherrer, and Olivier Pietquin · 2016
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A distributional perspective on reinforcement learning
Marc G Bellemare, Will Dabney, and Rémi Munos · 2017
Cited alongside, same era.
Count-based exploration with neural density models
Georg Ostrovski, Marc G Bellemare, Aäron van den Oord, and Rémi Munos · 2017
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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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 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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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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Politex: Regret bounds for policy iteration using expert prediction
Nevena Lazic, Yasin Abbasi-Yadkori, Kush Bhatia, Gellert Weisz, Peter Bartlett, and Csaba Szepesvari · 2019
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Variance-reduced q q -learning is minimax optimal
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