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Distributional Reinforcement Learning (RL) differs from traditional RL in that, rather than the expectation of total returns, it estimates distributions and has achieved state-of-the-art performance on Atari Games.
Dynamic Programming
Richard Bellman · 1957
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Robust estimation of a location parameter
Peter J. Huber · 1964
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Markov decision processes with a new optimality criterion: Discrete time
Stratton C Jaquette et al · 1973
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The variance of discounted markov decision processes
Matthew J Sobel · 1982
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Learning to predict by the methods of temporal differences
Richard S Sutton · 1988
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Mean, variance, and probabilistic criteria in finite markov decision processes: a review
DJ White · 1988
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Learning from delayed rewards
Christopher John Cornish Hellaby Watkins · 1989
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Markov Decision Processes: Discrete Stochastic Dynamic Programming
Martin L. Puterman · 1994
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Integral probability metrics and their generating classes of functions
Alfred Müller · 1997
Cited alongside, same era.
Nonparametric return distribution approximation for reinforcement learning
Tetsuro Morimura, Masashi Sugiyama, Hisashi Kashima, Hirotaka Hachiya, and Toshiyuki Tanaka · 2010
Cited alongside, same era.
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.
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
Cited alongside, same era.
Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver · 2016
Cited alongside, same era.
Distributed distributional deterministic policy gradients
Gabriel Barth-Maron, Matthew W Hoffman, David Budden, Will Dabney, Dan Horgan, Alistair Muldal, Nicolas Heess, and Timothy Lillicrap · 2018
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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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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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Deep reinforcement learning with double q-learning
Hado Van Hasselt, Arthur Guez, and David Silver · 2016
Cited alongside, same era.
Dueling network architectures for deep reinforcement learning
Ziyu Wang, Tom Schaul, Matteo Hessel, Hado Hasselt, Marc Lanctot, and Nando Freitas · 2016
Cited alongside, same era.
A distributional perspective on reinforcement learning
Marc G Bellemare, Will Dabney, and Rémi Munos · 2017
Cited alongside, same era.
Implicit quantile networks for distributional reinforcement learning
Will Dabney, Georg Ostrovski, David Silver, and Remi Munos
Cited in the paper.
Distributional reinforcement learning with quantile regression
Will Dabney, Mark Rowland, Marc G Bellemare, and Rémi Munos
Cited in the paper.
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An analysis of categorical distributional reinforcement learning
Mark Rowland, Marc Bellemare, Will Dabney, Remi Munos, and Yee Whye Teh · 2018
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Exploration by distributional reinforcement learning
Yunhao Tang and Shipra Agrawal · 2018
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