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 Gheshlaghi Azar; David Silver

doi:https://doi.org/10.1609/aaai.v32i1.11796

Rainbow: Combining Improvements in Deep Reinforcement Learning

2018 · Proceedings of the AAAI Conference on Artificial Intelligence · 1,783 citations · 1 from inside this corpus

Matteo Hessel, Joseph Modayil low, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Gheshlaghi Azar, David Silver

The source holds an abstract for this work, but its best open-access copy is under no open licence, which does not permit us to republish the text. Read it at the source below.

12 of 12 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.

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Citation edge evidence

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Mastering Atari, Go, chess and shogi by planning with a learned model→Rainbow: Combining Improvements in Deep Reinforcement LearningLegacy edge — source detail unavailable
Rainbow: Combining Improvements in Deep Reinforcement Learning→Adam: A Method for Stochastic OptimizationLegacy edge — source detail unavailable
Rainbow: Combining Improvements in Deep Reinforcement Learning→Reinforcement Learning: An IntroductionLegacy edge — source detail unavailable
Rainbow: Combining Improvements in Deep Reinforcement Learning→Reinforcement Learning: An IntroductionLegacy edge — source detail unavailable
Rainbow: Combining Improvements in Deep Reinforcement Learning→Deep Reinforcement Learning with Double Q-LearningLegacy edge — source detail unavailable
Rainbow: Combining Improvements in Deep Reinforcement Learning→Playing Atari with Deep Reinforcement LearningLegacy edge — source detail unavailable
Rainbow: Combining Improvements in Deep Reinforcement Learning→Deep Reinforcement Learning with Double Q-LearningLegacy edge — source detail unavailable
Rainbow: Combining Improvements in Deep Reinforcement Learning→Trust Region Policy OptimizationLegacy edge — source detail unavailable
Rainbow: Combining Improvements in Deep Reinforcement Learning→Learning to predict by the methods of temporal differencesLegacy edge — source detail unavailable
Rainbow: Combining Improvements in Deep Reinforcement Learning→Dueling Network Architectures for Deep Reinforcement LearningLegacy edge — source detail unavailable
Rainbow: Combining Improvements in Deep Reinforcement Learning→Asynchronous Methods for Deep Reinforcement LearningLegacy edge — source detail unavailable
Rainbow: Combining Improvements in Deep Reinforcement Learning→Self-improving reactive agents based on reinforcement learning, planning and teachingLegacy edge — source detail unavailable
Playing Atari with Deep Reinforcement Learning→Reinforcement Learning: An IntroductionLegacy edge — source detail unavailable
Trust Region Policy Optimization→Playing Atari with Deep Reinforcement LearningLegacy edge — source detail unavailable
Deep Reinforcement Learning with Double Q-Learning→Self-improving reactive agents based on reinforcement learning, planning and teachingLegacy edge — source detail unavailable
Deep Reinforcement Learning with Double Q-Learning→Learning to predict by the methods of temporal differencesLegacy edge — source detail unavailable
Dueling Network Architectures for Deep Reinforcement Learning→Deep Reinforcement Learning with Double Q-LearningLegacy edge — source detail unavailable
Asynchronous Methods for Deep Reinforcement Learning→Adam: A Method for Stochastic OptimizationLegacy edge — source detail unavailable
Asynchronous Methods for Deep Reinforcement Learning→Playing Atari with Deep Reinforcement LearningLegacy edge — source detail unavailable
Asynchronous Methods for Deep Reinforcement Learning→Reinforcement Learning: An IntroductionLegacy edge — source detail unavailable
Deep Reinforcement Learning with Double Q-Learning→Learning to predict by the methods of temporal differencesLegacy edge — source detail unavailable
Mastering Atari, Go, chess and shogi by planning with a learned model→Reinforcement Learning: An IntroductionLegacy edge — source detail unavailable

What this paper cites, inside the corpus

Paper	Year	Cited
Adam: A Method for Stochastic Optimization	2014	84,698
Reinforcement Learning: An Introduction	1998	27,409
Reinforcement Learning: An Introduction	2005	25,758
Deep Reinforcement Learning with Double Q-Learning	2016	6,271
Playing Atari with Deep Reinforcement Learning	2013	5,104
Deep Reinforcement Learning with Double Q-Learning	2016	3,516
Trust Region Policy Optimization	2015	3,129
Learning to predict by the methods of temporal differences	1988	2,818
Dueling Network Architectures for Deep Reinforcement Learning	2015	1,811
Asynchronous Methods for Deep Reinforcement Learning	2016	1,689
Self-improving reactive agents based on reinforcement learning, planning and teaching	1992	1,656

What cites it, inside the corpus

Paper	Year	Cited
Mastering Atari, Go, chess and shogi by planning with a learned model		1,374

Links

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Topics

Reinforcement Learning in Robotics	Computer Science
Evolutionary Algorithms and Applications	Computer Science
Modular Robots and Swarm Intelligence	Engineering

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complete

Nothing in this record contradicts itself and no field we check is missing.

supports10 author record(s) attached.
supports58 reference(s) recorded.
neutralThe DOI carries no year to check against.
supportsA title is present.

Provenance

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