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Deep reinforcement learning has been successfully applied to several visual-input tasks using model-free methods.
“Asynchronous Methods for Deep Reinforcement Learning”
Volodymyr Mnih et al · 1937
Earlier work this paper cites.
“Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search”
Rémi Coulom · 2006
Earlier work this paper cites.
“Bandit Based Monte-Carlo Planning”
Levente Kocsis and Csaba Szepesvári · 2006
Earlier work this paper cites.
“Reinforcement Learning: The Good, The Bad and The Ugly”
Peter Dayan and Yael Niv · 2008
Earlier work this paper cites.
T. Tieleman and G. Hinton · 2012
Cited alongside, same era.
“Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning”
Xiaoxiao Guo et al · 2014
Cited alongside, same era.
“Human-Level Control through Deep Reinforcement Learning”
Volodymyr Mnih et al · 2015
Cited alongside, same era.
“Action-Conditional Video Prediction Using Deep Networks in Atari Games”
Junhyuk Oh et al · 2015
Later among the works it cites.
“The Malmo Platform for Artificial Intelligence Experimentation”
Matthew Johnson, Katja Hofmann, Tim Hutton and David Bignell · 2016
Later among the works it cites.
“Control of Memory, Active Perception, and Action in Minecraft”
Junhyuk Oh, Valliappa Chockalingam, Satinder. Singh and Honglak Lee · 2016
Later among the works it cites.
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