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Human-level control through deep reinforcement learning

Human-level control through deep reinforcement learning An artificial agent is developed that learns to play a diverse range of classic Atari 2600 computer games directly from sensory experience, achieving a performance comparable to that of an expert human player; this work paves the way to building general-purpose learning algorithms that bridge the divide between perception and action. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Nature Springer Journals

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References (48)

Publisher
Springer Journals
Copyright
Copyright © 2015 by Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.
Subject
Science, Humanities and Social Sciences, multidisciplinary; Science, Humanities and Social Sciences, multidisciplinary; Science, multidisciplinary
ISSN
0028-0836
eISSN
1476-4687
DOI
10.1038/nature14236
Publisher site
See Article on Publisher Site

Abstract

An artificial agent is developed that learns to play a diverse range of classic Atari 2600 computer games directly from sensory experience, achieving a performance comparable to that of an expert human player; this work paves the way to building general-purpose learning algorithms that bridge the divide between perception and action.

Journal

NatureSpringer Journals

Published: Feb 25, 2015

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