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.
Description
Human-level control through deep reinforcement learning | Nature
%0 Journal Article
%1 mnih2015humanlevel
%A Mnih, Volodymyr
%A Kavukcuoglu, Koray
%A Silver, David
%A Rusu, Andrei A.
%A Veness, Joel
%A Bellemare, Marc G.
%A Graves, Alex
%A Riedmiller, Martin
%A Fidjeland, Andreas K.
%A Ostrovski, Georg
%A Petersen, Stig
%A Beattie, Charles
%A Sadik, Amir
%A Antonoglou, Ioannis
%A King, Helen
%A Kumaran, Dharshan
%A Wierstra, Daan
%A Legg, Shane
%A Hassabis, Demis
%D 2015
%J Nature
%K machinelearning neuralnetwork reinforcementlearning
%N 7540
%P 529--533
%R 10.1038/nature14236
%T Human-level control through deep reinforcement learning
%U https://doi.org/10.1038/nature14236
%V 518
%X 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.
@article{mnih2015humanlevel,
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.},
added-at = {2020-03-25T21:22:39.000+0100},
author = {Mnih, Volodymyr and Kavukcuoglu, Koray and Silver, David and Rusu, Andrei A. and Veness, Joel and Bellemare, Marc G. and Graves, Alex and Riedmiller, Martin and Fidjeland, Andreas K. and Ostrovski, Georg and Petersen, Stig and Beattie, Charles and Sadik, Amir and Antonoglou, Ioannis and King, Helen and Kumaran, Dharshan and Wierstra, Daan and Legg, Shane and Hassabis, Demis},
biburl = {https://www.bibsonomy.org/bibtex/2fb15f4471c81dc2b9edf2304cb2f7083/cpankow},
description = {Human-level control through deep reinforcement learning | Nature},
doi = {10.1038/nature14236},
interhash = {eac59980357d99db87b341b61ef6645f},
intrahash = {fb15f4471c81dc2b9edf2304cb2f7083},
issn = {14764687},
journal = {Nature},
keywords = {machinelearning neuralnetwork reinforcementlearning},
number = 7540,
pages = {529--533},
refid = {Mnih2015},
timestamp = {2020-03-25T21:22:39.000+0100},
title = {Human-level control through deep reinforcement learning},
url = {https://doi.org/10.1038/nature14236},
volume = 518,
year = 2015
}