Mastering the game of Go without human knowledge

A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa , superhuman proficiency in challenging domains. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. The tree search in AlphaGo evaluated positions and selected moves using d...

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Vydáno v:	Nature (London) Ročník 550; číslo 7676; s. 354 - 359
Hlavní autoři:	Silver, David, Schrittwieser, Julian, Simonyan, Karen, Antonoglou, Ioannis, Huang, Aja, Guez, Arthur, Hubert, Thomas, Baker, Lucas, Lai, Matthew, Bolton, Adrian, Chen, Yutian, Lillicrap, Timothy, Hui, Fan, Sifre, Laurent, van den Driessche, George, Graepel, Thore, Hassabis, Demis
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Nature Publishing Group UK 19.10.2017 Nature Publishing Group
Témata:	631/378/1788 639/705/1042 639/705/117 Algorithms Artificial intelligence Artificial neural networks Computer games Forecasts and trends Games Games, Recreational Go (Game) Humanities and Social Sciences Humans Learning Machine learning Mastering multidisciplinary Neural networks Neural Networks (Computer) Probability Reinforcement Reinforcement (Psychology) Science Software Supervised Machine Learning Technology application Unsupervised Machine Learning
ISSN:	0028-0836, 1476-4687, 1476-4687
On-line přístup:	Získat plný text
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Abstract	A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa , superhuman proficiency in challenging domains. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. The tree search in AlphaGo evaluated positions and selected moves using deep neural networks. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from self-play. Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGo’s own move selections and also the winner of AlphaGo’s games. This neural network improves the strength of the tree search, resulting in higher quality move selection and stronger self-play in the next iteration. Starting tabula rasa , our new program AlphaGo Zero achieved superhuman performance, winning 100–0 against the previously published, champion-defeating AlphaGo. Starting from zero knowledge and without human data, AlphaGo Zero was able to teach itself to play Go and to develop novel strategies that provide new insights into the oldest of games. AlphaGo Zero goes solo To beat world champions at the game of Go, the computer program AlphaGo has relied largely on supervised learning from millions of human expert moves. David Silver and colleagues have now produced a system called AlphaGo Zero, which is based purely on reinforcement learning and learns solely from self-play. Starting from random moves, it can reach superhuman level in just a couple of days of training and five million games of self-play, and can now beat all previous versions of AlphaGo. Because the machine independently discovers the same fundamental principles of the game that took humans millennia to conceptualize, the work suggests that such principles have some universal character, beyond human bias.
AbstractList	A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. The tree search in AlphaGo evaluated positions and selected moves using deep neural networks. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from self-play. Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGo's own move selections and also the winner of AlphaGo's games. This neural network improves the strength of the tree search, resulting in higher quality move selection and stronger self-play in the next iteration. Starting tabula rasa, our new program AlphaGo Zero achieved superhuman performance, winning 100-0 against the previously published, champion-defeating AlphaGo. A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. The tree search in AlphaGo evaluated positions and selected moves using deep neural networks. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from self-play. Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGos own move selections and also the winner of AlphaGos games. This neural network improves the strength of the tree search, resulting in higher quality move selection and stronger self-play in the next iteration. Starting tabula rasa:, our new program AlphaGo Zero achieved superhuman performance, winning 1000 against the previously published, champion-defeating AlphaGo. A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. The tree search in AlphaGo evaluated positions and selected moves using deep neural networks. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from self-play. Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGo's own move selections and also the winner of AlphaGo's games. This neural network improves the strength of the tree search, resulting in higher quality move selection and stronger self-play in the next iteration. Starting tabula rasa, our new program AlphaGo Zero achieved superhuman performance, winning 100-0 against the previously published, champion-defeating AlphaGo.A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. The tree search in AlphaGo evaluated positions and selected moves using deep neural networks. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from self-play. Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGo's own move selections and also the winner of AlphaGo's games. This neural network improves the strength of the tree search, resulting in higher quality move selection and stronger self-play in the next iteration. Starting tabula rasa, our new program AlphaGo Zero achieved superhuman performance, winning 100-0 against the previously published, champion-defeating AlphaGo. A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa , superhuman proficiency in challenging domains. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. The tree search in AlphaGo evaluated positions and selected moves using deep neural networks. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from self-play. Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGo’s own move selections and also the winner of AlphaGo’s games. This neural network improves the strength of the tree search, resulting in higher quality move selection and stronger self-play in the next iteration. Starting tabula rasa , our new program AlphaGo Zero achieved superhuman performance, winning 100–0 against the previously published, champion-defeating AlphaGo. Starting from zero knowledge and without human data, AlphaGo Zero was able to teach itself to play Go and to develop novel strategies that provide new insights into the oldest of games. AlphaGo Zero goes solo To beat world champions at the game of Go, the computer program AlphaGo has relied largely on supervised learning from millions of human expert moves. David Silver and colleagues have now produced a system called AlphaGo Zero, which is based purely on reinforcement learning and learns solely from self-play. Starting from random moves, it can reach superhuman level in just a couple of days of training and five million games of self-play, and can now beat all previous versions of AlphaGo. Because the machine independently discovers the same fundamental principles of the game that took humans millennia to conceptualize, the work suggests that such principles have some universal character, beyond human bias.
Audience	Academic
Author	Baker, Lucas Bolton, Adrian Lillicrap, Timothy Schrittwieser, Julian Chen, Yutian van den Driessche, George Sifre, Laurent Simonyan, Karen Lai, Matthew Hui, Fan Graepel, Thore Guez, Arthur Huang, Aja Antonoglou, Ioannis Silver, David Hubert, Thomas Hassabis, Demis
Author_xml	– sequence: 1 givenname: David surname: Silver fullname: Silver, David email: davidsilver@google.com organization: DeepMind – sequence: 2 givenname: Julian surname: Schrittwieser fullname: Schrittwieser, Julian organization: DeepMind – sequence: 3 givenname: Karen surname: Simonyan fullname: Simonyan, Karen organization: DeepMind – sequence: 4 givenname: Ioannis surname: Antonoglou fullname: Antonoglou, Ioannis organization: DeepMind – sequence: 5 givenname: Aja surname: Huang fullname: Huang, Aja organization: DeepMind – sequence: 6 givenname: Arthur surname: Guez fullname: Guez, Arthur organization: DeepMind – sequence: 7 givenname: Thomas surname: Hubert fullname: Hubert, Thomas organization: DeepMind – sequence: 8 givenname: Lucas surname: Baker fullname: Baker, Lucas organization: DeepMind – sequence: 9 givenname: Matthew surname: Lai fullname: Lai, Matthew organization: DeepMind – sequence: 10 givenname: Adrian surname: Bolton fullname: Bolton, Adrian organization: DeepMind – sequence: 11 givenname: Yutian surname: Chen fullname: Chen, Yutian organization: DeepMind – sequence: 12 givenname: Timothy surname: Lillicrap fullname: Lillicrap, Timothy organization: DeepMind – sequence: 13 givenname: Fan surname: Hui fullname: Hui, Fan organization: DeepMind – sequence: 14 givenname: Laurent surname: Sifre fullname: Sifre, Laurent organization: DeepMind – sequence: 15 givenname: George surname: van den Driessche fullname: van den Driessche, George organization: DeepMind – sequence: 16 givenname: Thore surname: Graepel fullname: Graepel, Thore organization: DeepMind – sequence: 17 givenname: Demis surname: Hassabis fullname: Hassabis, Demis organization: DeepMind
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/29052630$$D View this record in MEDLINE/PubMed
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Snippet	A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa , superhuman proficiency in challenging domains. Recently, AlphaGo... A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. Recently, AlphaGo...
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Title	Mastering the game of Go without human knowledge
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