A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
Computers can beat humans at increasingly complex games, including chess and Go. However, these programs are typically constructed for a particular game, exploiting its properties, such as the symmetries of the board on which it is played. Silver et al. developed a program called AlphaZero, which ta...
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| Published in | Science (American Association for the Advancement of Science) Vol. 362; no. 6419; pp. 1140 - 1144 |
|---|---|
| Main Authors | , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
The American Association for the Advancement of Science
07.12.2018
|
| Subjects | |
| Online Access | Get full text |
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| Abstract | Computers can beat humans at increasingly complex games, including chess and Go. However, these programs are typically constructed for a particular game, exploiting its properties, such as the symmetries of the board on which it is played. Silver
et al.
developed a program called AlphaZero, which taught itself to play Go, chess, and shogi (a Japanese version of chess) (see the Editorial, and the Perspective by Campbell). AlphaZero managed to beat state-of-the-art programs specializing in these three games. The ability of AlphaZero to adapt to various game rules is a notable step toward achieving a general game-playing system.
Science
, this issue p.
1140
; see also pp.
1087
and
1118
AlphaZero teaches itself to play three different board games and beats state-of-the-art programs in each.
The game of chess is the longest-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific adaptations, and handcrafted evaluation functions that have been refined by human experts over several decades. By contrast, the AlphaGo Zero program recently achieved superhuman performance in the game of Go by reinforcement learning from self-play. In this paper, we generalize this approach into a single AlphaZero algorithm that can achieve superhuman performance in many challenging games. Starting from random play and given no domain knowledge except the game rules, AlphaZero convincingly defeated a world champion program in the games of chess and shogi (Japanese chess), as well as Go. |
|---|---|
| AbstractList | Computers can beat humans at increasingly complex games, including chess and Go. However, these programs are typically constructed for a particular game, exploiting its properties, such as the symmetries of the board on which it is played. Silver
et al.
developed a program called AlphaZero, which taught itself to play Go, chess, and shogi (a Japanese version of chess) (see the Editorial, and the Perspective by Campbell). AlphaZero managed to beat state-of-the-art programs specializing in these three games. The ability of AlphaZero to adapt to various game rules is a notable step toward achieving a general game-playing system.
Science
, this issue p.
1140
; see also pp.
1087
and
1118
AlphaZero teaches itself to play three different board games and beats state-of-the-art programs in each.
The game of chess is the longest-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific adaptations, and handcrafted evaluation functions that have been refined by human experts over several decades. By contrast, the AlphaGo Zero program recently achieved superhuman performance in the game of Go by reinforcement learning from self-play. In this paper, we generalize this approach into a single AlphaZero algorithm that can achieve superhuman performance in many challenging games. Starting from random play and given no domain knowledge except the game rules, AlphaZero convincingly defeated a world champion program in the games of chess and shogi (Japanese chess), as well as Go. The game of chess is the longest-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific adaptations, and handcrafted evaluation functions that have been refined by human experts over several decades. By contrast, the AlphaGo Zero program recently achieved superhuman performance in the game of Go by reinforcement learning from self-play. In this paper, we generalize this approach into a single AlphaZero algorithm that can achieve superhuman performance in many challenging games. Starting from random play and given no domain knowledge except the game rules, AlphaZero convincingly defeated a world champion program in the games of chess and shogi (Japanese chess), as well as Go. The game of chess is the longest-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific adaptations, and handcrafted evaluation functions that have been refined by human experts over several decades. By contrast, the AlphaGo Zero program recently achieved superhuman performance in the game of Go by reinforcement learning from self-play. In this paper, we generalize this approach into a single AlphaZero algorithm that can achieve superhuman performance in many challenging games. Starting from random play and given no domain knowledge except the game rules, AlphaZero convincingly defeated a world champion program in the games of chess and shogi (Japanese chess), as well as Go.The game of chess is the longest-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific adaptations, and handcrafted evaluation functions that have been refined by human experts over several decades. By contrast, the AlphaGo Zero program recently achieved superhuman performance in the game of Go by reinforcement learning from self-play. In this paper, we generalize this approach into a single AlphaZero algorithm that can achieve superhuman performance in many challenging games. Starting from random play and given no domain knowledge except the game rules, AlphaZero convincingly defeated a world champion program in the games of chess and shogi (Japanese chess), as well as Go. One program to rule them allComputers can beat humans at increasingly complex games, including chess and Go. However, these programs are typically constructed for a particular game, exploiting its properties, such as the symmetries of the board on which it is played. Silver et al. developed a program called AlphaZero, which taught itself to play Go, chess, and shogi (a Japanese version of chess) (see the Editorial, and the Perspective by Campbell). AlphaZero managed to beat state-of-the-art programs specializing in these three games. The ability of AlphaZero to adapt to various game rules is a notable step toward achieving a general game-playing system.Science, this issue p. 1140; see also pp. 1087 and 1118The game of chess is the longest-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific adaptations, and handcrafted evaluation functions that have been refined by human experts over several decades. By contrast, the AlphaGo Zero program recently achieved superhuman performance in the game of Go by reinforcement learning from self-play. In this paper, we generalize this approach into a single AlphaZero algorithm that can achieve superhuman performance in many challenging games. Starting from random play and given no domain knowledge except the game rules, AlphaZero convincingly defeated a world champion program in the games of chess and shogi (Japanese chess), as well as Go. |
| Author | Lillicrap, Timothy Schrittwieser, Julian Sifre, Laurent Simonyan, Karen Lai, Matthew Kumaran, Dharshan Graepel, Thore Guez, Arthur Lanctot, Marc Antonoglou, Ioannis Silver, David Hubert, Thomas Hassabis, Demis |
| Author_xml | – sequence: 1 givenname: David surname: Silver fullname: Silver, David organization: DeepMind, 6 Pancras Square, London N1C 4AG, UK., University College London, Gower Street, London WC1E 6BT, UK – sequence: 2 givenname: Thomas surname: Hubert fullname: Hubert, Thomas organization: DeepMind, 6 Pancras Square, London N1C 4AG, UK – sequence: 3 givenname: Julian surname: Schrittwieser fullname: Schrittwieser, Julian organization: DeepMind, 6 Pancras Square, London N1C 4AG, UK – sequence: 4 givenname: Ioannis surname: Antonoglou fullname: Antonoglou, Ioannis organization: DeepMind, 6 Pancras Square, London N1C 4AG, UK – sequence: 5 givenname: Matthew surname: Lai fullname: Lai, Matthew organization: DeepMind, 6 Pancras Square, London N1C 4AG, UK – sequence: 6 givenname: Arthur surname: Guez fullname: Guez, Arthur organization: DeepMind, 6 Pancras Square, London N1C 4AG, UK – sequence: 7 givenname: Marc surname: Lanctot fullname: Lanctot, Marc organization: DeepMind, 6 Pancras Square, London N1C 4AG, UK – sequence: 8 givenname: Laurent surname: Sifre fullname: Sifre, Laurent organization: DeepMind, 6 Pancras Square, London N1C 4AG, UK – sequence: 9 givenname: Dharshan orcidid: 0000-0001-5995-5264 surname: Kumaran fullname: Kumaran, Dharshan organization: DeepMind, 6 Pancras Square, London N1C 4AG, UK – sequence: 10 givenname: Thore orcidid: 0000-0003-3957-0310 surname: Graepel fullname: Graepel, Thore organization: DeepMind, 6 Pancras Square, London N1C 4AG, UK – sequence: 11 givenname: Timothy surname: Lillicrap fullname: Lillicrap, Timothy organization: DeepMind, 6 Pancras Square, London N1C 4AG, UK – sequence: 12 givenname: Karen surname: Simonyan fullname: Simonyan, Karen organization: DeepMind, 6 Pancras Square, London N1C 4AG, UK – sequence: 13 givenname: Demis surname: Hassabis fullname: Hassabis, Demis organization: DeepMind, 6 Pancras Square, London N1C 4AG, UK |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30523106$$D View this record in MEDLINE/PubMed |
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| Snippet | Computers can beat humans at increasingly complex games, including chess and Go. However, these programs are typically constructed for a particular game,... The game of chess is the longest-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated... One program to rule them allComputers can beat humans at increasingly complex games, including chess and Go. However, these programs are typically constructed... |
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| SubjectTerms | Adaptation Algorithms Artificial intelligence Chess Computers Games Go/no-go discrimination learning Machine learning Mathematics Reinforcement State of the art |
| Title | A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play |
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