A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play

• Published in: Science (American Association for the Advancement of Science) Vol. 362; no. 6419; pp. 1140 - 1144
• Main Authors: Silver, David, Hubert, Thomas, Schrittwieser, Julian, Antonoglou, Ioannis, Lai, Matthew, Guez, Arthur, Lanctot, Marc, Sifre, Laurent, Kumaran, Dharshan, Graepel, Thore, Lillicrap, Timothy, Simonyan, Karen, Hassabis, Demis
• Format: Journal Article
• Language: English
• Published: United States The American Association for the Advancement of Science 07.12.2018
• Subjects: Adaptation; Algorithms; Artificial intelligence; Chess; Computers; Games; Go/no-go discrimination learning; Machine learning; Mathematics; Reinforcement; State of the art

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...