Silver, D;
Hubert, T;
Schrittwieser, J;
Antonoglou, I;
Lai, M;
Guez, A;
Lanctot, M;
... Hassabis, D; + view all
(2018)
A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play.
Science
, 362
(6419)
pp. 1140-1144.
10.1126/science.aar6404.
Preview |
Text
alphazero_preprint.pdf - Accepted Version Download (3MB) | Preview |
Abstract
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.
| Type: | Article |
|---|---|
| Title: | A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1126/science.aar6404 |
| Publisher version: | https://doi.org/10.1126/science.aar6404 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10069050 |
Archive Staff Only
 |
View Item |
