Learning to Play the Chess Variant Crazyhouse Above World Champion Level With Deep Neural Networks and Human Data.
Monte-Carlo tree search
chess
crazyhouse
deep learning
supervised learning
Journal
Frontiers in artificial intelligence
ISSN: 2624-8212
Titre abrégé: Front Artif Intell
Pays: Switzerland
ID NLM: 101770551
Informations de publication
Date de publication:
2020
2020
Historique:
received:
20
08
2019
accepted:
26
03
2020
entrez:
18
3
2021
pubmed:
19
3
2021
medline:
19
3
2021
Statut:
epublish
Résumé
Deep neural networks have been successfully applied in learning the board games Go, chess, and shogi without prior knowledge by making use of reinforcement learning. Although starting from zero knowledge has been shown to yield impressive results, it is associated with high computationally costs especially for complex games. With this paper, we present
Identifiants
pubmed: 33733143
doi: 10.3389/frai.2020.00024
pmc: PMC7861260
doi:
Types de publication
Journal Article
Langues
eng
Pagination
24Informations de copyright
Copyright © 2020 Czech, Willig, Beyer, Kersting and Fürnkranz.
Références
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