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Polygames: Improved zero learning
Author(s) -
Tristan Cazenave,
YenChi Chen,
GuanWei Chen,
Shi-Yu Chen,
Xian-Dong Chiu,
Julien Dehos,
Maria Elsa,
Qucheng Gong,
Hengyuan Hu,
Vasil Khalidov,
Cheng-Ling Li,
HsinI Lin,
Yu-Jin Lin,
Xavier Martinet,
Vegard Mella,
Jérémy Rapin,
Baptiste Rozière,
Gabriel Synnaeve,
Fabien Teytaud,
Olivier Teytaud,
Shi-Cheng Ye,
Yi-Jun Ye,
Shi-Jim Yen,
Sergey Zagoruyko
Publication year - 2020
Publication title -
icga journal
Language(s) - English
Resource type - Journals
eISSN - 2468-2438
pISSN - 1389-6911
DOI - 10.3233/icg-200157
Subject(s) - zero (linguistics) , pooling , computer science , rank (graph theory) , layer (electronics) , artificial intelligence , multimedia , mathematics , combinatorics , philosophy , linguistics , chemistry , organic chemistry
Since DeepMind's AlphaZero, Zero learning quickly became the state-of-the-art method for many board games. It can be improved using a fully convolutional structure (no fully connected layer). Using such an architecture plus global pooling, we can create bots independent of the board size. The training can be made more robust by keeping track of the best checkpoints during the training and by training against them. Using these features, we release Polygames, our framework for Zero learning, with its library of games and its checkpoints. We won against strong humans at the game of Hex in 19x19, which was often said to be untractable for zero learning; and in Havannah. We also won several first places at the TAAI competitions.

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