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Polygames: Improved Zero Learning

27 January 2020
Tristan Cazenave
Yen-Chi Chen
Guanting Chen
Shi-Yu Chen
Xian-Dong Chiu
J. Dehos
Maria Elsa
Qucheng Gong
Hengyuan Hu
Vasil Khalidov
Cheng-Ling Li
Hsin-I Lin
Yu-Jin Lin
Xavier Martinet
Vegard Mella
Jérémy Rapin
Baptiste Roziere
Gabriel Synnaeve
F. Teytaud
O. Teytaud
Shi-Cheng Ye
Yi-Jun Ye
Shi-Jim Yen
Sergey Zagoruyko
    OffRL
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Abstract

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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