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Journal of Zhejiang University SCIENCE C 1998 Vol.-1 No.-1 P.

http://doi.org/10.1631/FITEE.2300493


TibetanGoTinyNet: a light weight U-Net style network for Zero learning of Tibetan Go


Author(s):  Xiali LI, Yanyin ZHANG, Licheng WU, Yandong CHEN, Junzhi YU

Affiliation(s):  Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing 100081, China; more

Corresponding email(s):   xiaer_li@163.com, 18560711191@163.com, wulicheng@tsinghua.edu.cn, chenyd2022@163.com, junzhi.yu@ia.ac.cn

Key Words:  Zero learning, Tibetan Go, U-Net, Self-attention mechanism, Capsule network, MCTS


Xiali LI, Yanyin ZHANG, Licheng WU, Yandong CHEN, Junzhi YU. TibetanGoTinyNet: a light weight U-Net style network for Zero learning of Tibetan Go[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .

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author="Xiali LI, Yanyin ZHANG, Licheng WU, Yandong CHEN, Junzhi YU",
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year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2300493"
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Abstract: 
The game of tibetan Go faces the scarcity of expert knowledge and studied literature. Therefore, we studied the zero learning model of tibetan Go under limited computing power resources and proposed a novel scaleinvariant u-Net style two-headed output lightweight network TibetanGoTinyNet. The lightweight convolutional neural networks (CNN) and capsule structure are applied to the encoder and decoder of the network to reduce computational burden and achieve better feature extract results. Several autonomous self-attentive mechanisms are integrated into the network to capture the tibetan Go boardâĂŹs spatial and global information and select important channels. The training data were generated entirely from self-play games. TibetanGoTinyNet achieved 62%âĂŞ78% winning rates against four other u-Net style models including Ghost-UNet and Res-UNet. It also achieved 75% winning rates in the ablation experiments on the attention mechanism with embedded positional information. The model saved about 33% of the training time with 45%âĂŞ50% winning rates for different Monte Carlo tree search (MCTS) counts when migrated from 9×9 to 11×11 boards. Code for our model is available at https://github.com/paulzyy/TibetanGoTinyNet.

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