JZUS - Journal of Zhejiang University SCIENCE

Frontiers of Information Technology & Electronic Engineering

Accepted manuscript available online (unedited version)

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

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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,in press.https://doi.org/10.1631/FITEE.2300493

@article{title="TibetanGoTinyNet: a light weight U-Net style network for Zero learning of Tibetan Go",
author="Xiali LI, Yanyin ZHANG, Licheng WU, Yandong CHEN, Junzhi YU",
journal="Frontiers of Information Technology & Electronic Engineering",
year="in press",
publisher="Zhejiang University Press & Springer",
doi="https://doi.org/10.1631/FITEE.2300493"
}

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%A Xiali LI
%A Yanyin ZHANG
%A Licheng WU
%A Yandong CHEN
%A Junzhi YU
%J Frontiers of Information Technology & Electronic Engineering
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%@ 2095-9184
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%I Zhejiang University Press & Springer
doi="https://doi.org/10.1631/FITEE.2300493"

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T1 - TibetanGoTinyNet: a light weight U-Net style network for Zero learning of Tibetan Go
A1 - Xiali LI
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A1 - Licheng WU
A1 - Yandong CHEN
A1 - Junzhi YU
J0 - Frontiers of Information Technology & Electronic Engineering
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Y1 - in press
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doi="https://doi.org/10.1631/FITEE.2300493"

Abstract
Chinese Summary
Academic Network
Reviewer Comment

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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