Multilabeled Value Networks for Computer Go.

arXiv: Artificial Intelligence(2018)

引用 19|浏览18
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摘要
This paper proposes a new approach to a novel value network architecture for the game Go, called a multilabeled (ML) value network. In the ML value network, different values (win rates) are trained simultaneously for different settings of komi, a compensation given to balance the initiative of playing first. The ML value network has three advantages: 1) it outputs values for different komi; (2) it...
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关键词
Games,Training,Network architecture,Supervised learning,Learning (artificial intelligence),Monte Carlo methods,Servers
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