Exact-win strategy for overcoming AlphaZero

Yen Chi Chen, Chih Hung Chen, Shun Shii Lin

研究成果: 書貢獻/報告類型會議貢獻

摘要

The Monte-Carlo Tree Search used in the AlphaZero may easily miss a critical move because it is based on sampling search space and focuses on the most promising moves. In addition, the Monte-Carlo Tree Search may sample a move for many times even if this move has been explored with a determined game-theoretical value. In this paper, we propose an Exact-win-MCTS that makes use of sub-tree’s information (WIN, LOSS, DRAW, and UNKNOWN) to prune unneeded moves to increase the opportunities of discovering the critical moves. Our method improves and generalizes some previous MCTS variations as well as the AlphaZero approach. The experiments show that our Exact-win-MCTS substantially promotes the strengths of Tic-Tac-Toe, Connect4, and Go programs especially. Finally, our Exact-win Zero defeats the Leela Zero, which is a replication of AlphaZero and is currently one of the best open-source Go programs, with a significant 61% win rate. Therefore, we are pleased to announce that our Exact-win-MCTS has overcome the AlphaZero approach without using extra training time, playing time, or computer resources. As far as we know, this is the first practical idea with concrete experiments to beat the AlphaZero approach.

原文英語
主出版物標題2018 International Conference on Computational Intelligence and Intelligent Systems, CIIS 2018
發行者Association for Computing Machinery
頁面26-31
頁數6
ISBN(電子)9781450365956
DOIs
出版狀態已發佈 - 2018 十一月 17
事件2018 International Conference on Computational Intelligence and Intelligent Systems, CIIS 2018 - Phuket, 泰国
持續時間: 2018 十一月 172018 十一月 19

出版系列

名字ACM International Conference Proceeding Series

會議

會議2018 International Conference on Computational Intelligence and Intelligent Systems, CIIS 2018
國家泰国
城市Phuket
期間2018/11/172018/11/19

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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