@inproceedings{fc9d0b5f5e974fd2af3d19809e868941,
title = "Efficient reinforcement learning for reversi AI",
abstract = "Reversi (or Othello) is a simple and popular board game played on an 8x8 board. In the field of reinforcement learning, searching of the game tree of Reversi is widely studied as a classic problem, since it has a small board and thus a state space not too complex to analyze. Monte Carlo tree search (MCTS) is a heuristic search algorithm for decision tree search, which is often applied to the AI methods for board games, such as the application of AlphaGo in the field of Go games. We modify and apply the Monte Carlo tree search strategy to Reversi AI. Applying some engineering optimizations (such as multithreading), we achieve significant results with high time efficiency.",
keywords = "board game, Monte Carlo tree search, reinforcement learning, Reversi",
author = "Haoran Chen and Keqin Liu",
note = "Publisher Copyright: {\textcopyright} 2023 SPIE.; 2nd International Conference on Statistics, Applied Mathematics, and Computing Science, CSAMCS 2022 ; Conference date: 25-11-2022 Through 27-11-2022",
year = "2023",
doi = "10.1117/12.2672198",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Wanyang Dai and Shi Jin",
booktitle = "Second International Conference on Statistics, Applied Mathematics, and Computing Science, CSAMCS 2022",
}