Efficient reinforcement learning for reversi AI

Haoran Chen; Keqin Liu

doi:10.1117/12.2672198

28 March 2023 Efficient reinforcement learning for reversi AI

Haoran Chen, Keqin Liu

Proceedings Volume 12597, Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022); 125973Y (2023) https://doi.org/10.1117/12.2672198
Event: Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022), 2022, Nanjing, China

Abstract

Reversi (or Othello) is a simple and popular board game played on an eight-by-eight 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.

Citation Download Citation

Haoran Chen and Keqin Liu "Efficient reinforcement learning for reversi AI", Proc. SPIE 12597, Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022), 125973Y (28 March 2023); https://doi.org/10.1117/12.2672198

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available