APPLYING SELF-PLAY REINFORCEMENT LEARNING TO KAZAKH NATIONAL BOARD GAMES: A CASE STUDY ON TOGYZKUMALAK
Dinara Zhussupova · Journal of problem in computer science and information technologies · 2026
The paper presents the results of a study on training agents for competitive board games, using the Kazakh national Togyzkumalak game, a zero--sum game, as a case study. As demonstrated by Alpha Zero, it is possible to train a champion without human expert knowledge or supervised learning. However, each game has unique characteristics that require specific research to develop an excellent player. In this study, two approaches were used to train agents: model-free and model-based. The self-play technique was used effectively during the training. As a result, the agents developed the ability to play Togyzkumalak at a competitive level.