Analysis of the Application of Artificial Intelligence in Mahjong

Abstract

Over the past few years, AI-based models for Mahjong have received a substantial amount of contribution due to the advancements in machine learning and game theory. In this review, these state-of-the-art AI models are considered, including Tjong, Kanachan, Suphx, and Zhejiang University’s developed model. The deployed approaches are disparately represented by reinforcement learning, Monte Carlo tree search (MCTS), and deep neural networks, all of which aim at solving the issues that come with the game’s structure, such as incomplete and overburdening information. The Tjong model complements the power of the transformer architecture with hierarchical decision-making and in turn, brings strategic depth to the gameplay. Kanachan employs Q-learning and MCTS to optimize decision-making, while Suphx combines these methods with the novel ideas of Double Q-learning and Thompson Sampling to achieve higher performance than seen before. The integration of reinforcement learning and a new evaluation model imbues the model with the unique properties of both learning machines and human expertise, namely depth and intelligence. As a result of all these exploits, obstacles still exist, the most notable among them are the management of completely unknown information, handling long-distance games, and the strategic balance between offense and defense. In the future, determinants of probabilistic reasoning, the improvement of deep learning systems, and the prowess of reward shaping will result in AI improvement in Mahjong.