Towards a Competitive 3-Player Mahjong AI using Deep Reinforcement Learning

Abstract

Mahjong is a multi-player imperfect-information game with challenging features for AI research. Sanma, being a 3-player variant of Japanese Riichi Mahjong, possesses unique characteristics and a more aggressive playing style than the 4-player game. It is thus challenging and of research interest in its own right, but has not been explored. We present Meowjong, the first ever AI for Sanma using deep reinforcement learning (RL). We define a 2-dimensional data structure for encoding the observable information in a game. We pre-train 5 convolutional neural networks (CNNs) for Sanma’s 5 actions—discard, Pon, Kan, Kita and Riichi, and enhance the major (discard) action’s model via self-play reinforcement learning. Meowjong demonstrates potential for becoming the state-of-the-art in Sanma, by achieving test accuracies comparable with AIs for 4-player Mahjong through supervised learning, and gaining a significant further enhancement from reinforcement learning.