  Mahjong is a popular multi-player imperfect-information game developed in
China in the late 19th-century, with some very challenging features for AI
research. Sanma, being a 3-player variant of the Japanese Riichi Mahjong,
possesses unique characteristics including fewer tiles and, consequently, a
more aggressive playing style. It is thus challenging and of great research
interest in its own right, but has not yet been explored. In this paper, we
present Meowjong, an AI for Sanma using deep reinforcement learning. We define
an informative and compact 2-dimensional data structure for encoding the
observable information in a Sanma game. We pre-train 5 convolutional neural
networks (CNNs) for Sanma's 5 actions -- discard, Pon, Kan, Kita and Riichi,
and enhance the major action's model, namely the discard model, via self-play
reinforcement learning using the Monte Carlo policy gradient method. Meowjong's
models achieve test accuracies comparable with AIs for 4-player Mahjong through
supervised learning, and gain a significant further enhancement from
reinforcement learning. Being the first ever AI in Sanma, we claim that
Meowjong stands as a state-of-the-art in this game.
