  People have made remarkable progress in game AIs, especially in domain of
perfect information game. However, trick-taking poker game, as a popular form
of imperfect information game, has been regarded as a challenge for a long
time. Since trick-taking game requires high level of not only reasoning, but
also inference to excel, it can be a new milestone for imperfect information
game AI. We study Gongzhu, a trick-taking game analogous to, but slightly
simpler than contract bridge. Nonetheless, the strategies of Gongzhu are
complex enough for both human and computer players. We train a strong Gongzhu
AI ScrofaZero from \textit{tabula rasa} by deep reinforcement learning, while
few previous efforts on solving trick-taking poker game utilize the
representation power of neural networks. Also, we introduce new techniques for
imperfect information game including stratified sampling, importance weighting,
integral over equivalent class, Bayesian inference, etc. Our AI can achieve
human expert level performance. The methodologies in building our program can
be easily transferred into a wide range of trick-taking games.
