Suphx: Mastering Mahjong with Deep Reinforcement Learning
  Artificial Intelligence (AI) has achieved great success in many domains, and
game AI is widely regarded as its beachhead since the dawn of AI. In recent
years, studies on game AI have gradually evolved from relatively simple
environments (e.g., perfect-information games such as Go, chess, shogi or
two-player imperfect-information games such as heads-up Texas hold'em) to more
complex ones (e.g., multi-player imperfect-information games such as
multi-player Texas hold'em and StartCraft II). Mahjong is a popular
multi-player imperfect-information game worldwide but very challenging for AI
research due to its complex playing/scoring rules and rich hidden information.
We design an AI for Mahjong, named Suphx, based on deep reinforcement learning
with some newly introduced techniques including global reward prediction,
oracle guiding, and run-time policy adaptation. Suphx has demonstrated stronger
performance than most top human players in terms of stable rank and is rated
above 99.99% of all the officially ranked human players in the Tenhou platform.
This is the first time that a computer program outperforms most top human
players in Mahjong.
