Mastering the Game of Guandan with Deep Reinforcement Learning and
  Behavior Regulating
Yifan Yanggong Hao Pan Lei Wang
Games are a simplified model of reality and often serve as a favored platform
for Artificial Intelligence (AI) research. Much of the research is concerned
with game-playing agents and their decision making processes. The game of
Guandan (literally, "throwing eggs") is a challenging game where even
professional human players struggle to make the right decision at times. In
this paper we propose a framework named GuanZero for AI agents to master this
game using Monte-Carlo methods and deep neural networks. The main contribution
of this paper is about regulating agents' behavior through a carefully designed
neural network encoding scheme. We then demonstrate the effectiveness of the
proposed framework by comparing it with state-of-the-art approaches.