  Card game AI has always been a hot topic in the research of artificial
intelligence. In recent years, complex card games such as Mahjong, DouDizhu and
Texas Hold'em have been solved and the corresponding AI programs have reached
the level of human experts. In this paper, we are devoted to developing an AI
program for a more complex card game, GuanDan, whose rules are similar to
DouDizhu but much more complicated. To be specific, the characteristics of
large state and action space, long length of one episode and the unsure number
of players in the GuanDan pose great challenges for the development of the AI
program. To address these issues, we propose the first AI program DanZero for
GuanDan using reinforcement learning technique. Specifically, we utilize a
distributed framework to train our AI system. In the actor processes, we
carefully design the state features and agents generate samples by self-play.
In the learner process, the model is updated by Deep Monte-Carlo Method. After
training for 30 days using 160 CPUs and 1 GPU, we get our DanZero bot. We
compare it with 8 baseline AI programs which are based on heuristic rules and
the results reveal the outstanding performance of DanZero. We also test DanZero
with human players and demonstrate its human-level performance.
