Publication Date

Spring 5-20-2020

Degree Type

Master's Project

Degree Name

Master of Science (MS)


Computer Science

First Advisor

Chris Pollett

Second Advisor

Mark Stamp

Third Advisor

Fabio Di Troia


Dou Di Zhu, deep Q-learning, rule-based


We describe our implementation of AIs for the Chinese game Dou Di Zhu. Dou Di Zhu is a three-player game played with a standard 52 card deck together with two jokers. One player acts as a landlord and has the advantage of receiving three extra cards, the other two players play as peasants. We designed and implemented a Deep Q-learning Neural Network (DQN) agent to play the Dou Di Zhu. At the same time, we also designed and made a pure Q-learning based agent as well as a Zhou rule-based agent to compare with our main agent. We show the DQN model has a 10% higher win rate than the Q-learning model and Zhou rule-based model when playing as the landlord, and a 5% higher win rate than the other models when playing as a peasant.