The Repository @ St. Cloud State

Open Access Knowledge and Scholarship

Date of Award


Culminating Project Type

Starred Paper

Degree Name

Computer Science: M.S.


Computer Science and Information Technology


School of Science and Engineering

First Advisor

Bryant Julstrom

Second Advisor

Donald Hamnes

Third Advisor

Jie Meichsner

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Keywords and Subject Headings

Artificial Intelligence, Evolutionary Algorithm, Gameplay, Machine Learning, Neural Networks, Q-Learning


Hanabi is a cooperative card game with hidden information that requires cooperation and communication between the players. For a machine learning agent to be successful at the Hanabi, it will have to learn how to communicate and infer information from the communication of other players. To approach the problem of Hanabi the machine learning methods of Q-learning and Evolutionary algorithm are proposed as potential solutions. The agents that were created using the method are shown to not achieve human levels of communication.



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