Date of Award
12-2020
Culminating Project Type
Starred Paper
Degree Name
Computer Science: M.S.
Department
Computer Science and Information Technology
College
School of Science and Engineering
First Advisor
Bryant Julstrom
Second Advisor
Donald Hamnes
Third Advisor
Jie Meichsner
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
Abstract
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.
Recommended Citation
Palmersten, Joseph, "Approaching Hanabi with Q-Learning and Evolutionary Algorithm" (2020). Culminating Projects in Computer Science and Information Technology. 34.
https://repository.stcloudstate.edu/csit_etds/34
