Study of Multiple Multiagent Reinforcement Learning Algorithms in Grid Games

It appears your Web browser is not configured to display PDF files. Download adobe Acrobat or click here to download the PDF file.

Click here to download the PDF file.


De Beck-Courcelle, Pascal




This thesis studies multiagent reinforcement learning algorithms and their implementation; In particular the Minimax-Q algorithm, the Nash-Q algorithm and the WOLF-PHC algorithm. We evaluate their ability to reach a Nash equilibrium and their performance during learning in general-sum game environments. We also testtheir performance when playing against each other. We show the problems with implementing the Nash-Q algorithm and the inconvenience of using it in future research. We fully review the Lemke-Howson Algorithm used in the Nash-Q algorithm to find the Nash equilibrium in bimatrix games. We find that the WOLF-PHC is a more adaptable algorithm and it performs better than the others in a general-sum game environment.


PHYSICAL SCIENCES Engineering - System Science
PHYSICAL SCIENCES Artificial Intelligence




Carleton University

Thesis Degree Name: 

Master of Applied Science: 

Thesis Degree Level: 


Thesis Degree Discipline: 

Electrical and Computer Engineering

Parent Collection: 

Theses and Dissertations

Items in CURVE are protected by copyright, with all rights reserved, unless otherwise indicated. They are made available with permission from the author(s).