Study of Multiple Multiagent Reinforcement Learning Algorithms in Grid Games

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Creator: 

De Beck-Courcelle, Pascal

Date: 

2013

Abstract: 

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.

Subject: 

PHYSICAL SCIENCES Engineering - System Science
PHYSICAL SCIENCES Artificial Intelligence

Language: 

English

Publisher: 

Carleton University

Thesis Degree Name: 

Master of Applied Science: 
M.App.Sc.

Thesis Degree Level: 

Master's

Thesis Degree Discipline: 

Electrical and Computer Engineering

Parent Collection: 

Theses and Dissertations

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