Study of Multiple Multiagent Reinforcement Learning Algorithms in Grid Games

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  • 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 gameenvironment.

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  • Copyright © 2013 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
Date Created
  • 2013


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