Resource Allocation in the Grid Using Reinforcement Learning

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Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3 (AAMAS'04)

Resource Allocation in the Grid Using Reinforcement Learning (PDF)

New York City, New York, USA

July 19-July 23

ISBN: 0-7695-2092-8

	ASCII Text	x
	Aram Galstyan, Karl Czajkowski, Kristina Lerman, "Resource Allocation in the Grid Using Reinforcement Learning," Autonomous Agents and Multiagent Systems, International Joint Conference on, vol. 3, pp. 1314-1315, Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3 (AAMAS'04), 2004.

	BibTex	x
	@article{ 10.1109/AAMAS.2004.10281, author = {Aram Galstyan and Karl Czajkowski and Kristina Lerman}, title = {Resource Allocation in the Grid Using Reinforcement Learning}, journal ={Autonomous Agents and Multiagent Systems, International Joint Conference on}, volume = {3}, year = {2004}, isbn = {0-7695-2092-8}, pages = {1314-1315}, doi = {http://doi.ieeecomputersociety.org/10.1109/AAMAS.2004.10281}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Autonomous Agents and Multiagent Systems, International Joint Conference on TI - Resource Allocation in the Grid Using Reinforcement Learning SN - 0-7695-2092-8 SP1314 EP1315 A1 - Aram Galstyan, A1 - Karl Czajkowski, A1 - Kristina Lerman, PY - 2004 KW - null VL - 3 JA - Autonomous Agents and Multiagent Systems, International Joint Conference on ER -

Aram Galstyan, University of Southern California

Karl Czajkowski, University of Southern California

Kristina Lerman, University of Southern California

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/AAMAS.2004.10281

In this paper we study a minimalist decentralized algorithm for resource allocation in a simplified Grid-like environment. We consider a system consisting of large number of heterogenous reinforcement learning agents that share common resources for their computational needs. There is no communication between the agents: the only information that agents receive is the (expected) completion time of a job it submitted to a particular resource and which serves as a reinforcement signal for the agent. The results of our experiments suggest that reinforcement learning can be used to improve the quality of resource allocation in large scale heterogenous system.

Citation:

Aram Galstyan, Karl Czajkowski, Kristina Lerman, "Resource Allocation in the Grid Using Reinforcement Learning," aamas, vol. 3, pp.1314-1315, Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3 (AAMAS'04), 2004

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