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Resource Allocation in the Grid Using Reinforcement Learning
New York City, New York, USA July 19-July 23
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/AAMAS.2004.10281Third International Joint Conference ...
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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
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@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
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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
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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