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