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Adaptive Load Balancing: A Study in Multi-Agent Learning (1995)
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| Venue: | Journal of Artificial Intelligence Research |
| Citations: | 94 - 0 self |
BibTeX
@ARTICLE{Schaerf95adaptiveload,
author = {Andrea Schaerf and Yoav Shoham and Moshe Tennenholtz},
title = {Adaptive Load Balancing: A Study in Multi-Agent Learning},
journal = {Journal of Artificial Intelligence Research},
year = {1995},
volume = {2},
pages = {475--500}
}
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Abstract
We study the process of multi-agent reinforcement learning in the context of load balancing in a distributed system, without use of either central coordination or explicit communication. We first define a precise framework in which to study adaptive load balancing, important features of which are its stochastic nature and the purely local information available to individual agents. Given this framework, we show illuminating results on the interplay between basic adaptive behavior parameters and their effect on system efficiency. We then investigate the properties of adaptive load balancing in heterogeneous populations, and address the issue of exploration vs. exploitation in that context. Finally, we show that naive use of communication may not improve, and might even harm system efficiency. 1. Introduction This article investigates multi-agent reinforcement learning in the context of a concrete problem of undisputed importance -- load balancing. Real life provides us with many exampl...
Keyphrases
adaptive load balancing multi-agent learning system efficiency multi-agent reinforcement learning concrete problem many exampl naive use important feature heterogeneous population undisputed importance load balancing stochastic nature exploration v distributed system real life explicit communication basic adaptive behavior parameter central coordination local information precise framework individual agent load balancing
