Resource Allocation in the Grid Using Reinforcement Learning

  • Authors:
  • Aram Galstyan;Karl Czajkowski;Kristina Lerman

  • Affiliations:
  • University of Southern California;University of Southern California;University of Southern California

  • Venue:
  • AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
  • Year:
  • 2004

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Abstract

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.