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.

Date

January 1, 2004

Authors

Aram Galstyan, Karl Czajkowski, Kristina Lerman

Conference

Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004.

Volume

1

Pages

1314-1315

Publisher

IEEE Computer Society