Multi-agent reinforcement learning in network management

Abstract

This paper outlines research in progress intended to contribute to the autonomous management of networks, allowing policies to be dynamically adjusted and aligned to application directives according to the available resources. Many existing management approaches require static a priori policy deployment but our proposal goes one step further modifying initially deployed policies by learning from the system behaviour. We use a hierarchical policy model to show the connection of high level goals with network level configurations. We also intend to solve two important and mostly forgotten issues: the system has multiple goals some of them contradictory and we will show how to overcome it; and, some current works optimize one network element but being unaware of other participants; instead, our proposed scheme takes into account various social behaviours, such as cooperation and competition among different elements. © 2009 IFIP International Federation for Information Processing.