Routing in mobile ad-hoc networks as a reinforcement learning task

Abstract

Communicating nodes in Mobile Ad-hoc NETworks (MANETs) must deal with routing in an efficient and adaptive way. Efficiency feature is strongly recommended since both bandwidth and energy are scarce resources in MANETs. Besides, adaptivity is crucial to accomplish the routing task correctly in presence of varying network conditions in terms of mobility, links quality and traffic load. Our focus, in this paper, is on the application of Reinforcement Learning (RL) technique to achieve adaptive routing in MANETs. Particularly, we try to underline the main design-issues that arise when dealing with adaptive-routing as a Reinforcement Learning task. © 2011 Springer-Verlag.