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Mobilized ad-hoc networks: A reinforcement learning approach

By Yu-Han Chang, Tracey Ho and Leslie Pack Kaelbling

Abstract

Research in mobile ad-hoc networks has focused on situations in whichnodes have no control over their movements. We investigate animportant but overlooked domain in which nodes do have controlover their movements. Reinforcement learning methods can be used tocontrol both packet routing decisions and node mobility, dramaticallyimproving the connectivity of the network. We first motivate theproblem by presenting theoretical bounds for the connectivityimprovement of partially mobile networks and then present superiorempirical results under a variety of different scenarios in which themobile nodes in our ad-hoc network are embedded with adaptive routingpolicies and learned movement policies

Topics: AI, reinforcement learning, multi-agent learning, ad-hoc networking
Year: 2003
OAI identifier: oai:dspace.mit.edu:1721.1/30437
Provided by: DSpace@MIT
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