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Mobilized ad-hoc networks: A reinforcement learning approach

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dc.contributor.author Chang, Yu-Han en_US
dc.contributor.author Ho, Tracey en_US
dc.contributor.author Kaelbling, Leslie Pack en_US
dc.date.accessioned 2004-10-08T20:43:04Z
dc.date.available 2004-10-08T20:43:04Z
dc.date.issued 2003-12-04 en_US
dc.identifier.other AIM-2003-025 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/6732
dc.description.abstract Research in mobile ad-hoc networks has focused on situations in which nodes have no control over their movements. We investigate an important but overlooked domain in which nodes do have control over their movements. Reinforcement learning methods can be used to control both packet routing decisions and node mobility, dramatically improving the connectivity of the network. We first motivate the problem by presenting theoretical bounds for the connectivity improvement of partially mobile networks and then present superior empirical results under a variety of different scenarios in which the mobile nodes in our ad-hoc network are embedded with adaptive routing policies and learned movement policies. en_US
dc.format.extent 9 p. en_US
dc.format.extent 771382 bytes
dc.format.extent 1199447 bytes
dc.format.mimetype application/postscript
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.relation.ispartofseries AIM-2003-025 en_US
dc.subject AI en_US
dc.subject reinforcement learning en_US
dc.subject multi-agent learning en_US
dc.subject ad-hoc networking en_US
dc.title Mobilized ad-hoc networks: A reinforcement learning approach en_US


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