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Collaborative reinforcement learning of autonomic behaviour

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dc.contributor.author Dowling, Jim
dc.contributor.author Cunningham, Raymond
dc.contributor.author Curran, Eoin
dc.contributor.author Cahill, Vinny
dc.date.accessioned 2011-07-15T10:21:24Z
dc.date.available 2011-07-15T10:21:24Z
dc.date.issued 2004
dc.identifier.uri http://hdl.handle.net/10344/1105
dc.description peer-reviewed
dc.description.abstract This paper introduces Collaborative Reinforcement Learning (CRL), a coordination model for solving system-wide optimisation problems in distributed systems where there is no support for global state. In CRL the autonomic properties of a distributed system emerge from the coordination of individual agents solving discrete optimisation problems using Reinforcement Learning. In the context of an ad hoc routing protocol, we show how system-wide optimisation in CRL can be used to establish and maintain autonomic properties for decentralised distributed systems. en_US
dc.description.sponsorship SFI
dc.language.iso eng en_US
dc.publisher IEEE Computer Society en_US
dc.subject collaborative reinforcement learning en_US
dc.title Collaborative reinforcement learning of autonomic behaviour en_US
dc.type Conference item en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.type.restriction none en

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