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Transfer learning in multi-agent systems through parallel transfer

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Show simple item record Taylor, Adam Duparic, Ivana Galván-López, Edgar Clarke, Siobhán Cahill, Vinny 2013-08-09T15:06:07Z 2013-08-09T15:06:07Z 2013
dc.description peer-reviewed en_US
dc.description.abstract Transfer Learning(TL) has been shown to significantly accelerate the convergence of a reinforcement learning process. TL is the process of reusing learned information across tasks. Information is shared between a source and a target task. Previous work has required that the target task wait until the source task has finished learning before transferring information. The execution of the source task prior to the target task considerably extends the time required for the target task to complete. This paper proposes a novel approach allowing both source and target task to learn in parallel. This allows the transfer to be bi-directional, so processes can act as both source and target simultaneously. This, in consequence, allows tasks to learn from each other's experiences and thereby reduces the learning time required. The proposed ap- proach is evaluated on a multi-agent smart- grid scenario. en_US
dc.language.iso eng en_US
dc.relation.ispartofseries Proceedings of the Workshop on Theoretically Grounded Transfer Learning at the 30th International Conference on Machine Learning;
dc.subject transfer learning en_US
dc.subject TL en_US
dc.subject multi-agent sysems en_US
dc.title Transfer learning in multi-agent systems through parallel transfer en_US
dc.type info:eu-repo/semantics/conferenceObject en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.contributor.sponsor SFI en_US
dc.relation.projectid 10/IN.1/I2980 en_US
dc.relation.projectid 10/CE/I1855 en_US
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US

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