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dc.contributor.authorChristensen, Sören
dc.contributor.authorTomforde, Sven
dc.date2022-08-01
dc.date.accessioned2022-09-09T12:40:19Z
dc.date.available2022-09-09T12:40:19Z
dc.date.issued2022
dc.identifier.issn1432-122X
dc.identifier.urihttp://dx.doi.org/10.1007/s00287-022-01468-x
dc.identifier.urihttp://dl.gi.de/handle/20.500.12116/39365
dc.description.abstractWe propose to establish a research direction based on Reinforcement Learning in the scope of Cross Domain Fusion. More precisely, we combine the algorithmic approach of evolutionary rule-based Reinforcement Learning with the efficiency and performance of Deep Reinforcement Learning, while simultaneously developing a sound mathematical foundation. A possible scenario is traffic control in urban regions.de
dc.publisherSpringer
dc.relation.ispartofInformatik Spektrum: Vol. 45, No. 4
dc.relation.ispartofseriesInformatik Spektrum
dc.titleReinforcement learning as a basis for cross domain fusion of heterogeneous datade
dc.typeText/Journal Article
mci.reference.pages214-217
dc.identifier.doi10.1007/s00287-022-01468-x


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