Reinforcement learning as a basis for cross domain fusion of heterogeneous data

dc.contributor.author	Christensen, Sören
dc.contributor.author	Tomforde, Sven
dc.date.accessioned	2023-01-13T13:58:11Z
dc.date.available	2023-01-13T13:58:11Z
dc.date.issued	2022
dc.description.abstract	We 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.identifier.doi	10.1007/s00287-022-01487-8
dc.identifier.pissn	1432-122X
dc.identifier.uri	http://dx.doi.org/10.1007/s00287-022-01487-8
dc.identifier.uri	https://dl.gi.de/handle/20.500.12116/39986
dc.publisher	Springer
dc.relation.ispartof	Informatik Spektrum: Vol. 45, No. 5
dc.relation.ispartofseries	Informatik Spektrum
dc.title	Reinforcement learning as a basis for cross domain fusion of heterogeneous data	de
dc.type	Text/Journal Article
gi.citation.endPage	299
gi.citation.startPage	295

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