Potential-based reward shaping using state-space segmentation for efficiency in reinforcement learning

dc.contributor.author	Bal, Melis İlayda
dc.date.accessioned	2025-04-07T07:53:05Z
dc.date.available	2025-04-07T07:53:05Z
dc.date.issued	2024
dc.identifier.issn	0167-739X
dc.identifier.uri	http://hdl.handle.net/10900/163846
dc.language.iso	en	de_DE
dc.publisher	Amsterdam : Elsevier	de_DE
dc.relation.uri	http://dx.doi.org/10.1016/j.future.2024.03.057	de_DE
dc.subject.ddc	004	de_DE
dc.title	Potential-based reward shaping using state-space segmentation for efficiency in reinforcement learning	de_DE
dc.type	Article	de_DE
utue.quellen.id	20241001000000_01928
utue.publikation.seiten	469-484	de_DE
utue.personen.roh	Bal, Melis Ilayda
utue.personen.roh	Aydin, Hueseyin
utue.personen.roh	Iyiguen, Cem
utue.personen.roh	Polat, Faruk
dcterms.isPartOf.ZSTitelID	Future Generation Computer Systems - The International Journal of Escience	de_DE
dcterms.isPartOf.ZS-Volume	157	de_DE