Publication:

Distill2Explain: Differentiable decision trees for explainable reinforcement learning in energy application controllers

Date

2024-01-01
Proceedings Paper
https://doi.org/10.1145/3632775.3661937
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dc.contributor.authorGokhale, Gargya
dc.contributor.authorKarimi Madahi, Seyed Soroush
dc.contributor.authorClaessens, Bert
dc.contributor.authorDevelder, Chris
dc.date.accessioned2025-12-16T09:30:40Z
dc.date.available2025-12-16T09:30:40Z
dc.date.createdwos2025-09-25
dc.date.issued2024-01-01
dc.description.wosFundingTextThis research has received funding from the Horizon 2020 Project RENergetic (grant no. 957845) and Energy Transition Fund's FlexMyHeat project.
dc.identifier.doi10.1145/3632775.3661937
dc.identifier.urihttps://imec-publications.be/handle/20.500.12860/58581
dc.language.isoeng
dc.provenance.editstepusergreet.vanhoof@imec.be
dc.publisherASSOC COMPUTING MACHINERY
dc.source.beginpage55
dc.source.conference15TH ACM INTERNATIONAL CONFERENCE ON FUTURE AND SUSTAINABLE ENERGY SYSTEMS, E-ENERGY 2024
dc.source.conferencedate2024-06-04
dc.source.conferencelocationSingapore
dc.source.endpage64
dc.source.journalPROCEEDINGS OF THE 15TH ACM INTERNATIONAL CONFERENCE ON FUTURE AND SUSTAINABLE ENERGY SYSTEMS, E-ENERGY 2024
dc.source.numberofpages10
dc.subject.keywordsMANAGEMENT-SYSTEM
dc.subject.keywordsTECHNOLOGIES
dc.subject.keywordsARCHITECTURE
dc.title

Distill2Explain: Differentiable decision trees for explainable reinforcement learning in energy application controllers

dc.typeProceedings paper
dspace.entity.typePublication
imec.internal.crawledAt2025-10-22
imec.internal.sourcecrawler
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