dc.contributor.author | Li, Zhuo | |
dc.contributor.author | Zhou, Xu | |
dc.contributor.author | De Turck, Filip | |
dc.contributor.author | Li, Taixin | |
dc.contributor.author | Ren, Yongmao | |
dc.contributor.author | Qin, Yifang | |
dc.date.accessioned | 2022-08-29T10:00:39Z | |
dc.date.available | 2022-07-04T02:26:40Z | |
dc.date.available | 2022-07-05T09:29:31Z | |
dc.date.available | 2022-08-29T10:00:39Z | |
dc.date.issued | 2022 | |
dc.identifier.issn | 1530-8669 | |
dc.identifier.other | WOS:000814570700002 | |
dc.identifier.uri | https://imec-publications.be/handle/20.500.12860/40058.3 | |
dc.source | WOS | |
dc.title | Feudal Multiagent Reinforcement Learning for Interdomain Collaborative Routing Optimization | |
dc.type | Journal article | |
dc.contributor.imecauthor | Li, Zhuo | |
dc.contributor.imecauthor | De Turck, Filip | |
dc.contributor.orcidimec | De Turck, Filip::0000-0003-4824-1199 | |
dc.identifier.doi | 10.1155/2022/1231979 | |
dc.source.numberofpages | 11 | |
dc.source.peerreview | yes | |
dc.source.beginpage | ID1231979 | |
dc.source.journal | WIRELESS COMMUNICATIONS & MOBILE COMPUTING | |
dc.source.issue | / | |
dc.source.volume | 2022 | |
imec.availability | Published - open access | |
dc.description.wosFundingText | This work was supported by the National Key R&D Program of China (Grant No. 2018YFB1800100); the National Natural Science Foundation of China (Grant No. U1909204); the Beijing Natural Science Foundation, China (Grant No. 4202082); and the Open Research Projects of Zhejiang Lab (Grant No. 2021LC0AB03). | |