{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T16:29:34Z","timestamp":1780763374587,"version":"3.54.1"},"reference-count":60,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT, South Korea","doi-asserted-by":"publisher","award":["2017-0-00096"],"award-info":[{"award-number":["2017-0-00096"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/access.2024.3447056","type":"journal-article","created":{"date-parts":[[2024,8,21]],"date-time":"2024-08-21T23:38:17Z","timestamp":1724283497000},"page":"144982-144991","source":"Crossref","is-referenced-by-count":3,"title":["Multi-Agent Transformer Networks With Graph Attention"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2013-8616","authenticated-orcid":false,"given":"Woobeen","family":"Jin","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Kwangwoon University, Seoul, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7624-2020","authenticated-orcid":false,"given":"Hyukjoon","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Kwangwoon University, Seoul, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Guided deep reinforcement learning for swarm systems","author":"H\u00fcttenrauch","year":"2017","journal-title":"arXiv:1709.06011"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2012.2219061"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1613\/jair.2447"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2016.01.031"},{"key":"ref5","first-page":"5887","article-title":"QTRAN: Learning to factorize with transformation for cooperative multi-agent reinforcement learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Son"},{"key":"ref6","article-title":"Learning nearly decomposable value functions via communication minimization","author":"Wang","year":"2019","journal-title":"arXiv:1910.05366"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-60990-0_12"},{"key":"ref8","article-title":"Rethinking the implementation tricks and monotonicity constraint in cooperative multi-agent reinforcement learning","author":"Hu","year":"2021","journal-title":"arXiv:2102.03479"},{"key":"ref9","article-title":"The StarCraft multi-agent challenge","author":"Samvelyan","year":"2019","journal-title":"arXiv:1902.04043"},{"issue":"1","key":"ref10","first-page":"7234","article-title":"Monotonic value function factorisation for deep multi-agent reinforcement learning","volume":"21","author":"Rashid","year":"2020","journal-title":"J. Mach. Learn. Res."},{"key":"ref11","article-title":"Value-decomposition networks for cooperative multi-agent learning","author":"Sunehag","year":"2017","journal-title":"arXiv:1706.05296"},{"key":"ref12","first-page":"1","article-title":"Maven: Multi-agent variational exploration","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Mahajan"},{"key":"ref13","first-page":"16509","article-title":"Multi-agent reinforcement learning is a sequence modeling problem","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Wen"},{"key":"ref14","first-page":"13458","article-title":"Settling the variance of multi-agent policy gradients","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Kuba"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref16","article-title":"Neural machine translation by jointly learning to align and translate","author":"Bahdanau","year":"2014","journal-title":"arXiv:1409.0473"},{"key":"ref17","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","author":"Devlin","year":"2018","journal-title":"arXiv:1810.04805"},{"key":"ref18","article-title":"Graph attention networks","author":"Veli\u010dkovi\u0107","year":"2017","journal-title":"arXiv:1710.10903"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/B978-1-55860-335-6.50027-1"},{"key":"ref20","volume-title":"Markov Games","volume":"52","author":"Zachrisson","year":"1964"},{"key":"ref21","first-page":"1","article-title":"Multi-agent actor-critic for mixed cooperative-competitive environments","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Lowe"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11794"},{"key":"ref23","article-title":"On the use and misuse of absorbing states in multi-agent reinforcement learning","author":"Cohen","year":"2021","journal-title":"arXiv:2111.05992"},{"key":"ref24","article-title":"Proximal policy optimization algorithms","author":"Schulman","year":"2017","journal-title":"arXiv:1707.06347"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.2005605"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2021.01.001"},{"key":"ref27","article-title":"How powerful are graph neural networks?","author":"Xu","year":"2018","journal-title":"arXiv:1810.00826"},{"key":"ref28","first-page":"1","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Hamilton"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/3363574"},{"key":"ref30","first-page":"4055","article-title":"Image transformer","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Parmar"},{"key":"ref31","article-title":"Axial attention in multidimensional transformers","author":"Ho","year":"2019","journal-title":"arXiv:1912.12180"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/3371158.3371232"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N19-1361"},{"key":"ref34","article-title":"Molecule attention transformer","author":"Maziarka","year":"2020","journal-title":"arXiv:2002.08264"},{"key":"ref35","article-title":"Longformer: The long-document transformer","author":"Beltagy","year":"2020","journal-title":"arXiv:2004.05150"},{"key":"ref36","article-title":"Reformer: The efficient transformer","author":"Kitaev","year":"2020","journal-title":"arXiv:2001.04451"},{"key":"ref37","first-page":"1","article-title":"ViLBERT: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Lu"},{"key":"ref38","article-title":"VisualBERT: A simple and performant baseline for vision and language","author":"Harold Li","year":"2019","journal-title":"arXiv:1908.03557"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i05.6211"},{"key":"ref40","article-title":"Deep multi-agent reinforcement learning with relevance graphs","author":"Malysheva","year":"2018","journal-title":"arXiv:1811.12557"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-87479-9_61"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmsy.2021.08.002"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1111\/mice.12702"},{"key":"ref44","article-title":"Trust region policy optimisation in multi-agent reinforcement learning","author":"Kuba","year":"2021","journal-title":"arXiv:2109.11251"},{"key":"ref45","article-title":"FACMAC: Factored multi-agent centralised policy gradients","author":"Peng","year":"2020","journal-title":"arXiv:2003.06709"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2012.6386109"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2021.3103416"},{"key":"ref48","article-title":"Learning combinatorial optimization algorithms over graphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Khalil"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-020-09938-y"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN52387.2021.9533711"},{"key":"ref51","article-title":"Deep graph library: A graph-centric, highly-performant package for graph neural networks","author":"Wang","year":"2019","journal-title":"arXiv:1909.01315"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1016\/j.icte.2022.11.003"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3215774"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/REDUNDANCY48165.2019.9003345"},{"key":"ref55","article-title":"GRPE: Relative positional encoding for graph transformer","author":"Park","year":"2022","journal-title":"arXiv:2201.12787"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1103\/PRXEnergy.2.043007"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1007\/s13235-021-00420-0"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1103\/PRXEnergy.1.033005"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2023.3296769"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1007\/s10472-023-09860-3"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10380310\/10643073.pdf?arnumber=10643073","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,11]],"date-time":"2024-10-11T04:26:21Z","timestamp":1728620781000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10643073\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":60,"URL":"https:\/\/doi.org\/10.1109\/access.2024.3447056","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}