{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:37:18Z","timestamp":1761176238825,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>Robust adaptation in multiagent settings requires learning not just a single optimal behavior, but a repertoire of high-performing and diverse team behaviors that can succeed under environmental contingencies. Traditional multiagent reinforcement learning methods typically converge to a single specialized team behavior, limiting their adaptability. Recent approaches like Mix-ME promote behavioral diversity but rely solely on evolutionary operators, often resulting in sample-inefficiency and uncoordinated team composition. This work introduces Multiagent Sample-Efficient Quality-Diversity (MASQD), a learning framework that produces an archive of diverse, high-performing multiagent teams. MASQD builds on the Cross-Entropy Method Reinforcement Learning algorithm and extends it to the multiagent setting by representing teams as parameter-shared neural networks, directing exploration from previously discovered behaviors, and guiding refinement through a descriptor-conditioned critic. Through this coupling of anchored exploration and targeted exploitation, MASQD produces functional diversity: teams that are not only behaviorally distinct but also robust and effective under varied conditions. Experiments across four Multiagent MuJoCo tasks show that MASQD outperforms state-of-the-art baselines in both team fitness and functional diversity.<\/jats:p>","DOI":"10.3233\/faia251201","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:54:28Z","timestamp":1761126868000},"source":"Crossref","is-referenced-by-count":0,"title":["Multiagent Quality-Diversity for Effective Adaptation"],"prefix":"10.3233","author":[{"given":"Siddarth","family":"Iyer","sequence":"first","affiliation":[{"name":"Oregon State University"}]},{"given":"Ayhan Alp","family":"Aydeniz","sequence":"additional","affiliation":[{"name":"Oregon State University"}]},{"given":"Gaurav","family":"Dixit","sequence":"additional","affiliation":[{"name":"Oregon State University"}]},{"given":"Kagan","family":"Tumer","sequence":"additional","affiliation":[{"name":"Oregon State University"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251201","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:54:28Z","timestamp":1761126868000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251201"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251201","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}