Performance Optimization of Multi-Agent CooperativeAlgorithms in Basketball Offensive and DefensiveTactics Simulation

Performance Optimization of Multi-Agent CooperativeAlgorithms in Basketball Offensive and DefensiveTactics Simulation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Performance Optimization of Multi-Agent CooperativeAlgorithms in Basketball Offensive and DefensiveTactics Simulation Ying Ji This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7144053/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The evolution of intelligent systems has spotlighted the significance of cooperative behavior among autonomous agents,particularly in dynamic environments where strategic interactions and real-time decision-making are crucial. In line with thescope of Frontiers in Computer Science, which emphasizes intelligent systems, artificial intelligence, and distributed computing,this study investigates advanced coordination strategies in multi-agent systems, aiming to optimize collective performance inreal-time tactical environments. Traditional models of multi-agent cooperation—be they centralized or decentralized—facechallenges in scalability, adaptability, and real-time communication under constrained conditions. Centralized models, whileoptimal in action planning, often falter under large-scale agent scenarios due to bottlenecks in processing and communication.Decentralized approaches, although more scalable, struggle with policy alignment and information asymmetry, leadingto suboptimal global behavior. A novel optimization framework, termed Hierarchical Policy Synchronization (HiPS), isproposed and incorporated into the Cooperative Decision-Making Network (CDMN) to tackle these limitations. This approachintroduces hierarchical decomposition, where agents are organized into coordination groups with synchronization protocols.Through local policy aggregation, inter-group meta-policy alignment, and a reinforcement learning objective regularized byfeedback consistency, the model achieves high scalability, robustness to agent failure, and efficient coordination in uncertainenvironments. Empirical evaluations validate that HiPS significantly improves convergence speed and cooperative efficacycompared to conventional decentralized strategies. By integrating adaptive communication and coordination-aware learning,our method aligns with the journal’s focus on intelligent algorithms and real-world applications of cooperative AI. Physical sciences/Mathematics and computing Physical sciences/Physics Multi-Agent Systems Cooperative Decision-Making Hierarchical Policy Synchronization Adaptive Coordination Reinforcement Learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7144053","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":494542406,"identity":"3bf577c0-04aa-4d3c-82ab-6afdfa3a2702","order_by":0,"name":"Ying Ji","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYHAD5mNgio2dgDoeBJMtjYEhAUgxE6+FxwyshYGQFnv2M8YfPu64k7id/cy3Bx9/bJPnY2Zg/PAxB48tPDkGhjPPPEvc2ZO73XBGwm3DNmYGZsmZ2/A5LMcgmbftcOKGA7nbpHkSbjMCtbAx8+LTwv/G4DBYy/k3z0Ba7AlrkcgxbAZruZHDBtKSSFjLjWfFjDPbDhtvuPHMTHJG2u3kNmbGZrx+Ye9P3vzhY9th2Q3nk59JfLC5bTu/vfngh494tDAwcBigizA24FMPsucBAQWjYBSMglEw4gEAhvlQr285A4AAAAAASUVORK5CYII=","orcid":"","institution":"Hangzhou Dianzi University","correspondingAuthor":true,"prefix":"","firstName":"Ying","middleName":"","lastName":"Ji","suffix":""}],"badges":[],"createdAt":"2025-07-17 02:08:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7144053/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7144053/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90790975,"identity":"07ee7ca7-1b47-4170-88de-b8625a06234c","added_by":"auto","created_at":"2025-09-08 08:18:04","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2358597,"visible":true,"origin":"","legend":"","description":"","filename":"ScientificReports.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7144053/v1_covered_9a390351-cc26-4ec9-997b-6cd2da9e0dce.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Performance Optimization of Multi-Agent CooperativeAlgorithms in Basketball Offensive and DefensiveTactics Simulation","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Multi-Agent Systems, Cooperative Decision-Making, Hierarchical Policy Synchronization, Adaptive Coordination, Reinforcement Learning","lastPublishedDoi":"10.21203/rs.3.rs-7144053/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7144053/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The evolution of intelligent systems has spotlighted the significance of cooperative behavior among autonomous agents,particularly in dynamic environments where strategic interactions and real-time decision-making are crucial. In line with thescope of Frontiers in Computer Science, which emphasizes intelligent systems, artificial intelligence, and distributed computing,this study investigates advanced coordination strategies in multi-agent systems, aiming to optimize collective performance inreal-time tactical environments. Traditional models of multi-agent cooperation—be they centralized or decentralized—facechallenges in scalability, adaptability, and real-time communication under constrained conditions. Centralized models, whileoptimal in action planning, often falter under large-scale agent scenarios due to bottlenecks in processing and communication.Decentralized approaches, although more scalable, struggle with policy alignment and information asymmetry, leadingto suboptimal global behavior. A novel optimization framework, termed Hierarchical Policy Synchronization (HiPS), isproposed and incorporated into the Cooperative Decision-Making Network (CDMN) to tackle these limitations. This approachintroduces hierarchical decomposition, where agents are organized into coordination groups with synchronization protocols.Through local policy aggregation, inter-group meta-policy alignment, and a reinforcement learning objective regularized byfeedback consistency, the model achieves high scalability, robustness to agent failure, and efficient coordination in uncertainenvironments. Empirical evaluations validate that HiPS significantly improves convergence speed and cooperative efficacycompared to conventional decentralized strategies. 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