From Individual Decisions to Team Emergence: A Survey on Explainable Cooperative Multi-Agent Reinforcement Learning

From Individual Decisions to Team Emergence: A Survey on Explainable Cooperative Multi-Agent Reinforcement Learning | 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 Research Article From Individual Decisions to Team Emergence: A Survey on Explainable Cooperative Multi-Agent Reinforcement Learning Lei Sheng, Xiliang Chen, Zhiqiang Pan, Fei Cai, Honghui Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7965328/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Multi-Agent Reinforcement Learning (MARL) holds significant promise for cooperative decision-making, yet its reliance on deep neural networks (DNNs) creates ''black-box'' characteristics that impede trustworthy deployment in high-stakes scenarios. This lack of transparency complicates tracing decision logic and raises concerns about safety and accountability. This survey systematically reviews Explainable MARL (XMARL) for cooperative settings, deconstructing the decision-making chain from individual agent policies to collective team behavior. To address the absence of a unified framework, we introduce a novel multi-level taxonomy encompassing microscopic individual behavior, interaction mechanisms, team strategy emergence, and system-level performance. We organize core explanatory questions and technical approaches within this structure, summarize the principles and limitations of representative methods, and critically discuss key challenges such as evaluation standards, causal reasoning integration, and deployment adaptability. Our goal is to provide both theoretical foundation and technical guidance for building transparent, trustworthy, and verifiably cooperative multi-agent systems (MASs). Multi-Agent Reinforcement Learning (MARL) Explainable Artificial Intelligence (XAI) Collaborative Autonomous Systems Explainable Reinforcement Learning (XRL) Trustworthy AI Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 09 Dec, 2025 Reviews received at journal 09 Dec, 2025 Reviews received at journal 02 Dec, 2025 Reviews received at journal 01 Dec, 2025 Reviewers agreed at journal 20 Nov, 2025 Reviewers agreed at journal 17 Nov, 2025 Reviewers agreed at journal 17 Nov, 2025 Reviewers invited by journal 04 Nov, 2025 Editor assigned by journal 03 Nov, 2025 Submission checks completed at journal 28 Oct, 2025 First submitted to journal 27 Oct, 2025 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. 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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-7965328","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":542517797,"identity":"cc626a43-8ca2-4189-8d78-e04236fa922a","order_by":0,"name":"Lei Sheng","email":"","orcid":"","institution":"National University of Defense Technology","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Sheng","suffix":""},{"id":542517799,"identity":"cfa0a630-ac2f-4540-94b9-39602f6ec5d8","order_by":1,"name":"Xiliang Chen","email":"","orcid":"","institution":"Army Engineering 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