Computational task transfer scheme based on multi-agent generative adversarial imitation learning

Computational task transfer scheme based on multi-agent generative adversarial imitation 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 Computational task transfer scheme based on multi-agent generative adversarial imitation learning Haojing Huang, Jiajun Li, Fei Lu, Jianxin Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4819175/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 The multi-agent computing task scheduling problem can be transformed into a task assignment optimization problem under the premise of minimum system cost and maximum sample utilization. For this problem, this paper proposes a computational task migration scheme based on multi-agent body generation adversarial imitation learning. In the battlefield, this scheme uses the idle computing power of tanks, fighting vehicles, drones or infantry equipment to form a Ad Hoc cloud. Based on the characteristics of distributed training of reinforcement learning algorithm, it makes full use of equipment resources to solve the transfer scheme of computing tasks. By using behavioral cloning to construct the data set and build the initial strategy model, the reward function is simplified, and the network structure, parameters and training hyperparameters are set. Driven by the training set, the generative adversarial inverse reinforcement learning method is used for network training. The simulation results show that the MAGAIL-MCT algorithm can mimic the expert trajectory behavior and successfully train the neural network. The experimental results prove that the scheme is improved in reducing system overhead, improving sample utilization, reducing migration delay, migration energy consumption and reducing the impact of mobility. multi-agent generative confrontation imitation learning task transfer Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 30 Aug, 2024 Reviews received at journal 27 Aug, 2024 Reviewers agreed at journal 10 Aug, 2024 Reviewers agreed at journal 07 Aug, 2024 Reviewers agreed at journal 07 Aug, 2024 Reviews received at journal 06 Aug, 2024 Reviewers agreed at journal 06 Aug, 2024 Reviewers invited by journal 06 Aug, 2024 Editor assigned by journal 06 Aug, 2024 Submission checks completed at journal 06 Aug, 2024 First submitted to journal 29 Jul, 2024 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. 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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-4819175","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":347438917,"identity":"b932552f-70ef-490f-9939-33a7d695fbd2","order_by":0,"name":"Haojing Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYBACfmb+jw8+VNTIEa9Fsr3B2HDGmWPGxGsx6DlgJs3bxpzYQLwWiYQEaR42tvS+4wmMHz7mEKHFXCLhgOEcHpncmWceMEvO3EaEFssZiQ0JbyTYcjfcSGBj5iVGi8GNZIYDPAbM6QbEazlzjLGRJ4E5gXgtku09zIwzDhwznHnmYTNxfuFn5mH/8fFfjTzf8eSDHz4SowUBDpAQNTAtCaTqGAWjYBSMgpECAMbiOnE311c2AAAAAElFTkSuQmCC","orcid":"","institution":"Open University of Guangdong (Guandong Polytechnic Institute)","correspondingAuthor":true,"prefix":"","firstName":"Haojing","middleName":"","lastName":"Huang","suffix":""},{"id":347438918,"identity":"5b9f2d65-a4c1-413e-8717-ccffbaa05da0","order_by":1,"name":"Jiajun Li","email":"","orcid":"","institution":"Dongguan Polytechnic","correspondingAuthor":false,"prefix":"","firstName":"Jiajun","middleName":"","lastName":"Li","suffix":""},{"id":347438919,"identity":"9b4628c0-aad1-4a27-a0a0-b4032e6f070d","order_by":2,"name":"Fei Lu","email":"","orcid":"","institution":"Open University of Guangdong (Guandong Polytechnic Institute)","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Lu","suffix":""},{"id":347438921,"identity":"32f1c620-329a-472c-b0a4-ed0ad5717faa","order_by":3,"name":"Jianxin Li","email":"","orcid":"","institution":"Dongguan Polytechnic","correspondingAuthor":false,"prefix":"","firstName":"Jianxin","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-07-29 05:18:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4819175/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4819175/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63750883,"identity":"ff20ab3e-1938-4674-bf07-06f1fe3924d2","added_by":"auto","created_at":"2024-09-02 03:41:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":425450,"visible":true,"origin":"","legend":"","description":"","filename":"Computationaltasktransferschemebasedonmultiagentgenerativeadversarialimitationlearning.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4819175/v1_covered_a5751559-d180-4a0f-8e17-79aacac5639a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Computational task transfer scheme based on multi-agent generative adversarial imitation learning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-journal-of-supercomputing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Journal of Supercomputing](https://www.springer.com/journal/11227)","snPcode":"11227","submissionUrl":"https://submission.nature.com/new-submission/11227/3","title":"The Journal of Supercomputing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"multi-agent, generative confrontation, imitation learning, task transfer","lastPublishedDoi":"10.21203/rs.3.rs-4819175/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4819175/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe multi-agent computing task scheduling problem can be transformed into a task assignment optimization problem under the premise of minimum system cost and maximum sample utilization. For this problem, this paper proposes a computational task migration scheme based on multi-agent body generation adversarial imitation learning. In the battlefield, this scheme uses the idle computing power of tanks, fighting vehicles, drones or infantry equipment to form a Ad Hoc cloud. Based on the characteristics of distributed training of reinforcement learning algorithm, it makes full use of equipment resources to solve the transfer scheme of computing tasks. By using behavioral cloning to construct the data set and build the initial strategy model, the reward function is simplified, and the network structure, parameters and training hyperparameters are set. Driven by the training set, the generative adversarial inverse reinforcement learning method is used for network training. The simulation results show that the MAGAIL-MCT algorithm can mimic the expert trajectory behavior and successfully train the neural network. 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