Adaptive Human-Computer Interaction Strategies Through Reinforcement Learning in Complex Scenarios

Adaptive Human-Computer Interaction Strategies Through Reinforcement Learning in Complex Scenarios | 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 Adaptive Human-Computer Interaction Strategies Through Reinforcement Learning in Complex Scenarios Rui Liu, Yifan Zhuang, Runsheng Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7821187/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 This study addresses the challenges of dynamics and complexity in intelligent human-computer interaction and proposes a reinforcement learning-based optimization framework to improve long-term returns and overall experience. Human-computer interaction is modeled as a Markov decision process, with state space, action space, reward function, and discount factor defined to capture the dynamics of user input, system feedback, and interaction environment. The method combines policy function, value function, and advantage function, updates parameters through policy gradient, and continuously adjusts during interaction to balance immediate feedback and long-term benefits. To validate the framework, multimodal dialog and scene-aware datasets are used as the experimental platform, with multiple sensitivity experiments conducted on key factors such as discount factor, exploration rate decay, environmental noise, and data imbalance. Evaluation is carried out using cumulative reward, average episode reward, convergence speed, and task success rate. Results show that the proposed method outperforms existing approaches across several metrics, achieving higher task completion while maintaining strategy stability. Comparative experiments further confirm its advantages in interaction efficiency and long-term return, demonstrating the significant value of reinforcement learning in optimizing human-computer interaction. reinforcement learning human-computer interaction strategy optimization interactive experience Full Text Additional Declarations The authors declare no competing interests. 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. 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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-7821187","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":527335063,"identity":"0f4894ae-6c64-4886-9a80-6ab8e6365abc","order_by":0,"name":"Rui Liu","email":"","orcid":"","institution":"University of Melbourne","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Liu","suffix":""},{"id":527335064,"identity":"ab0e73d2-2448-4010-b465-e9a6922d724c","order_by":1,"name":"Yifan Zhuang","email":"","orcid":"","institution":"University of Southern California","correspondingAuthor":false,"prefix":"","firstName":"Yifan","middleName":"","lastName":"Zhuang","suffix":""},{"id":527335065,"identity":"8c439a2d-cf2e-4ed1-9f18-a41d76bb2440","order_by":2,"name":"Runsheng Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYFADCRBRAcTMzA2kaDkD0sJIihbGNhCLgBbzGcnPHjDuOZwnP7v52MOv82qj+duBWn5UbMOpReZGmrkBw7PDxQZ3jqUby247njvjMGMDY8+Z27idI5FgJsFw4HDiBokcM2nJbcdyG4BamBnb8GlJ/wbWMn9G/jdpyTnHcucT1pIDsaXhRg6b5MeGmtwNBLXwvCmTSDiQnrjhRpqZNMOxA7kbgVoO4vULe/o2iQ8HrIEOS34m+aOmLnfe+cMHH/yowK2FQSCBgSEBymbmYTgMZhzArR4I+JGkGX8w1OFVPApGwSgYBSMTAABtwly0R+E/CQAAAABJRU5ErkJggg==","orcid":"","institution":"University of Southern California","correspondingAuthor":true,"prefix":"","firstName":"Runsheng","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-10-09 23:23:12","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7821187/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7821187/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93383851,"identity":"db22b071-fb06-4bd1-a819-f7e61f772db4","added_by":"auto","created_at":"2025-10-13 09:16:07","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":444547,"visible":true,"origin":"","legend":"","description":"","filename":"AdaptiveHumanComputerInteractionStrategiesThroughReinforcementLearninginComplexScenarios.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7821187/v1_covered_0d021f88-5f0e-4e90-aa13-9d3bcf285726.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eAdaptive Human-Computer Interaction Strategies Through Reinforcement Learning in Complex Scenarios\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"reinforcement learning, human-computer interaction, strategy optimization, interactive experience","lastPublishedDoi":"10.21203/rs.3.rs-7821187/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7821187/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eThis study addresses the challenges of dynamics and complexity in intelligent human-computer interaction and proposes a reinforcement learning-based optimization framework to improve long-term returns and overall experience. 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