Optimistic actor-critic reinforcement learning for control of spatially evolving flows

Optimistic actor-critic reinforcement learning for control of spatially evolving flows | 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 Optimistic actor-critic reinforcement learning for control of spatially evolving flows Amine SAIBI, Lionel MATHELIN, Onofrio SEMERARO This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5009875/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Sep, 2025 Read the published version in Flow, Turbulence and Combustion → Version 1 posted 9 You are reading this latest preprint version Abstract Flow control has attracted research for the positive outcomes in engineering systems applications and is finding renewed interest thanks to the rise of data-driven modeling and control algorithms. Interestingly, due to the nonlinearities and the strong convective nature leading to time delays, fluid systems can also serve as challenging test-bed for the further development of control algorithms; indeed, despite the huge potential, the limitations introduced by the plant represent a challenge and the inherent complexity of the dynamics at play requires appropriate strategies during the training process. In this contribution, we consider a reinforcement learning framework and introduce a well-suited actor-critic algorithm to tackle these challenges. The presented algorithm is i) data-parsimonious, ii) leverages optimistic order in the value iteration and iii) is equipped with policy improvement-based error bounds. These features allow a speed-up of the convergence and provide a theoretical-based stopping criterion. As test cases, we consider a linearized version of the Kuramoto-Sivashinsky equation and the control of instabilities in a two-dimensional boundary-layer flow developing over a flat plate, by introducing a minimal number of sensors and actuators. Concerning analogous works that appeared in literature, we show that keeping a rather classical plant is sufficient for guaranteeing adequate performance if the input-output dynamics as well as the observability properties are taken into account. Reinforcement-Learning Flow control Shear flows Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 15 Sep, 2025 Read the published version in Flow, Turbulence and Combustion → Version 1 posted Editorial decision: Revision requested 05 Nov, 2024 Reviews received at journal 05 Nov, 2024 Reviews received at journal 07 Oct, 2024 Reviewers agreed at journal 09 Sep, 2024 Reviewers agreed at journal 08 Sep, 2024 Reviewers invited by journal 06 Sep, 2024 Editor assigned by journal 06 Sep, 2024 Submission checks completed at journal 05 Sep, 2024 First submitted to journal 31 Aug, 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-5009875","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":362945038,"identity":"3f8c384a-03fa-47fd-8d46-8c368ecf397e","order_by":0,"name":"Amine SAIBI","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYFACxgYwxSbB2PgAzGI+QLyWZgMIK4FY2ySAuojSwj/tcNuDDwx18nzSzW3VvDu2yTOw8T7Ab/btxHbDGQyHDdtkDrbd5j1z27CBjd0AvzW3E9ukeRgOMLZJJAK1tN1OYJBvw69DHqTlD0OdPUhLMVgLGxt+LQYgLcCATQRpYSZKiyFQi2SPweFkoF+aJecC/dJGSIvc7fRnEj8q6mznz25/+OHtjtvy/IS0QJ0HpUHRSpQGBICmhFEwCkbBKBgFKAAACENAhdNZUKAAAAAASUVORK5CYII=","orcid":"","institution":"Institut Jean Le Rond d'Alembert","correspondingAuthor":true,"prefix":"","firstName":"Amine","middleName":"","lastName":"SAIBI","suffix":""},{"id":362945039,"identity":"c161aaae-a26b-454a-864f-65e580761ad3","order_by":1,"name":"Lionel MATHELIN","email":"","orcid":"","institution":"Laboratoire Interdisciplinaire des Sciences du Numérique","correspondingAuthor":false,"prefix":"","firstName":"Lionel","middleName":"","lastName":"MATHELIN","suffix":""},{"id":362945040,"identity":"f48d6980-b6d6-40ec-89a0-70a6ccd91ef6","order_by":2,"name":"Onofrio SEMERARO","email":"","orcid":"","institution":"Laboratoire Interdisciplinaire des Sciences du Numérique","correspondingAuthor":false,"prefix":"","firstName":"Onofrio","middleName":"","lastName":"SEMERARO","suffix":""}],"badges":[],"createdAt":"2024-08-31 16:14:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5009875/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5009875/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10494-025-00697-w","type":"published","date":"2025-09-15T15:57:10+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":91890037,"identity":"0576ca36-deec-47f7-b216-6bc425ab9a49","added_by":"auto","created_at":"2025-09-22 16:03:49","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2213660,"visible":true,"origin":"","legend":"","description":"","filename":"FTaCSIG54AmineSaibi.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5009875/v1_covered_ed53e7d5-8b5f-4841-b5fc-2676c2dbc735.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimistic actor-critic reinforcement learning for control of spatially evolving flows","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":"flow-turbulence-and-combustion","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"appl","sideBox":"Learn more about [Flow, Turbulence and Combustion](http://link.springer.com/journal/10494)","snPcode":"10494","submissionUrl":"https://submission.nature.com/new-submission/10494/3","title":"Flow, Turbulence and Combustion","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Reinforcement-Learning, Flow control, Shear flows","lastPublishedDoi":"10.21203/rs.3.rs-5009875/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5009875/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Flow control has attracted research for the positive outcomes in engineering systems applications and is finding renewed interest thanks to the rise of data-driven modeling and control algorithms. 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