Adaptive Average Arterial Pressure Control by Multi-Agent On-Policy Reinforcement Learning

Adaptive Average Arterial Pressure Control by Multi-Agent On-Policy 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 Article Adaptive Average Arterial Pressure Control by Multi-Agent On-Policy Reinforcement Learning Xiaofeng Hong, Walid Ayadi, Khalid A. Alattas, Ardashir Mohammadzadeh, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4930194/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract The current research introduces a model-free ultra-local model (MFULM) controller that utilizes the multi-agent on-policy reinforcement learning (MAOPRL) technique for remotely regulating blood pressure through precise drug dosing in a closed-loop system. Within the closed-loop system, there exists a MFULM controller, an observer, and an intelligent MAOPRL algorithm. Initially, a flexible MFULM controller is created to make adjustments to blood pressure and medication dosages. Following this, an observer is incorporated into the main controller to improve performance and stability by estimating states and disturbances. The controller parameters are optimized using MAOPRL in an adaptive manner, which involves the use of an actor-critic approach in an adaptive fashion. This approach enhances the adaptability of the controller by allowing for dynamic modifications to dosage and blood pressure control parameters. In the presence of disturbances or instabilities, the critic's feedback aids the actor in adjusting actions to reduce their impact, utilizing a complementary strategy to tackle deficiencies in the primary controller. Lastly, various evaluations, including assessments under normal conditions, adaptability between patients, and stability evaluations against mixed disturbances, have been carried out to confirm the efficiency and viability of the proposed method. Blood Pressure (BP) Average arterial pressure (AAP) Drug delivery Model-free ultra-local model (MFULM) Multi-agent on-policy reinforcement learning (MAOPRL) Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 03 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 03 Nov, 2024 Reviews received at journal 22 Oct, 2024 Reviews received at journal 01 Oct, 2024 Reviewers agreed at journal 21 Sep, 2024 Reviewers agreed at journal 21 Sep, 2024 Reviewers invited by journal 16 Sep, 2024 Editor assigned by journal 16 Sep, 2024 Editor invited by journal 09 Sep, 2024 Submission checks completed at journal 05 Sep, 2024 First submitted to journal 17 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. 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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-4930194","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":361077386,"identity":"27bc92e0-eb76-4bdc-9750-7b7a533dcac1","order_by":0,"name":"Xiaofeng Hong","email":"","orcid":"","institution":"Zhejiang Guangsha Vocational and Technical University of Construction","correspondingAuthor":false,"prefix":"","firstName":"Xiaofeng","middleName":"","lastName":"Hong","suffix":""},{"id":361077387,"identity":"d3e514e1-4f81-498d-a523-d3e53f62cbcc","order_by":1,"name":"Walid Ayadi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYJACiQQgwcfMYPgASPPwEa2FjZnB2ACkhY0oLSACqNIMxsAP+GckP7zxgOGwPBs787bKrzl2MmwMzA8f3cBnw400Y4sEhsOGbcxsZbdltyUDHcZmbJyDz5obCWZAv9xmbGPmMbstuY0ZqIWHTRqfFvkb6d9AWuxBWoolt9UT1mJwIwdsSyJIC+PHbYcJazE886bYIsHgfzLQL8XSjNuO87AxE/CL3PH0jTd/VKTZ9vMf3vjx57Zqe3725oeP8XpfIAHkPAibmQdM4lMOAvwHEGzGH4RUj4JRMApGwYgEAFS8QDxLn85JAAAAAElFTkSuQmCC","orcid":"","institution":"Abu Dhabi Polytechnic","correspondingAuthor":true,"prefix":"","firstName":"Walid","middleName":"","lastName":"Ayadi","suffix":""},{"id":361077388,"identity":"52f6ecbc-3c79-497f-9a63-28cbe614b8ab","order_by":2,"name":"Khalid A. 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