An Edge Intelligence framework with Reinforcement Learning for Digital Twins in Industrial Metaverse

An Edge Intelligence framework with Reinforcement Learning for Digital Twins in Industrial Metaverse | 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 An Edge Intelligence framework with Reinforcement Learning for Digital Twins in Industrial Metaverse Reza Mohammadvand, Nasser Mozayani, Saeed Khoshkholghi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6876740/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 With the rapid advancement of emerging technologies such as the metaverse and digital twins, the need for effective coordination among communication, computation, and storage in complex systems and edge computing environments has become more crucial than ever. This research presents a novel architecture for an industrial metaverse based on digital twins, which optimizes resources by leveraging mobile edge computing and ultra-reliable low-latency communications. The proposed architecture utilizes task offloading and storage on edge servers to reduce latency and meet the requirements of future metaverse systems in terms of reliability and latency minimization.The proposed method relies on reinforcement learning algorithms, including Deep Q-Network and its advanced variants, including Double Deep Q-Network (DDQN) and Dueling Deep Q-Network (Dueling DQN) to enable intelligent decision-making and adaptability in dynamic conditions. By enhancing adaptability in varying scenarios and making smarter decisions, and according to the obtained simulation results, the proposed method reduces latency by more than 10% on average compared to the best method available in the literature. The results show that this model not only reduces latency and energy consumption, but also enables optimal use of resources. Metaverse Digital twin Reinforcement learning Edge intelligence Ultra-reliable and low-latency communication Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 29 Sep, 2025 Reviews received at journal 27 Sep, 2025 Reviews received at journal 23 Sep, 2025 Reviewers agreed at journal 19 Sep, 2025 Reviewers agreed at journal 15 Sep, 2025 Reviewers agreed at journal 25 Jun, 2025 Reviewers agreed at journal 25 Jun, 2025 Reviewers invited by journal 25 Jun, 2025 Editor assigned by journal 12 Jun, 2025 Submission checks completed at journal 12 Jun, 2025 First submitted to journal 12 Jun, 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. 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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