Sketching an open invisible space with multiagent reinforcement learning

Sketching an open invisible space with multiagent 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 Physical Sciences - Article Sketching an open invisible space with multiagent reinforcement learning Hongsheng Chen, Bei Wu, Chao Qian, Zhedong Wang, Pujing Lin, Erping Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4129277/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Controlling electromagnetic (EM) waves at will is fundamentally important for diverse applications, ranging from optical microcavities, super-resolution imaging, to quantum information processing. Decades ago, the forays into metamaterials and transformation optics have ignited?unprecedented?interest to create an invisibility cloak—a closed space with any object inside invisible. However, all features of the scattering waves become stochastic and uncontrollable when EM waves interact with an open and disordered environment, making an open invisible space almost impossible. Counterintuitively, here we for the first time present an open , cluttered , and dynamic but invisible space, wherein any freely-moving object maintains invisible. To adapt to the disordered environment, we randomly organize a swarm of reconfigurable metasurfaces, and master them by MetaSeeker, a population-based reinforcement learning (RL). MetaSeeker constructs a narcissistic internal world to mirror the stochastic physical world, capable of autonomous preferment, evolution, and adaptation. In the perception-decision-execution experiment, multiple RL agents automatically interact with the ever-changing environments and integrate a post-hoc explainability to visualize the decision-making process. The hidden objects, such as vehicle cluster and experimenter, can freely scale, race, and track in the invisible space, with the environmental similarity of 99.5%. Our results constitute a monumental stride to reshape the evolutionary landscape of metasurfaces from individual to swarm intelligence and usher in the remote management of entire EM space. Physical sciences/Optics and photonics Physical sciences/Materials science/Materials for optics/Metamaterials Physical sciences/Optics and photonics/Optical materials and structures/Metamaterials Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryMovieS1Experimentalresultsthatauthenticatetheinvisiblespace.mp4 Experimental results that authenticate the invisible space SupplementaryMovieS2MetaSeekerknowledgerepresentationandresponsetime.mp4 MetaSeeker knowledge representation and response time Cite Share Download PDF Status: Under Review 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4129277","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Physical Sciences - Article","associatedPublications":[],"authors":[{"id":290206702,"identity":"12d8f6e8-e6e9-42ef-a49b-019e120f08ed","order_by":0,"name":"Hongsheng 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Decades ago, the forays into metamaterials and transformation optics have ignited?unprecedented?interest to create an invisibility cloak—a closed space with any object inside invisible. However, all features of the scattering waves become stochastic and uncontrollable when EM waves interact with an open and disordered environment, making an open invisible space almost impossible. Counterintuitively, here we for the first time present an \u003cem\u003eopen\u003c/em\u003e, \u003cem\u003ecluttered\u003c/em\u003e, and \u003cem\u003edynamic \u003c/em\u003ebut invisible space, wherein any freely-moving object maintains invisible. To adapt to the disordered environment, we randomly organize a swarm of reconfigurable metasurfaces, and master them by MetaSeeker, a population-based reinforcement learning (RL). MetaSeeker constructs a narcissistic internal world to mirror the stochastic physical world, capable of autonomous preferment, evolution, and adaptation. 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