A Comparative Analysis of Reinforcement Learning Methods for UAV Autonomous Landing

A Comparative Analysis of Reinforcement Learning Methods for UAV Autonomous Landing | 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 A Comparative Analysis of Reinforcement Learning Methods for UAV Autonomous Landing Luís M. Branco, Francisco S. Neves, Andry M. Pinto This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7868510/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Autonomous landing of Unmanned Aerial Vehicles (UAVs) is a sequential decision-making task in which an agent must autonomously select a sequence of actions to guide a vehicle safely onto a landing platform. This paper presents a comparative analysis of different reinforcement learning algorithms applied to the UAV landing problem, including Deep Q-Network (DQN), Soft Actor Critic (SAC) and Actor–Critic with Experience Replay (ACER). The landing task was formulated with a compact state representation and a discrete action state. A two-zone reward shaping scheme was developed to encourage horizontal convergence during approach and prioritized vertical descent in the landing region. All algorithms were trained and evaluated in simulation. The results show that SAC and ACER converge faster than DQN during training. In the testing results, DQN and SAC achieved 100% task success with an average landing deviation of 0.086\,m and 0.150\,m, respectively, while ACER reached 97% success with an average landing deviation of 0.141\,m. DQN had the smallest average landing error, SAC obtained the lowest average episode length and ACER exhibited the lowest crash termination during training. A comprehensive analysis of these results indicates a trade-off between precision and sample efficiency. Deep Learning Reinforcement Learning Unmanned Aerial Vehicle Aerial Robotics Autonomous Landing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 04 Mar, 2026 Reviews received at journal 20 Jan, 2026 Reviewers agreed at journal 19 Jan, 2026 Reviewers agreed at journal 18 Jan, 2026 Reviews received at journal 23 Dec, 2025 Reviewers agreed at journal 04 Dec, 2025 Reviewers invited by journal 03 Dec, 2025 Editor assigned by journal 16 Oct, 2025 Submission checks completed at journal 16 Oct, 2025 First submitted to journal 15 Oct, 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. 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-7868510","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":555138058,"identity":"dce97c7b-36e4-41e5-b6fb-90274a29a5d7","order_by":0,"name":"Luís M. 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Landing","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"journal-of-intelligent-and-robotic-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of Intelligent \u0026 Robotic Systems](https://link.springer.com/journal/10846)","snPcode":"10846","submissionUrl":"https://submission.springernature.com/new-submission/10846/3","title":"Journal of Intelligent \u0026 Robotic Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Deep Learning, Reinforcement Learning, Unmanned Aerial Vehicle, Aerial Robotics, Autonomous Landing","lastPublishedDoi":"10.21203/rs.3.rs-7868510/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7868510/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAutonomous landing of Unmanned Aerial Vehicles (UAVs) is a sequential decision-making task in which an agent must autonomously select a sequence of actions to guide a vehicle safely onto a landing platform. This paper presents a comparative analysis of different reinforcement learning algorithms applied to the UAV landing problem, including Deep Q-Network (DQN), Soft Actor Critic (SAC) and Actor\u0026ndash;Critic with Experience Replay (ACER). The landing task was formulated with a compact state representation and a discrete action state. A two-zone reward shaping scheme was developed to encourage horizontal convergence during approach and prioritized vertical descent in the landing region. All algorithms were trained and evaluated in simulation. The results show that SAC and ACER converge faster than DQN during training. In the testing results, DQN and SAC achieved 100% task success with an average landing deviation of 0.086\\,m and 0.150\\,m, respectively, while ACER reached 97% success with an average landing deviation of 0.141\\,m. DQN had the smallest average landing error, SAC obtained the lowest average episode length and ACER exhibited the lowest crash termination during training. A comprehensive analysis of these results indicates a trade-off between precision and sample efficiency.\u003c/p\u003e","manuscriptTitle":"A Comparative Analysis of Reinforcement Learning Methods for UAV Autonomous Landing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-05 13:08:45","doi":"10.21203/rs.3.rs-7868510/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-04T23:35:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-20T10:54:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"252855805338816738150194185092057951622","date":"2026-01-19T11:04:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"272042318900215661175664113755279734499","date":"2026-01-18T09:31:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-23T22:33:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"138712423607005410534095402959788258420","date":"2025-12-04T09:21:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-03T22:21:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-16T15:43:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-16T10:56:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Intelligent \u0026 Robotic Systems","date":"2025-10-15T13:05:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-intelligent-and-robotic-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of Intelligent \u0026 Robotic Systems](https://link.springer.com/journal/10846)","snPcode":"10846","submissionUrl":"https://submission.springernature.com/new-submission/10846/3","title":"Journal of Intelligent \u0026 Robotic Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7fb99238-770b-4a56-9874-0821f0817fe2","owner":[],"postedDate":"December 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-25T16:23:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-05 13:08:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7868510","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7868510","identity":"rs-7868510","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}