{"id":42983,"date":"2026-03-25T16:47:20","date_gmt":"2026-03-25T15:47:20","guid":{"rendered":"https:\/\/www.l3s.de\/?p=42983"},"modified":"2026-03-25T16:47:24","modified_gmt":"2026-03-25T15:47:24","slug":"reinforcement-learning-boosts-routing-performance-in-the-bitcoin-lightning-network","status":"publish","type":"post","link":"https:\/\/www.l3s.de\/de\/reinforcement-learning-boosts-routing-performance-in-the-bitcoin-lightning-network\/","title":{"rendered":"Reinforcement Learning verbessert Routing im Bitcoin Lightning Network\u00a0"},"content":{"rendered":"<p><strong>L3S Beste Ver\u00f6ffentlichung des Quartals (Q3+Q4\/2025)<\/strong>\u00a0<br><strong>Category:\u00a0Green\u00a0AI<\/strong>\u00a0<\/p>\n\n\n\n<p style=\"font-size:23px\"><strong>Hybrid pathfinding optimization for the Lightning Network with Reinforcement Learning<\/strong>&nbsp;<\/p>\n\n\n\n<p>Autoren: D. Valko, D.\u00a0Kudenko&nbsp;<\/p>\n\n\n\n<p>Erschienen in&nbsp;<a href=\"https:\/\/engineering%20applications%20of%20artificial%20intelligence\/\" target=\"_blank\" rel=\"noreferrer noopener\"><em>Engineering Applications of Artificial Intelligence<\/em><\/a>&nbsp;<\/p>\n\n\n\n<div style=\"height:23px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p><strong>Das Paper in K\u00fcrze:<\/strong>&nbsp;<\/p>\n\n\n\n<p>Das Bitcoin Lightning Network erm\u00f6glicht schnelle und kosteng\u00fcnstige Transaktionen. Dennoch k\u00f6nnen Zahlungen scheitern, weil das Netzwerk nicht immer zuverl\u00e4ssige Routen findet. In unserem neuen Paper verbinden wir Reinforcement Learning (RL) mit bestehenden Routing-Algorithmen, um dynamisch bessere Zahlungswege auszuw\u00e4hlen. Tests mit einem realen Netzwerk-Snapshot zeigen, dass dieser hybride Ansatz die Erfolgsquote von Zahlungen unter anspruchsvollen Netzwerkbedingungen um rund 10\u202f Prozent erh\u00f6hen kann. Gleichzeitig bleibt das System effizient und vermeidet unn\u00f6tig lange Routing-Pfade, die die Infrastruktur belasten w\u00fcrden.&nbsp;<\/p>\n\n\n\n<p>Unsere Ergebnisse verdeutlichen, dass RL das Potenzial hat, die Zuverl\u00e4ssigkeit und Skalierbarkeit digitaler Zahlungssysteme der n\u00e4chsten Generation deutlich zu verbessern \u2013 und das ohne \u00c4nderungen an der zugrunde liegenden Architektur.&nbsp;<\/p>\n\n\n\n<figure class=\"wp-block-image size-large is-resized\"><img decoding=\"async\" width=\"1024\" height=\"768\" data-src=\"https:\/\/www.l3s.de\/wp-content\/uploads\/2026\/03\/Kudenko-Hybrid-pathfinding-1024x768.jpg\" alt=\"\" class=\"wp-image-42984 lazyload\" style=\"--smush-placeholder-width: 1024px; --smush-placeholder-aspect-ratio: 1024\/768;width:565px\" data-srcset=\"https:\/\/www.l3s.de\/wp-content\/uploads\/2026\/03\/Kudenko-Hybrid-pathfinding-1024x768.jpg 1024w, https:\/\/www.l3s.de\/wp-content\/uploads\/2026\/03\/Kudenko-Hybrid-pathfinding-300x225.jpg 300w, https:\/\/www.l3s.de\/wp-content\/uploads\/2026\/03\/Kudenko-Hybrid-pathfinding-768x576.jpg 768w, https:\/\/www.l3s.de\/wp-content\/uploads\/2026\/03\/Kudenko-Hybrid-pathfinding-1536x1152.jpg 1536w, https:\/\/www.l3s.de\/wp-content\/uploads\/2026\/03\/Kudenko-Hybrid-pathfinding-2048x1536.jpg 2048w, https:\/\/www.l3s.de\/wp-content\/uploads\/2026\/03\/Kudenko-Hybrid-pathfinding-16x12.jpg 16w\" data-sizes=\"(max-width: 1024px) 100vw, 1024px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" \/><\/figure>\n\n\n\n<p><strong>Link zum Paper:<\/strong>:\u202f&nbsp;<a href=\"https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0952197625002258\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0952197625002258<\/a>&nbsp;<\/p>","protected":false},"excerpt":{"rendered":"<p>Durch die Kombination von Reinforcement Learning mit bestehenden Routing-Algorithmen lassen sich Zahlungserfolgsraten im Lightning Network deutlich steigern. <\/p>","protected":false},"author":11,"featured_media":42977,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[10],"tags":[],"class_list":["post-42983","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news"],"acf":[],"aioseo_notices":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/www.l3s.de\/de\/wp-json\/wp\/v2\/posts\/42983","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.l3s.de\/de\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.l3s.de\/de\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.l3s.de\/de\/wp-json\/wp\/v2\/users\/11"}],"replies":[{"embeddable":true,"href":"https:\/\/www.l3s.de\/de\/wp-json\/wp\/v2\/comments?post=42983"}],"version-history":[{"count":1,"href":"https:\/\/www.l3s.de\/de\/wp-json\/wp\/v2\/posts\/42983\/revisions"}],"predecessor-version":[{"id":42985,"href":"https:\/\/www.l3s.de\/de\/wp-json\/wp\/v2\/posts\/42983\/revisions\/42985"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.l3s.de\/de\/wp-json\/wp\/v2\/media\/42977"}],"wp:attachment":[{"href":"https:\/\/www.l3s.de\/de\/wp-json\/wp\/v2\/media?parent=42983"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.l3s.de\/de\/wp-json\/wp\/v2\/categories?post=42983"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.l3s.de\/de\/wp-json\/wp\/v2\/tags?post=42983"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}