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Building on data-driven automation and reinforcement learning trends, this work presents ROAR, an architectural approach that places deep reinforcement learning inside P4 programmable switches. Network devices function as independent agents in a multi-agent RL setting and learn cooperative behavior to reduce packet queuing time. Experiments show throughput and delay improvements as offered traffic increases versus traditional approaches.","POLITECNICO DI TORINO Repository ISTITUZIONALE  \nROAR: Routing Packets in P4 Switches With Multi-Agent Decisions Logic  \nOriginal  \nROAR: Routing Packets in P4 Switches With Multi-Agent Decisions Logic / Angi, Antonino; Sacco, Alessio; Esposito, Flavio; Marchetto, Guido. -ELETTRONICO. - (2024), pp. 63-68. (Intervento presentato al convegno 2024 IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN) tenutosi a Stockholm (SE) nel 05-08 May 2024) [10 . 1109/icmlcn59089 .2024. 10625142] .  \nAvailability:  \nThis version is available at: 11583/2991799 since: 2024-08-20T12:49:19Z  \nPublisher: IEEE  \nPublished  \nDOI:10.1109/icmlcn59089.2024.10625142  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \nIEEE postprint/Author's Accepted Manuscript  \n©2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collecting works, for resale or lists, or reuse of any copyrighted component of this work in other works.  \n(Article begins on next page)  \n18 September 2024  \nROAR: Routing Packets in P4 Switches With Multi-Agent Decisions Logic  \nAntonino Angi ∗ Alessio Sacco ∗ Flavio Esposito † Guido Marchetto ∗  \n∗ Department of Control and Computer Engineering, Politecnico di Torino, Italy † Computer Science Department, Saint Louis University, USA  \nAbstract—The soaring complexity of networks has led to more complex methods to efficiently manage and orchestrate the multitude of network environments. Recent advances in machine learning (ML) have opened new opportunities for network management automation, exploiting existing advancesin software-defined infrastructures. Advanced routing strategies have been proposed to accommodate the traffic demand of interactive systems, where the common architecture is composed of a data-driven network management schema collecting network data that feed a reinforcement learning (RL) algorithm. However, the overhead introduced by the SDN controller and its operations can be mitigated if the networking architecture is redesigned. In this paper, we propose ROAR, a novel architectural solution that implements Deep Reinforcement Learning (DRL) inside P4 programmable switches to perform adaptive routing policies based on network conditions and traffic patterns. The network devices act independently in a multi-agent reinforcement learning (MARL) framework but are able to learn cooperative behaviors to reduce the queuing time of transmitting packets. Experimental results show that for an increasing amount of traffic in the network, there is both a throughput and delay improvement in the transmission compared to traditional approaches.  \nIndex Terms—deep reinforcement learning, P4, routing  \nI. INTRODUCTION  \nIn recent years, there has been a rapid increase in the number of brand-new applications, which not only places more and more demands on communication technologies, e.g., 5G and 6G, but also poses significant difficulties for the Internet. Each application, in particular, has distinct yet strict requirements for latency, jitter, throughput, and packet loss rate. We can observe that as networks continue to evolve and become more complex, the need for efficient routing mechanisms becomes increasingly crucial.  \nOne dictating trend is applying Machine Learning (ML) and Deep Learning (DL) to routing with the aim of leveraging information about past traffic conditions to learn good routing configurations for future conditions [1] . The flexibility provided by SDN enables a fast reactive and proactive network management, in which the SDN controller can easily observe the network and react to changes in traffic demand and evolutions [2],[3]. One class of ML is Reinforcement Learn","cbCaioJezcWC24MN","https://ap.wps.com/l/cbCaioJezcWC24MN","pdf",573887,1,7,"English","en",105,"# Introduction\n## Motivation and challenges of routing in complex networks\n## Machine learning and reinforcement learning for routing\n# ROAR approach\n## Distributed deep reinforcement learning inside P4 switches\n## Multi-agent decision logic and queue minimization\n# Evaluation results\n## Throughput and delay improvements under increasing traffic","[{\"question\":\"What problem does ROAR address in network routing?\",\"answer\":\"ROAR targets the performance limitations of centralized or slow routing decisions in complex networks, especially for short traffic bursts where fine-grained adaptation is needed.\"},{\"question\":\"How is routing implemented in ROAR?\",\"answer\":\"ROAR implements deep reinforcement learning directly in P4 programmable switches, where each switch acts as an agent to choose a forwarding port based on next-hop topology and outgoing queue state.\"},{\"question\":\"What do the experiments show about ROAR’s performance?\",\"answer\":\"As network traffic increases, ROAR achieves both throughput and delay improvements compared with traditional routing approaches.\"}]","ROAR - Routing Packets in P4 Switches With Multi-Agent Decisions Logic - Paper summary | PDF",1785935821,18,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"roar-routing-packets-in-p4-switches-with-multi-agent-decisions-logic-paper-summary","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/roar-routing-packets-in-p4-switches-with-multi-agent-decisions-logic-paper-summary/126946/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does ROAR address in network routing?","Question",{"text":75,"@type":76},"ROAR targets the performance limitations of centralized or slow routing decisions in complex networks, especially for short traffic bursts where fine-grained adaptation is needed.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is routing implemented in ROAR?",{"text":80,"@type":76},"ROAR implements deep reinforcement learning directly in P4 programmable switches, where each switch acts as an agent to choose a forwarding port based on next-hop topology and outgoing queue state.",{"name":82,"@type":73,"acceptedAnswer":83},"What do the experiments show about ROAR’s performance?",{"text":84,"@type":76},"As network traffic increases, ROAR achieves both throughput and delay improvements compared with traditional routing approaches.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]