PRISMA: A Packet Routing Simulator for Multi-Agent Reinforcement Learning
| Autor: | Redha A. Alliche, Tiago Da Silva Barros, Ramon Aparicio-Pardo, Lucile Sassatelli |
|---|---|
| Přispěvatelé: | Combinatorics, Optimization and Algorithms for Telecommunications (COATI), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-COMmunications, Réseaux, systèmes Embarqués et Distribués (Laboratoire I3S - COMRED), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Equipe SIGNET, Signal, Images et Systèmes (Laboratoire I3S - SIS), Institut Universitaire de France (IUF), Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.), This work was performed using HPC resources from GENCI-IDRIS (Grant 2021-AD011012577)., ANR-19-CE25-0001,ARTIC,Contrôle basé sur l'Intelligence Artificielle de réseau en nuage(2019) |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
ns-3
[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI] [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] Network Simulation [INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA] [INFO.INFO-AU]Computer Science [cs]/Automatic Control Engineering Packet Routing [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO] ML tool Multi-Agent [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation Reinforce- ment Learning [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] |
| Zdroj: | 4th Intl Workshop on Network Intelligence collocated with IFIP Networking 2022 4th Intl Workshop on Network Intelligence collocated with IFIP Networking 2022, Jun 2022, Catania, Italy. ⟨10.23919/IFIPNetworking55013.2022.9829797⟩ |
| DOI: | 10.23919/IFIPNetworking55013.2022.9829797⟩ |
| Popis: | International audience; In this paper, we present PRISMA: Packet Routing Simulator for Multi-Agent Reinforcement Learning. To the best of our knowledge, this is the first tool specifically conceived to develop and test Reinforcement Learning (RL) algorithms for the Distributed Packet Routing (DPR) problem. In this problem, where a communication node selects the outgoing port to forward a packet using local information, distance-vector routing protocol (e.g., RIP) are traditionally applied. However, when network status changes very dynamically, is uncertain, or is partially hidden (e.g., wireless ad hoc networks or wired multi-domain networks), RL is an alternate solution to discover routing policies better fitted to these cases. Unfortunately, no RL tools have been developed to tackle the DPR problem, forcing the researchers to implement their own simplified RL simulation environments, complicating reproducibility and reducing realism. To overcome these issues, we present PRISMA, which offers to the community a standardized framework where: (i) communication process is realistically modelled (thanks to ns3); (ii) distributed nature is explicitly considered (nodes are implemented as separated threads); (iii) and, RL proposals can be easily developed (thanks to a modular code design and real-time training visualization interfaces) and fairly compared them. |
| Databáze: | OpenAIRE |
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