{"title":"Wireless MAC Protocol Synthesis and Optimization With Multi-Agent Distributed Reinforcement Learning","authors":"Navid Keshtiarast;Oliver Renaldi;Marina Petrova","doi":"10.1109/LNET.2024.3503289","DOIUrl":null,"url":null,"abstract":"In this letter, we propose a novel Multi-Agent Deep Reinforcement Learning (MADRL) framework for MAC protocol design. Unlike centralized approaches, which rely on a single entity for decision-making, MADRL empowers individual network nodes to autonomously learn and optimize their MAC from local observations. Our framework is the first of a kind that enables distributed multi-agent learning within the ns-3 environment, and facilitates the design and synthesis of adaptive MAC protocols tailored to specific environmental conditions. We demonstrate the effectiveness of the MADRL framework through extensive simulations, showcasing superior performance compared to legacy protocols across diverse scenarios.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"6 4","pages":"242-246"},"PeriodicalIF":0.0000,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Networking Letters","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10758702/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
在这封信中,我们为 MAC 协议设计提出了一种新颖的多代理深度强化学习(MADRL)框架。与依赖单一实体进行决策的集中式方法不同,MADRL 使单个网络节点能够根据本地观测结果自主学习和优化其 MAC。我们的框架是首个能在 ns-3 环境中实现分布式多代理学习的框架,有助于设计和合成适应特定环境条件的自适应 MAC 协议。我们通过大量仿真证明了 MADRL 框架的有效性,并在各种场景中展示了与传统协议相比更优越的性能。
Wireless MAC Protocol Synthesis and Optimization With Multi-Agent Distributed Reinforcement Learning
In this letter, we propose a novel Multi-Agent Deep Reinforcement Learning (MADRL) framework for MAC protocol design. Unlike centralized approaches, which rely on a single entity for decision-making, MADRL empowers individual network nodes to autonomously learn and optimize their MAC from local observations. Our framework is the first of a kind that enables distributed multi-agent learning within the ns-3 environment, and facilitates the design and synthesis of adaptive MAC protocols tailored to specific environmental conditions. We demonstrate the effectiveness of the MADRL framework through extensive simulations, showcasing superior performance compared to legacy protocols across diverse scenarios.