{"title":"通过强化学习实现自主数据轮渡路线设计","authors":"D. Henkel, T. Brown","doi":"10.1109/WOWMOM.2008.4594888","DOIUrl":null,"url":null,"abstract":"Communication in delay tolerant networks can be facilitated by the use of dedicated mobile ldquoferriesrdquo which physically transport data packets between network nodes. The goal is for the ferry to autonomously find routes which minimize the average packet delay in the network. We prove that paths which visit all nodes in a round-trip fashion, i.e., solutions to the traveling salesman problem, do not yield the lowest average packet delay. We propose two novel ferry path planning algorithms based on stochastic modeling and machine learning. We model the path planning task as a Markov decision process with the ferry acting as an independent agent. We apply reinforcement learning to enable the ferry to make optimal decisions. Simulation experiments show the resulting routes have lower average packet delay than solutions known to date.","PeriodicalId":346269,"journal":{"name":"2008 International Symposium on a World of Wireless, Mobile and Multimedia Networks","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2008-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"37","resultStr":"{\"title\":\"Towards autonomous data ferry route design through reinforcement learning\",\"authors\":\"D. Henkel, T. Brown\",\"doi\":\"10.1109/WOWMOM.2008.4594888\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Communication in delay tolerant networks can be facilitated by the use of dedicated mobile ldquoferriesrdquo which physically transport data packets between network nodes. The goal is for the ferry to autonomously find routes which minimize the average packet delay in the network. We prove that paths which visit all nodes in a round-trip fashion, i.e., solutions to the traveling salesman problem, do not yield the lowest average packet delay. We propose two novel ferry path planning algorithms based on stochastic modeling and machine learning. We model the path planning task as a Markov decision process with the ferry acting as an independent agent. We apply reinforcement learning to enable the ferry to make optimal decisions. Simulation experiments show the resulting routes have lower average packet delay than solutions known to date.\",\"PeriodicalId\":346269,\"journal\":{\"name\":\"2008 International Symposium on a World of Wireless, Mobile and Multimedia Networks\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-06-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"37\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 International Symposium on a World of Wireless, Mobile and Multimedia Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WOWMOM.2008.4594888\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 International Symposium on a World of Wireless, Mobile and Multimedia Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WOWMOM.2008.4594888","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Towards autonomous data ferry route design through reinforcement learning
Communication in delay tolerant networks can be facilitated by the use of dedicated mobile ldquoferriesrdquo which physically transport data packets between network nodes. The goal is for the ferry to autonomously find routes which minimize the average packet delay in the network. We prove that paths which visit all nodes in a round-trip fashion, i.e., solutions to the traveling salesman problem, do not yield the lowest average packet delay. We propose two novel ferry path planning algorithms based on stochastic modeling and machine learning. We model the path planning task as a Markov decision process with the ferry acting as an independent agent. We apply reinforcement learning to enable the ferry to make optimal decisions. Simulation experiments show the resulting routes have lower average packet delay than solutions known to date.