https://scholars.lib.ntu.edu.tw/handle/123456789/489473
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.author | Althamary, Ibrahim | en_US |
dc.contributor.author | Huang, Chih-Wei | en_US |
dc.contributor.author | Lin, Phone | en_US |
dc.contributor.author | PHONE LIN | zz |
dc.creator | Althamary, Ibrahim;Huang, Chih-Wei;Lin, Phone | - |
dc.date.accessioned | 2020-05-04T08:03:08Z | - |
dc.date.available | 2020-05-04T08:03:08Z | - |
dc.date.issued | 2019 | - |
dc.identifier.uri | https://scholars.lib.ntu.edu.tw/handle/123456789/489473 | - |
dc.description.abstract | Under the rapid development of the Internet of Things (IoT), vehicles can be recognized as mobile smart agents that communicating, cooperating, and competing for resources and information. The task between vehicles is to learn and make decisions depending on the policy to improve the effectiveness of the multi-agent system (MAS) that deals with the continually changing environment. The multi-agent reinforcement learning (MARL) is considered as one of the learning frameworks for finding reliable solutions in a highly dynamic vehicular MAS. In this paper, we provide a survey on research issues related to vehicular networks such as resource allocation, data offloading, cache placement, ultra-reliable low latency communication (URLLC), and high mobility. Furthermore, we show the potential applications of MARL that enables decentralized and scalable decision making in vehicle-to-everything (V2X) scenarios. © 2019 IEEE. | - |
dc.relation.ispartof | 15th International Wireless Communications & Mobile Computing Conference, IWCMC 2019, Tangier, Morocco, June 24-28, 2019 | - |
dc.subject | 5G; Caching; Data Offloading; Multi-agent; Reinforcement Learning; URLLC; Vehicular Network | - |
dc.subject.classification | [SDGs]SDG2 | - |
dc.subject.other | Decision making; Fertilizers; Internet of things; Machine learning; Mobile agents; Mobile computing; Multi agent systems; Surveys; Vehicles; Caching; Data Offloading; Multi agent; URLLC; Vehicular networks; Reinforcement learning | - |
dc.title | A Survey on Multi-Agent Reinforcement Learning Methods for Vehicular Networks. | en_US |
dc.type | conference paper | en |
dc.identifier.doi | 10.1109/IWCMC.2019.8766739 | - |
dc.identifier.url | https://doi.org/10.1109/IWCMC.2019.8766739 | - |
dc.relation.pages | 1154-1159 | - |
item.cerifentitytype | Publications | - |
item.fulltext | no fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_5794 | - |
item.openairetype | conference paper | - |
item.grantfulltext | none | - |
crisitem.author.dept | Networking and Multimedia | - |
crisitem.author.dept | Computer Science and Information Engineering | - |
crisitem.author.dept | Center for Artificial Intelligence and Advanced Robotics | - |
crisitem.author.orcid | 0000-0001-7103-5516 | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | Others: University-Level Research Centers | - |
顯示於: | 資訊工程學系 |
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