MARVEL: Multi-agent reinforcement learning for VANET delay minimization
| Autor: | Wenbo Ding, Sicong Liu, Gang Li, Zihan Wang, Chengyue Lu, Ling Cheng |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Routing protocol
Vehicular ad hoc network Computer Networks and Communications business.industry Computer science Wireless ad hoc network ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS Stability (learning theory) Manhattan mobility model 020302 automobile design & engineering 02 engineering and technology Network topology 0203 mechanical engineering Reinforcement learning Electrical and Electronic Engineering Routing (electronic design automation) business Computer network |
| Zdroj: | China Communications. 18:1-11 |
| ISSN: | 1673-5447 |
| Popis: | In urban Vehicular Ad hoc Networks (VANETs), high mobility of vehicular environment and frequently changed network topology call for a low delay end-to-end routing algorithm. In this paper, we propose a Multi-Agent Reinforcement Learning (MARL) based decentralized routing scheme, where the inherent similarity between the routing problem in VANET and the MARL problem is exploited. The proposed routing scheme models the interaction between vehicles and the environment as a multi-agent problem in which each vehicle autonomously establishes the communication channel with a neighbor device regardless of the global information. Simulation performed in the 3GPP Manhattan mobility model demonstrates that our proposed decentralized routing algorithm achieves less than 45.8 ms average latency and high stability of 0.05 % averaging failure rate with varying vehicle capacities. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|