To solve the hard problem of edge server deployment in internet of vehicle environments,an edge server deployment strategy based on multi-agent reinforcement learning(CKM-MAPPO)was proposed.It focuses on optimizing the load balancing among edge servers and minimizing edge servers'delay and energy consumption.Firstly,the Canopy and K-means algorithms were used to determine the number and initial location of edge server deployment.Then,the multi-agent reinforcement learning algorithm was leveraged to determine the optimal deployment location of the edge server.Finally,the accuracy and effectiveness of the proposed algorithm were evaluated through a series of experiments.The results show that compared with the benchmark algorithm,the proposed method improves load balancing by 26.5%,and the time delay and energy consumption are reduced by 12.4%and 17.9%,respectively.
维普
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