COORDINATED MULTIAGENT NEURAL NETWORK ARCHITECTURE FOR REAL-TIME DECISION MAKING IN AN INTELLIGENT NETWORKED ENVIRONMENT

Published by: Benwari A. Ezekiel, Ezeofor J. Chukwunanzo , Nwazor, O. Nkolika

Pages: 1-13 | DOI: 10.5281/zenodo.17529812


Urban traffic congestion poses a persistent challenge in rapidly developing regions such as Port Har-court, Nigeria. Traditional methods often lack adaptability to dynamic urban road networks. In this paper, a coordinated multi-agent deep reinforcement learning (MADRL) system that leverages graph neural networks (GNNs) for optimizing traffic flow is presented. The methodology involves building
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