Adaptive Routing in Dynamic Networks by Multi-Agent Reinforcement Learning: Design, Modeling and Evaluation of MARL Routing

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The proliferation of dynamic networks, such as mobile ad hoc networks (MANETs) and vehicular networks (VANETs), poses major challenges to conventional routing protocols, which struggle to adapt to rapid topology changes and fluctuating quality of service. This paper presents an alternative based on multi-agent reinforcement learning (MARL). We propose MARL Routing, a fully distributed routing architecture where each node acts as an intelligent agent learning from local forwarding decisions via a cooperative Q- learning algorithm. The main contribution lies in the design of a composite reward function, a partial Q-table reset mechanism for increased responsiveness, and a sparse information-sharing protocol (Q gossip). Experimental validation, conducted under NS-3, compares MARL Routing to OLSR, AODV, and classical Q- routing. The results demonstrate significant superiority in highly mobile environments, with a delivery rate of 91% at 15 m/s compared to 60-70% for reference protocols, and a convergence time of less than 2 seconds after a sudden failure. Sensitivity analysis confirms the robustness of the chosen parameters. Keywords: Adaptive routing, Dynamic networks, Multi-agent reinforcement learning, Distributed Q- learning, Mobility, Network protocols, MARL Routing. Title: Adaptive Routing in Dynamic Networks by Multi-Agent Reinforcement Learning: Design, Modeling and Evaluation of MARL Routing Author: MUKUNA WA MUKUNA FADER, EKILA BOLA TRÉSOR, MANZIA MANSANGA ELIANE International Journal of Novel Research in Computer Science and Software Engineering ISSN 2394-7314 Vol. 13, Issue 3, September 2026 - December 2026 Page No: 1-7 Novelty Journals Website: www.noveltyjournals.com Published Date: 16-September-2026 DOI: https://doi.org/10.5281/zenodo.22793830 Paper Download Link (Source) https://www.noveltyjournals.com/upload/paper/Adaptive%20Routing%20in%20Dynamic%20Networks%20by%20Multi-Agent%20Reinforcement-16092026-1.pdf