Modern communication networks like 5G and 6G are increasingly integrating Distributed Artificial Intelligence (DAI) to provide fast decision-making services like traffic management despite both the unpredictable patterns of network traffic and the intrinsic dynamism of the underlying communication network. In particular, Distributed Artificial Intelligence will enable optimal network resource usage and prevent network congestion, addressing the challenges posed by the dynamic patterns characterizing complex communication networks like 5G and 6G networks.This paper focuses on designing and assessing a new traffic management solution based on a Multi-Agent Deep Reinforcement Learning (MA-DRL). Our solution aims at adapting to network conditions including changing traffic loads, fluctuating link delays, and unpredictable packet loss scenarios, to prevent network traffic congestion, while improving throughput, latency, and loss compared to existing traffic management methods.Distributed AI, MARL, Delay optimization, packet loss optimization, traffic management, congestion control


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    Title :

    A Multi-Agent Deep Reinforcement Learning Approach for Traffic Management in Complex Communication Networks


    Contributors:


    Publication date :

    2025-05-12


    Size :

    488736 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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