Multi-Agent Deep Reinforcement Learning for Weighted Multi-Path Routing
Θεόδωρος Θεοδωρόπουλος, Dimitrios Kafetzis, John Violos, Antonios Makris, Konstantinos Tserpes · 2023
Traditional multi-path routing methods distribute evenly traffic across multiple paths in a network, which can lead to inefficient use of resources if some paths are significantly longer or less reliable than others. Weighted multi-path routing addresses this issue by introducing weights to appropriately distribute traffic across the available paths based on their state. This paper proposes a novel approach to weighted multi-path routing using a multi-agent actor-critic framework, in a manner that is aligned with the need to keep up with the Quality of Service requirements of contemporary, bandwidth-intensive applications.