Paper ID: 2205.12009

Graph Convolutional Reinforcement Learning for Collaborative Queuing Agents

Hassan Fawaz, Julien Lesca, Pham Tran Anh Quang, Jérémie Leguay, Djamal Zeghlache, Paolo Medagliani

In this paper, we explore the use of multi-agent deep learning as well as learning to cooperate principles to meet stringent service level agreements, in terms of throughput and end-to-end delay, for a set of classified network flows. We consider agents built on top of a weighted fair queuing algorithm that continuously set weights for three flow groups: gold, silver, and bronze. We rely on a novel graph-convolution based, multi-agent reinforcement learning approach known as DGN. As benchmarks, we propose centralized and distributed deep Q-network approaches and evaluate their performances in different network, traffic, and routing scenarios, highlighting the effectiveness of our proposals and the importance of agent cooperation. We show that our DGN-based approach meets stringent throughput and delay requirements across all scenarios.

Submitted: May 24, 2022