Graph Convolutional Reinforcement Learning for Collaborative Queuing Agents
This addresses service level agreement challenges in network management for improved flow prioritization, though it is incremental as it builds on existing multi-agent and reinforcement learning methods.
The paper tackles the problem of meeting strict throughput and delay requirements for classified network flows by using a graph-convolutional multi-agent reinforcement learning approach (DGN) to set weights in a weighted fair queuing system, showing it meets all requirements across various scenarios.
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.