  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.
