# Graph Convolutional Reinforcement Learning for Collaborative Queuing Agents

arXiv: https://arxiv.org/abs/2205.12009 · HTML: https://mlsanity.com/paper/2205.12009/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 34 references that are in the map.
- First posted on arXiv 24 May 2022.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents](https://mlsanity.com/paper/1802.08757/) (2018)
- [Learning Attentional Communication for Multi-Agent Cooperation](https://mlsanity.com/paper/1805.07733/) (2018)
- [Graph Convolutional Reinforcement Learning](https://mlsanity.com/paper/1810.09202/) (2018)
- [CoLight: Learning Network-level Cooperation for Traffic Signal Control](https://mlsanity.com/paper/1905.05717/) (2019)
- [Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control](https://mlsanity.com/paper/1909.02682/) (2019)
- [Learning Transferable Cooperative Behavior in Multi-Agent Teams](https://mlsanity.com/paper/1906.01202/) (2019)
- [Deep Multi-Agent Reinforcement Learning with Relevance Graphs](https://mlsanity.com/paper/1811.12557/) (2018)
- [Packet Routing with Graph Attention Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2107.13181/) (2021)
- [Multi-Agent Reinforcement Learning for Network Load Balancing in Data Center](https://mlsanity.com/paper/2201.11727/) (2022)
- [LFQ: Online Learning of Per-flow Queuing Policies using Deep Reinforcement Learning](https://mlsanity.com/paper/2007.02735/) (2020)

## Abstract (arXiv)

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.