Routing with Graph Convolutional Networks and Multi-Agent Deep Reinforcement Learning
Routing with Graph Convolutional Networks and Multi-Agent Deep Reinforcement Learning
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DOI：
10.1109/nfv-sdn56302.2022.9974607
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Sai Shreyas Bhavanasi;L. Pappone;Flavio Esposito
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文献类型：
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作者：
Sai Shreyas Bhavanasi;L. Pappone;Flavio Esposito
计算机网络社区一直在稳步增加对机器学习的调查，以帮助解决诸如路由、流量预测和资源管理等任务。特别是，由于最近在其他应用方面的成功，强化学习(RL)在网络管理和最近的路由方面都有了稳步的增长。然而，由于需要重新培训，网络拓扑中的变化阻碍了基于RL的路由方法在真实环境中的应用。在本文中，我们将路由问题看作一个RL问题，具有两个新的特点：最小化流集冲突和在动态网络条件下处理无需重新训练的路由问题。我们将此方法与其他路由协议(包括多代理学习)与各种服务质量指标进行比较，并报告我们学到的教训。
The computer networking community has been steadily increasing investigations into machine learning to help solve tasks such as routing, traffic prediction, and resource management. In particular, due to the recent successes in other applications, Reinforcement Learning (RL) has seen steady growth in network management and, more recently, in routing. However, changes in the network topology prevent RL-based routing approaches from being employed in real environments due to the need for retraining. In this paper, we approach routing as an RL problem with two novel twists: minimizing flow set collisions and dealing with routing in dynamic network conditions without retraining. We compare this approach to other routing protocols, including multi-agent learning, to various Quality-of-Service metrics, and we report our lesson learned.