@INPROCEEDINGS{Espo2211:Routing,
AUTHOR="Sai Shreyas Bhavanasi and Lorenzo Pappone and Flavio Esposito",
TITLE="Routing with Graph Convolutional Networks and {Multi-Agent} Deep
Reinforcement Learning",
BOOKTITLE="2022 IEEE Conference on Network Function Virtualization and Software
Defined Networks (NFV-SDN) (NFV-SDN'22)",
ADDRESS="Phoenix, USA",
DAYS="14",
MONTH=nov,
YEAR=2022,
ABSTRACT="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."
}