  Packet routing is a fundamental problem in communication networks that
decides how the packets are directed from their source nodes to their
destination nodes through some intermediate nodes. With the increasing
complexity of network topology and highly dynamic traffic demand, conventional
model-based and rule-based routing schemes show significant limitations, due to
the simplified and unrealistic model assumptions, and lack of flexibility and
adaption. Adding intelligence to the network control is becoming a trend and
the key to achieving high-efficiency network operation. In this paper, we
develop a model-free and data-driven routing strategy by leveraging
reinforcement learning (RL), where routers interact with the network and learn
from the experience to make some good routing configurations for the future.
Considering the graph nature of the network topology, we design a multi-agent
RL framework in combination with Graph Neural Network (GNN), tailored to the
routing problem. Three deployment paradigms, centralized, federated, and
cooperated learning, are explored respectively. Simulation results demonstrate
that our algorithm outperforms some existing benchmark algorithms in terms of
packet transmission delay and affordable load.
