A Multi-Agent, Policy-Gradient approach to Network Routing
Machine Learning
2025-12-04 v1 Networking and Internet Architecture
Abstract
Network routing is a distributed decision problem which naturally admits numerical performance measures, such as the average time for a packet to travel from source to destination. OLPOMDP, a policy-gradient reinforcement learning algorithm, was successfully applied to simulated network routing under a number of network models. Multiple distributed agents (routers) learned co-operative behavior without explicit inter-agent communication, and they avoided behavior which was individually desirable, but detrimental to the group's overall performance. Furthermore, shaping the reward signal by explicitly penalizing certain patterns of sub-optimal behavior was found to dramatically improve the convergence rate.
Cite
@article{arxiv.2512.03211,
title = {A Multi-Agent, Policy-Gradient approach to Network Routing},
author = {Nigel Tao and Jonathan Baxter and Lex Weaver},
journal= {arXiv preprint arXiv:2512.03211},
year = {2025}
}