# A Multi-Agent, Policy-Gradient approach to Network Routing

arXiv: https://arxiv.org/abs/2512.03211 · HTML: https://mlsanity.com/paper/2512.03211/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 25 references that are in the map.
- First posted on arXiv 2 Dec 2025.

## Builds on (most cited first, 1 of 1)

- [Learning Finite-State Controllers for Partially Observable Environments](https://mlsanity.com/paper/1301.6721/) (2013)

## Abstract (arXiv)

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.