# A Novel Reinforcement Learning Routing Algorithm for Congestion Control in Complex Networks

arXiv: https://arxiv.org/abs/2401.00297 · HTML: https://mlsanity.com/paper/2401.00297/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 24 references that are in the map.
- First posted on arXiv 30 Dec 2023.

## Builds on (most cited first, 2 of 2)

- [Controlling congestion on complex networks: fairness, efficiency and network structure](https://mlsanity.com/paper/1512.09293/) (2015)
- [Fair Resource Allocation in Optical Networks under Tidal Traffic](https://mlsanity.com/paper/2005.05874/) (2020)

## Abstract (arXiv)

Despite technological advancements, the significance of interdisciplinary subjects like complex networks has grown. Exploring communication within these networks is crucial, with traffic becoming a key concern due to the expanding population and increased need for connections. Congestion tends to originate in specific network areas but quickly proliferates throughout. Consequently, understanding the transition from a flow-free state to a congested state is vital. Numerous studies have delved into comprehending the emergence and control of congestion in complex networks, falling into three general categories: soft strategies, hard strategies, and resource allocation strategies. This article introduces a routing algorithm leveraging reinforcement learning to address two primary objectives: congestion control and optimizing path length based on the shortest path algorithm, ultimately enhancing network throughput compared to previous methods. Notably, the proposed method proves effective not only in Barab'asi-Albert scale-free networks but also in other network models such as Watts-Strogatz (small-world) and Erd"os-R'enyi (random network). Simulation experiment results demonstrate that, across various traffic scenarios and network topologies, the proposed method can enhance efficiency criteria by up to 30% while reducing maximum node congestion by five times.