# Multi-Agent Deep Reinforcement Learning for Distributed Satellite Routing

arXiv: https://arxiv.org/abs/2402.17666 · HTML: https://mlsanity.com/paper/2402.17666/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 6 references that are in the map.
- First posted on arXiv 27 Feb 2024.

## Builds on (most cited first, 3 of 3)

- [LEO Small-Satellite Constellations for 5G and Beyond-5G Communications](https://mlsanity.com/paper/1912.08110/) (2019)
- [Exploiting topology awareness for routing in LEO satellite constellations](https://mlsanity.com/paper/2006.12242/) (2020)
- [Deep Learning Aided Routing for Space-Air-Ground Integrated Networks Relying on Real Satellite, Flight, and Shipping Data](https://mlsanity.com/paper/2110.15138/) (2021)

## Built on by (most cited first, 2 of 2)

- [Continual Deep Reinforcement Learning for Decentralized Satellite Routing](https://mlsanity.com/paper/2405.12308/) (2024)
- [An open source Multi-Agent Deep Reinforcement Learning Routing Simulator for satellite networks](https://mlsanity.com/paper/2407.11047/) (2024)

## Abstract (arXiv)

This paper introduces a Multi-Agent Deep Reinforcement Learning (MA-DRL) approach for routing in Low Earth Orbit Satellite Constellations (LSatCs). Each satellite is an independent decision-making agent with a partial knowledge of the environment, and supported by feedback received from the nearby agents. Building on our previous work that introduced a Q-routing solution, the contribution of this paper is to extend it to a deep learning framework able to quickly adapt to the network and traffic changes, and based on two phases: (1) An offline exploration learning phase that relies on a global Deep Neural Network (DNN) to learn the optimal paths at each possible position and congestion level; (2) An online exploitation phase with local, on-board, pre-trained DNNs. Results show that MA-DRL efficiently learns optimal routes offline that are then loaded for an efficient distributed routing online.