Multi-Agent Deep Reinforcement Learning for Distributed Satellite Routing
<|reference_start|>Multi-Agent Deep Reinforcement Learning for Distributed Satellite Routing: 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.<|reference_end|>
@article{lozano-cuadra2024multi-agent,
    title={Multi-Agent Deep Reinforcement Learning for Distributed Satellite
  Routing},
    author={Federico Lozano-Cuadra, Beatriz Soret},
    journal={arXiv preprint arXiv:2402.17666},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.17666},
    primaryClass={cs.LG cs.IT math.IT}
}