Multi-Agent Deep Reinforcement Learning for Distributed Satellite Routing

Federico Lozano-Cuadra; Beatriz Soret

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arXiv · 2402.17666

Multi-Agent Deep Reinforcement Learning for Distributed Satellite Routing

Federico Lozano-Cuadra·Beatriz Soret

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Abstract

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.

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BibTeX RIS

Federico Lozano-Cuadra, Beatriz Soret. 2024-02-27. Multi-Agent Deep Reinforcement Learning for Distributed Satellite Routing. https://arxiv.org/abs/2402.17666

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