Future space communication architectures deployed across heterogeneous space systems will require novel methods of coordinating inter-system communication and command distribution. As network complexity increases in time and distance, the ability to facilitate command and control across a large number of systems is a significant constraint on mission performance. This study presents the application of multi-agent reinforcement learning (MARL) to demonstrate a collaborative mesh network of inter-satellite links that self-configure and self-optimize in response to varying mission data needs.This paper explores methods of scaling distributed reinforcement learning-based approaches where satellites modeled as RL agents can observe their local wireless environment, share knowledge with other satellites, and cooperatively achieve network-wide mission objectives. It also implements a transfer learning approach for increasing the network size of a distributed, multi-agent system without modifying action and observation spaces.


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    Titel :

    Scaling Collaborative Space Networks with Deep Multi-Agent Reinforcement Learning


    Beteiligte:
    Ma, Ricky (Autor:in) / Hernandez, Gabe (Autor:in) / Hernandez, Carrie (Autor:in)


    Erscheinungsdatum :

    20.06.2023


    Format / Umfang :

    2490936 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch