Perini, Dominick J.Muller, Braeden P.Kopacz, JustinMichaels, Alan J.2025-04-282025-04-282025-04-10Perini, D.J.; Muller, B.P.; Kopacz, J.; Michaels, A.J. An Application of Explainable Multi-Agent Reinforcement Learning for Spectrum Situational Awareness. Electronics 2025, 14, 1533.https://hdl.handle.net/10919/126246Allocating low-bandwidth radios to observe a wide portion of a spectrum is a key class of search-optimization problems that requires system designers to leverage limited resources and information efficiently. This work describes a multi-agent reinforcement learning system that achieves a balance between tuning radios to newly observed energy while maintaining regular sweep intervals to yield detailed captures of both short- and long-duration signals. This algorithm, which we have named SmartScan, and system implementation have demonstrated live adaptations to dynamic spectrum activity, persistence of desirable sweep intervals, and long-term stability. The SmartScan algorithm was also designed to fit into a real-time system by guaranteeing a constant inference latency. The result is an explainable, customizable, and modular approach to implementing intelligent policies into the scan scheduling of a spectrum monitoring system.application/pdfenCreative Commons Attribution 4.0 InternationalArticle - Refereed2025-04-25https://doi.org/10.3390/electronics14081533