Improving computational complexity of multi-target multi-agent reinforcement for hyperspectral satellite sensor tasking

Amir K. Saeed; Alhassan S. Yasin; Benjamin A. Johnson; Francisco Holguin; Benjamin M. Rodriguez

doi:10.1117/12.3014065

7 June 2024 Improving computational complexity of multi-target multi-agent reinforcement for hyperspectral satellite sensor tasking

Amir K. Saeed, Alhassan S. Yasin, Benjamin A. Johnson, Francisco Holguin, Benjamin M. Rodriguez

Author Affiliations +

Proceedings Volume 13040, Pattern Recognition and Tracking XXXV; 1304005 (2024) https://doi.org/10.1117/12.3014065
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

The study in this paper builds on previous research in reinforcement learning to address the challenges of computational complexity and scalability in multi-agent, multi-target satellite sensor tasking systems. Drawing on the groundwork laid by previous research conducted space-based hyperspectral imaging systems, novel approaches are introduced to optimize satellite tasking efficiency. The primary innovation is the implementation of a continuous space expansion method, which enhances system adaptability without necessitating intricate adjustments. Additionally, the study investigates transfer learning within larger state-action spaces, utilizing insights from smaller spaces to accelerate training in more extensive and intricate environments. Through a series of comprehensive experiments conducted in an enhanced physics-based Python simulation environment, the effectiveness and practicality of these strategies are confirmed. The outcomes reveal significant reductions in computational complexity in multi-agent, multi-target satellite tasking, rendering it more viable for real-world implementation. This research contributes to the advancement of AI-driven satellite tasking, enhancing its efficiency in managing extensive satellite constellations.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Amir K. Saeed, Alhassan S. Yasin, Benjamin A. Johnson, Francisco Holguin, and Benjamin M. Rodriguez "Improving computational complexity of multi-target multi-agent reinforcement for hyperspectral satellite sensor tasking", Proc. SPIE 13040, Pattern Recognition and Tracking XXXV, 1304005 (7 June 2024); https://doi.org/10.1117/12.3014065

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