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.
Machine learning and artificial intelligence algorithms have expanded dramatically in use across diverse fields of research and practice. Despite the extensive benefits that these algorithms can bring to researchers, system designers, and operators alike, the adoption of these algorithms in space-related scenarios has lagged behind other fields. In order to encourage the increased adoption of artificial intelligence and machine learning techniques to space-domain-related problems, flexible modeling and simulation capabilities are needed to build stakeholder trust in these techniques. This research presents the development of a flexible Python-based modeling and simulation environment for applying Reinforcement Learning to Low Earth Orbit satellite Hyper Spectral Imaging sensor tasking. With the transition away from small numbers of highly exquisite on-orbit systems to proliferated architectures characterized by constellations of lower cost and complexity spacecraft, the methods by which payload sensors are tasked have become dynamic and complex, making the problem of determining effective sensor tasking methods an important area of research. Such a problem lends itself well to the application of Reinforcement Learning. The focus of this work is on developing the role of intelligent systems in improving the data acquisition process in a space-based hyperspectral imaging system, and showing how the developed modeling and simulation framework can be successfully employed to improve the acquisition of targets of interest. A key strength of the presented reinforcement learning application framework is its non-commercial, extensible nature, suitable for both research and educational purposes.
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