Improving Computational Complexity of Multi-Target Multi-Agent Reinforcement for Hyperspectral Satellite Sensor Tasking

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.