Autonomous Decision-Making for Satellite Systems: A Decentralized Multi-Agent Deep Reinforcement Learning Approach in Partially Observable Dynamic Environments.

Traditional centralized operations struggle to scale with the complexity of large satellite constellations. This work presents a decentralized multi-agent deep reinforcement learning approach for operating an autonomous Space Situational Awareness constellation of 20 satellites. Four reward functions, with different reward scaling factors, are evaluated using a mission goal term across three coordination topologies. Developed strategies obtain similar or even outperform centralized rule-based and Mixed Integer Programming benchmarks in tested scenarios, and generalize across coordination topologies despite being trained in fully decentralized environments. Finally, space hardware inference confirms feasibility in modern systems, with execution times in the microsecond range.     «

Traditional centralized operations struggle to scale with the complexity of large satellite constellations. This work presents a decentralized multi-agent deep reinforcement learning approach for operating an autonomous Space Situational Awareness constellation of 20 satellites. Four reward functions, with different reward scaling factors, are evaluated using a mission goal term across three coordination topologies. Developed strategies obtain similar or even outperform centralized rule-base...     »