Safe Reinforcement Learning for Autonomous and Constraint-Aware Earth Observation Satellite Scheduling
Abstract. Satellite scheduling requires balancing dynamic tasking demands, limited resources, and real-time adaptability, conditions under which traditional rule-based and heuristic methods often fall short. This study explores Safe Reinforcement Learning (SRL) as a framework for autonomous, adaptive, and constraint-aw...