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This paper presents a Hierarchical Reinforcement Learning methodologytailored for optimizing CubeSat task scheduling in Low Earth Orbits (LEO).Incorporating a high-level policy for global task distribution and a low-levelpolicy for real-time adaptations as a safety mechanism, our approach integratesthe Similarity Attention-based Encoder (SABE) for task prioritization and anMLP estimator for energy consumption forecasting. Integrating this mechanismcreates a safe and fault-tolerant system for CubeSat task scheduling.Simulation results validate the Hierarchical Reinforcement Learning superiorconvergence and task success rate, outperforming both the MADDPG model andtraditional random scheduling across multiple CubeSat configurations.

Safe Hierarchical Reinforcement Learning for CubeSat Task Scheduling Based on Energy Consumption