Safe Hierarchical Reinforcement Learning for CubeSat Task Scheduling
  Based on Energy Consumption
Mahya Ramezani M. Amin Alandihallaj Jose Luis Sanchez-Lopez Andreas Hein
This paper presents a Hierarchical Reinforcement Learning methodology
tailored for optimizing CubeSat task scheduling in Low Earth Orbits (LEO).
Incorporating a high-level policy for global task distribution and a low-level
policy for real-time adaptations as a safety mechanism, our approach integrates
the Similarity Attention-based Encoder (SABE) for task prioritization and an
MLP estimator for energy consumption forecasting. Integrating this mechanism
creates a safe and fault-tolerant system for CubeSat task scheduling.
Simulation results validate the Hierarchical Reinforcement Learning superior
convergence and task success rate, outperforming both the MADDPG model and
traditional random scheduling across multiple CubeSat configurations.