Safe Hierarchical Reinforcement Learning for CubeSat Task Scheduling Based on Energy Consumption
This addresses task scheduling for CubeSats, offering a safe and fault-tolerant solution, though it appears incremental as it builds on existing hierarchical and attention-based methods.
The paper tackles CubeSat task scheduling in Low Earth Orbits by proposing a Hierarchical Reinforcement Learning method with safety mechanisms, achieving superior convergence and task success rates compared to MADDPG and random scheduling in simulations.
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.