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

TL;DR

This paper introduces a hierarchical reinforcement learning approach for CubeSat task scheduling that enhances safety, energy efficiency, and fault tolerance using attention mechanisms and energy forecasting, validated through simulations.

Contribution

It develops a novel hierarchical RL framework with safety mechanisms and energy prediction for efficient CubeSat task scheduling, outperforming existing models.

Findings

Superior convergence compared to MADDPG

Higher task success rate in simulations

Enhanced safety and fault tolerance

Abstract

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.