Reinforcement Learning-enabled Satellite Constellation Reconfiguration and Retasking for Mission-Critical Applications
This addresses reconfiguration and retasking issues in satellite constellations for mission-critical applications, but it is incremental as it applies existing RL methods to a specific domain.
The paper tackled the problem of satellite constellation reconfiguration and retasking after failures in mission-critical applications by introducing reinforcement learning techniques, with results showing that DQN and PPO achieved effective outcomes in average rewards, task completion rates, and response times.
The development of satellite constellation applications is rapidly advancing due to increasing user demands, reduced operational costs, and technological advancements. However, a significant gap in the existing literature concerns reconfiguration and retasking issues within satellite constellations, which is the primary focus of our research. In this work, we critically assess the impact of satellite failures on constellation performance and the associated task requirements. To facilitate this analysis, we introduce a system modeling approach for GPS satellite constellations, enabling an investigation into performance dynamics and task distribution strategies, particularly in scenarios where satellite failures occur during mission-critical operations. Additionally, we introduce reinforcement learning (RL) techniques, specifically Q-learning, Policy Gradient, Deep Q-Network (DQN), and Proximal Policy Optimization (PPO), for managing satellite constellations, addressing the challenges posed by reconfiguration and retasking following satellite failures. Our results demonstrate that DQN and PPO achieve effective outcomes in terms of average rewards, task completion rates, and response times.