DRL-based Optimal Scheduling for On-orbit Service with the Encoder-decoder network - Zhang, Jierui; Si, Chaoming; Ma, Changbo; Chen, Ting; Xia, Hongwei; Ma, Guangcheng (Chinese Control Conference, 2024)
In this paper, we present a deep reinforcement learning (DRL) based strategy for optimizing the scheduling of satellite on-orbit services. The orbital maneuvers necessitate the servicing satellite to consecutively rendezvous with multiple targets to execute its on-orbit missions. The principal aim of our optimization approach is to ascertain the most advantageous sequence for servicing satellites, thereby minimizing the overall cost, contingent upon the expenditure of propulsion maneuvers. To surmount this formidable challenge, we introduce an attention-based encoder-decoder neural network and train its parameters utilizing the REINFORCE algorithm with a greedy rollout baseline. Ultimately, experimental results across diverse scenarios validate the efficacy and supremacy of our proposed algorithm. The chief contribution of this work lies in its conceptualization of the satellite on-orbit service scheduling optimization quandary as an extended traveling salesman problem, culminating in the introduction of an innovative DRL-based methodology.