Fast Neural Policy Training for Agile Satellite Scheduling via Constraint-Consistent Transfer

作者：Ming Chen, Lei He, Yingwen Chen, Luona Wei, Xiaolu Liu, Guohua Wu, Witold Pedrycz · 发表于：IEEE Transactions on Automation Science and Engineering · 年份：2026 · DOI：10.1109/tase.2026.3703455 · 被引用次数：1 · 研究领域：Satellite Communication Systems、Age of Information Optimization、Reinforcement Learning in Robotics

The Agile Earth Observation Satellite Scheduling Problem (AEOSSP) presents a formidable challenge in combinatorial optimization. While Deep Reinforcement Learning (DRL) offers a promising avenue for automated solution construction, the nonlinear dynamics of satellite attitude control create a critical computational bottleneck: constraint validation must be executed sequentially, precluding efficient batch parallelization and severely impeding training throughput. To overcome this, we propose the Transfer Construction Method (TCM) for fast neural policy training in AEOSSP. TCM integrates two core components: 1) Enhanced Construction Model (ECM): A lightweight yet powerful neural policy tailored for end-to-end solution generation. 2) Constraint-Consistent Transfer (CCT): A two-stage training framework. In the CCT pre-training phase, the ECM is initialized on a constraint-consistent surrogate problem, S-AEOSSP, where linearized dynamics facilitate rapid batch constraint checking while preserving the underlying constraint topology. These structural priors are subsequently transferred to the full AEOSSP for deep-training, where the model adapts to precise orbital mechanics. By explicitly decoupling structural learning from dynamic complexity, TCM dramatically accelerates convergence. Experimental evaluations demonstrate that ECM alone surpasses state-of-the-art neural policies while reducing training time by 37.8%. Under the full TCM, the required training time is further compress...