随机航天任务设计的分层强化学习框架
Hierarchical Reinforcement Learning Framework for Stochastic Spaceflight Campaign Design
AI总结：
针对存在不确定性的多任务航天任务设计问题，本文提出结合强化学习与混合整数线性规划（MILP）的分层框架，通过分层优化任务级与子任务级决策，为空间任务设计提供新的解决方案。
摘要：
本文针对存在不确定性的多任务航天任务设计，开发了一种分层强化学习架构，涵盖飞行器设计、基础设施部署规划和空间运输调度。该问题涉及高维设计空间，在存在不确定性时尤其具有挑战性。为应对这一挑战，所开发的框架采用分层结构，结合强化学习和基于网络的混合整数线性规划（MILP），前者优化任务级决策（例如，整个任务中使用的飞行器设计、分配给任务中每个子任务的目标需求），后者优化详细的子任务级决策（例如，何时从何处发射何物到何处）。该框架被应用于一组以原位资源利用性能不确定为特征的载人月球探测任务场景作为案例研究。本工作的主要价值在于通过分层框架整合快速发展的强化学习研究与现有基于MILP的空间物流方法，以处理原本难以解决的不确定性下空间任务设计的复杂性。这种独特的框架有望成为人工智能在空间任务设计这一新兴研究方向的关键基石。
英文摘要：
This paper develops a hierarchical reinforcement learning architecture for multimission spaceflight campaign design under uncertainty, including vehicle design, infrastructure deployment planning, and space transportation scheduling. This problem involves a high-dimensional design space and is challenging especially with uncertainty present. To tackle this challenge, the developed framework has a hierarchical structure with reinforcement learning and network-based mixed-integer linear programming (MILP), where the former optimizes campaign-level decisions (e.g., design of the vehicle used throughout the campaign, destination demand assigned to each mission in the campaign), whereas the latter optimizes the detailed mission-level decisions (e.g., when to launch what from where to where). The framework is applied to a set of human lunar exploration campaign scenarios with uncertain in situ resource utilization performance as a case study. The main value of this work is its integration of the rapidly growing reinforcement learning research and the existing MILP-based space logistics methods through a hierarchical framework to handle the otherwise intractable complexity of space mission design under uncertainty. This unique framework is expected to be a critical steppingstone for the emerging research direction of artificial intelligence for space mission design.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
