Hierarchical Reinforcement Learning Framework for Stochastic Spaceflight Campaign Design. (arXiv:2103.08981v2 [cs.LG] UPDATED)

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 …

arxiv campaign design framework hierarchical learning reinforcement reinforcement learning stochastic