Hierarchical Reinforcement Learning Framework for Stochastic Spaceflight Campaign Design

TL;DR

This paper introduces a hierarchical reinforcement learning framework combined with MILP to optimize complex, uncertain spaceflight campaigns, demonstrated through lunar exploration scenarios, advancing AI applications in space mission planning.

Contribution

It presents a novel hierarchical RL and MILP integrated framework for space campaign design under uncertainty, addressing high-dimensional and complex decision-making challenges.

Findings

Successfully applied to lunar exploration scenarios

Effectively manages uncertainty in resource utilization

Enhances decision-making in space mission planning

Abstract

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.