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This paper develops a spaceflight mission designing framework which optimizes vehicle (spacecraft) design, resource deployment, and detailed space transportation scheduling at the same time, under the consideration of in-situ resource utilization in space and uncertain factors in mission scenario. This is enabled by the integrated framework of reinforcement learning and mixed-integer linear programming.

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This page is a summary of: Hierarchical Reinforcement Learning Framework for Stochastic Spaceflight Campaign Design, Journal of Spacecraft and Rockets, December 2021, American Institute of Aeronautics and Astronautics (AIAA), DOI: 10.2514/1.a35122.
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