Title:
Hierarchical Reinforcement Learning Framework for Stochastic Spaceflight Campaign Design
Hierarchical Reinforcement Learning Framework for Stochastic Spaceflight Campaign Design
| dc.contributor.author | Takubo, Yuji | |
| dc.contributor.author | Chen, Hao | |
| dc.contributor.author | Ho, Koki | |
| dc.contributor.corporatename | Georgia Institute of Technology. School of Aerospace Engineering | en_US |
| dc.date.accessioned | 2022-06-17T13:23:50Z | |
| dc.date.available | 2022-06-17T13:23:50Z | |
| dc.date.issued | 2021-12 | |
| dc.description | © AIAA | en_US |
| dc.description.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. | en_US |
| dc.identifier.citation | Y. Takubo, H. Chen, and K. Ho, “Hierarchical Reinforcement Learning Framework for Stochastic Spaceflight Campaign Design,” Journal of Spacecraft and Rockets, Vol. 59, No.2, pp. 421-433, 2022. DOI: 10.2514/1.A35122 | en_US |
| dc.identifier.doi | https://doi.org/10.2514/1.A35122 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1853/66802 | |
| dc.title | Hierarchical Reinforcement Learning Framework for Stochastic Spaceflight Campaign Design | en_US |
| dc.type | Text | |
| dc.type.genre | Post-print | |
| dspace.entity.type | Publication | |
| local.contributor.author | Ho, Koki | |
| local.contributor.corporatename | Daniel Guggenheim School of Aerospace Engineering | |
| relation.isAuthorOfPublication | 399ff8cd-3094-43c0-bc84-9071801e7ebf | |
| relation.isOrgUnitOfPublication | a348b767-ea7e-4789-af1f-1f1d5925fb65 |
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