TR2023-098
DEEP REINFORCEMENT LEARNING FOR STATION KEEPING ON NEAR RECTILINEAR HALO ORBITS
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- "DEEP REINFORCEMENT LEARNING FOR STATION KEEPING ON NEAR RECTILINEAR HALO ORBITS", AIAA/AAS Astrodynamics Specialist Conference, August 2023.BibTeX TR2023-098 PDF
- @inproceedings{Suda2023aug,
- author = {Suda, Takumi and Shimane, Yuri and Elango, Purnanand and Weiss, Avishai},
- title = {DEEP REINFORCEMENT LEARNING FOR STATION KEEPING ON NEAR RECTILINEAR HALO ORBITS},
- booktitle = {AIAA/AAS Astrodynamics Specialist Conference},
- year = 2023,
- month = aug,
- url = {https://www.merl.com/publications/TR2023-098}
- }
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- "DEEP REINFORCEMENT LEARNING FOR STATION KEEPING ON NEAR RECTILINEAR HALO ORBITS", AIAA/AAS Astrodynamics Specialist Conference, August 2023.
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Abstract:
In this work, we develop and evaluate a soft actor-critic (SAC) deep reinforcement learning (DRL) policy for station keeping of a spacecraft on a near-rectilinear halo orbit (NRHO) in the full-ephemeris dynamics. Monte Carlo simulations show that the DRL-based NRHO station-keeping policy maintains an approximately linear increase in delta-v at the apolune of each revolution, with a low spread in the delta-v gradient across the samples