Space Objects Maneuvering Prediction via Maximum Causal Entropy Inverse Reinforcement Learning
Name
2020_scitech05.pdf
Description
Accepted version
Size
1.22 MB
Format
Adobe PDF
Checksum (MD5)
29c67e998417a873d9ca1dd2c788e165
Author(s)
Doerr, Bryce G
Linares, Richard
Furfaro, Roberto
Date Issued
2020
Journal
AIAA Scitech 2020 Forum
Publisher
American Institute of Aeronautics and Astronautics (AIAA)
Citation
Doerr, Bryce G, Linares, Richard and Furfaro, Roberto. 2020. "Space Objects Maneuvering Prediction via Maximum Causal Entropy Inverse Reinforcement Learning." AIAA Scitech 2020 Forum, 1 PartF.
Version
Author's final manuscript
Abstract
Inverse Reinforcement Learning (RL) can be used to determine the behavior of Space Objects (SOs) by estimating the reward function that an SO is using for control. The approach discussed in this work can be used to analyze maneuvering of SOs from observational data. The inverse RL problem is solved using maximum causal entropy. This approach determines the optimal reward function that a SO is using while maneuvering with random disturbances by assuming that the observed trajectories are optimal with respect to the SO’s own reward function. Lastly, this paper develops results for scenarios involving Low Earth Orbit (LEO) station-keeping and Geostationary Orbit (GEO) station-keeping.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.2514/6.2020-0235
