Richard Linares, University of Minnesota, Roberto Furfaro, University of Arizona
Keywords: Maneuver detection, Reinforcement Learning, Machine Learning
Abstract:
This paper determines the behavior of Space Objects (SOs) using inverse Reinforcement Learning (RL) to estimate the reward function that each 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 the Feature Matching approach. This approach determines the optimal reward function that a SO is using while maneuvering by assuming that the observed trajectories are optimal with respect to the SO’s own reward function. This paper uses estimated orbital elements data to determine the behavior of SOs in a data-driven fashion.
Date of Conference: September 19-22, 2017
Track: Space Situational Awareness