Maneuvering detection and prediction using inverse reinforcement learning for space situational awareness

This paper uses inverse Reinforcement Learning (RL) 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 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 utilizes estimated orbital element data to determine the behavior of SOs in a data-driven fashion. Simple proof-of-concept results are shown for a simulation example.