  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.
