  We present a novel Double Deep Q Network (DDQN) application to a sensor
management problem in space situational awareness (SSA). Frequent launches of
satellites into Earth orbit pose a significant sensor management challenge,
whereby a limited number of sensors are required to detect and track an
increasing number of objects. In this paper, we demonstrate the use of
reinforcement learning to develop a sensor management policy for SSA. We
simulate a controllable Earth-based telescope, which is trained to maximise the
number of satellites tracked using an extended Kalman filter. The estimated
state covariance matrices for satellites observed under the DDQN policy are
greatly reduced compared to those generated by an alternate (random) policy.
This work provides the basis for further advancements and motivates the use of
reinforcement learning for SSA.
