  This paper proposes a novel model-based policy gradient algorithm for
tracking dynamic targets using a mobile robot, equipped with an onboard sensor
with limited field of view. The task is to obtain a continuous control policy
for the mobile robot to collect sensor measurements that reduce uncertainty in
the target states, measured by the target distribution entropy. We design a
neural network control policy with the robot $SE(3)$ pose and the mean vector
and information matrix of the joint target distribution as inputs and attention
layers to handle variable numbers of targets. We also derive the gradient of
the target entropy with respect to the network parameters explicitly, allowing
efficient model-based policy gradient optimization.
