This paper considers the problem of sensor scheduling for the purposes of detection and tracking of smart targets. Smart targets are targets that are able to detect when they are under surveillance and react in a manner that makes future surveillance more difficult. We take a reinforcement learning approach to adaptively schedule a multi-modality sensor so as to most quickly and effectively detect the presence of smart targets and track them as they travel through a surveillance region. An optimal scheduling strategy, which would simultaneously address the issue of target detection and tracking, is very challenging computationally. To avoid this difficulty, we advocate a two stage approach where targets are first detected and then handed off to the tracking algorithm.