  Persistent target tracking in urban environments using UAV is a difficult
task due to the limited field of view, visibility obstruction from obstacles
and uncertain target motion. The vehicle needs to plan intelligently in 3D such
that the target visibility is maximized. In this paper, we introduce Target
Following DQN (TF-DQN), a deep reinforcement learning technique based on Deep
Q-Networks with a curriculum training framework for the UAV to persistently
track the target in the presence of obstacles and target motion uncertainty.
The algorithm is evaluated through several simulation experiments qualitatively
as well as quantitatively. The results show that the UAV tracks the target
persistently in diverse environments while avoiding obstacles on the trained
environments as well as on unseen environments.
