  Drones are becoming versatile in a myriad of applications. This has led to
the use of drones for spying and intruding into the restricted or private air
spaces. Such foul use of drone technology is dangerous for the safety and
security of many critical infrastructures. In addition, due to the varied
low-cost design and agility of the drones, it is a challenging task to identify
and track them using the conventional radar systems. In this paper, we propose
a reinforcement learning based approach for identifying and tracking any
intruder drone using a chaser drone. Our proposed solution uses computer vision
techniques interleaved with the policy learning framework of reinforcement
learning to learn a control policy for chasing the intruder drone. The whole
system has been implemented using ROS and Gazebo along with the Ardupilot based
flight controller. The results show that the reinforcement learning based
policy converges to identify and track the intruder drone. Further, the learnt
policy is robust with respect to the change in speed or orientation of the
intruder drone.
