  This article presents a deep reinforcement learning-based approach to tackle
a persistent surveillance mission requiring a single unmanned aerial vehicle
initially stationed at a depot with fuel or time-of-flight constraints to
repeatedly visit a set of targets with equal priority. Owing to the vehicle's
fuel or time-of-flight constraints, the vehicle must be regularly refueled, or
its battery must be recharged at the depot. The objective of the problem is to
determine an optimal sequence of visits to the targets that minimizes the
maximum time elapsed between successive visits to any target while ensuring
that the vehicle never runs out of fuel or charge. We present a deep
reinforcement learning algorithm to solve this problem and present the results
of numerical experiments that corroborate the effectiveness of this approach in
comparison with common-sense greedy heuristics.
