  Active target sensing is the task of discovering and classifying an unknown
number of targets in an environment and is critical in search-and-rescue
missions. This paper develops a deep reinforcement learning approach to plan
informative trajectories that increase the likelihood for an uncrewed aerial
vehicle (UAV) to discover missing targets. Our approach efficiently (1)
explores the environment to discover new targets, (2) exploits its current
belief of the target states and incorporates inaccurate sensor models for
high-fidelity classification, and (3) generates dynamically feasible
trajectories for an agile UAV by employing a motion primitive library.
Extensive simulations on randomly generated environments show that our approach
is more efficient in discovering and classifying targets than several other
baselines. A unique characteristic of our approach, in contrast to heuristic
informative path planning approaches, is that it is robust to varying amounts
of deviations of the prior belief from the true target distribution, thereby
alleviating the challenge of designing heuristics specific to the application
conditions.
