  This work focuses on the persistent monitoring problem, where a set of
targets moving based on an unknown model must be monitored by an autonomous
mobile robot with a limited sensing range. To keep each target's position
estimate as accurate as possible, the robot needs to adaptively plan its path
to (re-)visit all the targets and update its belief from measurements collected
along the way. In doing so, the main challenge is to strike a balance between
exploitation, i.e., re-visiting previously-located targets, and exploration,
i.e., finding new targets or re-acquiring lost ones. Encouraged by recent
advances in deep reinforcement learning, we introduce an attention-based neural
solution to the persistent monitoring problem, where the agent can learn the
inter-dependencies between targets, i.e., their spatial and temporal
correlations, conditioned on past measurements. This endows the agent with the
ability to determine which target, time, and location to attend to across
multiple scales, which we show also helps relax the usual limitations of a
finite target set. We experimentally demonstrate that our method outperforms
other baselines in terms of number of targets visits and average estimation
error in complex environments. Finally, we implement and validate our model in
a drone-based simulation experiment to monitor mobile ground targets in a
high-fidelity simulator.
