  Maximum target coverage by adjusting the orientation of distributed sensors
is an important problem in directional sensor networks (DSNs). This problem is
challenging as the targets usually move randomly but the coverage range of
sensors is limited in angle and distance. Thus, it is required to coordinate
sensors to get ideal target coverage with low power consumption, e.g. no
missing targets or reducing redundant coverage. To realize this, we propose a
Hierarchical Target-oriented Multi-Agent Coordination (HiT-MAC), which
decomposes the target coverage problem into two-level tasks: targets assignment
by a coordinator and tracking assigned targets by executors. Specifically, the
coordinator periodically monitors the environment globally and allocates
targets to each executor. In turn, the executor only needs to track its
assigned targets. To effectively learn the HiT-MAC by reinforcement learning,
we further introduce a bunch of practical methods, including a self-attention
module, marginal contribution approximation for the coordinator,
goal-conditional observation filter for the executor, etc. Empirical results
demonstrate the advantage of HiT-MAC in coverage rate, learning efficiency,and
scalability, comparing to baselines. We also conduct an ablative analysis on
the effectiveness of the introduced components in the framework.
