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.

提出了一种分层目标导向的多智能体协调方法（HiT-MAC），通过协调传感器以获得低功耗、不丢失目标或减少重叠覆盖的目标覆盖，同时引入了一些实用方法，包括自我关注模块、对协调器的边际贡献近似以及对执行者的目标条件观察滤波器。实验结果表明，相比基线方法，HiT-MAC 在覆盖率、学习效率和可扩展性方面有优势，并对框架中引入的组件的有效性进行了削弱分析。

学习多智能体协作，提高定向传感器网络的目标覆盖率