论文标题
学习多代理协调以增强定向传感器网络中的目标覆盖率
Learning Multi-Agent Coordination for Enhancing Target Coverage in Directional Sensor Networks
论文作者
论文摘要
通过调整分布式传感器的方向，最大目标覆盖范围是方向传感器网络（DSN）中的重要问题。这个问题具有挑战性，因为目标通常会随机移动，但是传感器的覆盖范围在角度和距离上受到限制。因此，必须协调传感器以获得理想的目标覆盖范围，例如低功耗，例如没有丢失的目标或减少冗余覆盖范围。为了实现这一目标，我们提出了一个面向目标目标的多代理协调（HIT-MAC），将目标覆盖问题分解为两级任务：由协调员和执行者跟踪分配的目标的目标分配。具体而言，协调员会定期监视环境，并将目标分配给每个执行人。反过来，执行人只需要跟踪其分配的目标即可。为了通过增强学习有效地学习HIT-MAC，我们进一步介绍了许多实用方法，包括自我注意事项模块，协调器的边际贡献近似，对执行者的目标观察过滤器等。经验结果表明，HIT-MAC的优势在覆盖率率，学习效率和可伸缩性，与基础相比。我们还对框架中引入的组件的有效性进行了消融分析。
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.
