# Learning Multi-Agent Coordination for Enhancing Target Coverage in Directional Sensor Networks

arXiv: https://arxiv.org/abs/2010.13110 · HTML: https://mlsanity.com/paper/2010.13110/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2020.

## Built on by (most cited first, 19 of 19)

- [ToM2C: Target-oriented Multi-agent Communication and Cooperation with Theory of Mind](https://mlsanity.com/paper/2111.09189/) (2021)
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- [RSPT: Reconstruct Surroundings and Predict Trajectories for Generalizable Active Object Tracking](https://mlsanity.com/paper/2304.03623/) (2023)
- [TarGF: Learning Target Gradient Field to Rearrange Objects without Explicit Goal Specification](https://mlsanity.com/paper/2209.00853/) (2022)
- [Proactive Multi-Camera Collaboration For 3D Human Pose Estimation](https://mlsanity.com/paper/2303.03767/) (2023)
- [Coordinate-Aligned Multi-Camera Collaboration for Active Multi-Object Tracking](https://mlsanity.com/paper/2202.10881/) (2022)
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- [SocialGFs: Learning Social Gradient Fields for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2405.01839/) (2024)
- [DeCOM: Decomposed Policy for Constrained Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2111.05670/) (2021)
- [Multi-Target Active Object Tracking with Monte Carlo Tree Search and Target Motion Modeling](https://mlsanity.com/paper/2205.03555/) (2022)
- [Dynamic Size Message Scheduling for Multi-Agent Communication under Limited Bandwidth](https://mlsanity.com/paper/2306.10134/) (2023)
- [Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2411.11099/) (2024)
- [A Case Study of Counting the Number of Unique Users in Linear and Non-Linear Trails -- A Multi-Agent System Approach](https://mlsanity.com/paper/2503.07651/) (2025)
- [Interactive Distillation for Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2601.05407/) (2026)
- [Reinforcement Learning for Delivery Drone-Based Participatory Sensing in Dynamic Environments](https://mlsanity.com/paper/2607.18874/) (2026)

## Abstract (arXiv)

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.