# Cooperative Target Detection with AUVs: A Dual-Timescale Hierarchical MARDL Approach

arXiv: https://arxiv.org/abs/2509.13381 · HTML: https://mlsanity.com/paper/2509.13381/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 13 references that are in the map.
- First posted on arXiv 16 Sep 2025.

## Builds on (most cited first, 1 of 1)

- [AUV Rendezvous Online Path Planning in a Highly Cluttered Undersea Environment Using Evolutionary Algorithms](https://mlsanity.com/paper/1604.07002/) (2016)

## Built on by (most cited first, 1 of 1)

- [Task-Oriented Sensing and Covert Transmissions for Collaborative Multi-AUV Systems](https://mlsanity.com/paper/2607.13880/) (2026)

## Abstract (arXiv)

Autonomous Underwater Vehicles (AUVs) have shown great potential for cooperative detection and reconnaissance. However, collaborative AUV communications introduce risks of exposure. In adversarial environments, achieving efficient collaboration while ensuring covert operations becomes a key challenge for underwater cooperative missions. In this paper, we propose a novel dual time-scale Hierarchical Multi-Agent Proximal Policy Optimization (H-MAPPO) framework. The high-level component determines the individuals participating in the task based on a central AUV, while the low-level component reduces exposure probabilities through power and trajectory control by the participating AUVs. Simulation results show that the proposed framework achieves rapid convergence, outperforms benchmark algorithms in terms of performance, and maximizes long-term cooperative efficiency while ensuring covert operations.