ASF-HDRL: hierarchical reinforcement learning with attention-guided high-level switching and stage-conditioned feature modulation for multi-AUV cooperative tracking

The cooperative tracking of Multi-Autonomous Underwater vehicles (AUV) has shown great potential in fields such as ocean environmental monitoring, marine resource exploration, and underwater security. However, limited underwater acoustic communication range, sparse deployment of sensor nodes, and environmental uncertainties often lead to incomplete target trajectory information and partially observable states, resulting in decreased tracking accuracy and unstable performance in complex scenarios. To solve the above problems, this paper proposes a Hierarchical Deep Reinforcement Learning framework, termed ASF-HDRL (Attention-guided high-level Switching with stage-conditioned Feature modulation), which decouples high- and low-level policies and incorporates a target estimation algorithm to enhance adaptability and robustness in complex tasks. Simulation results demonstrate that ASF-HDRL achieves superior cooperative tracking performance in challenging simulation environments, outperforming several mainstream baseline methods in terms of convergence speed and tracking accuracy.