Diffusion-Guided Cooperative Policy Learning for Target Tracking Based on Underwater Mobile Agent Networks
Multi-agent reinforcement learning (MARL) provides a promising solution for cooperative target tracking in networks of autonomous underwater vehicles (AUVs). However, existing methods still face three major challenges: 1) policy non-stationarity caused by concurrent updates among multiple agents; 2) inefficient policy learning caused by the heterogeneous quality of experiences accumulated during exploration; and 3) policy drift between stochastic exploration and deterministic execution under dynamic underwater disturbances. To address these challenges, this paper develops a four-layer hierarchical MARL architecture comprising global training scheduling, multi-agent coordination, local pol...
| Authors | Jiaao Ma, Chuan Lin, Guangjie Han et al. |
|---|---|
| Primary category | cs.NI |
| Published | 31 March 2026 |
| Last updated at source | 12 August 2026 |
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