Multi-AUV Ad-hoc network-based Target Tracking: A Value Gradient Guidance Multi-Agent Diffusion Reinforcement Learning Approach
Advancing autonomous underwater swarm coordination through diffusion-based reinforcement learning
Multi-AUV ad-hoc network-based target tracking requires networked autonomous underwater vehicles (AUVs) to cooperatively track maneuvering targets under constrained acoustic communication, dynamic topology, and uncertain ocean disturbances. Although multi-agent reinforcement lear...
ArXiv CS.LG
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This research introduces diffusion-based reinforcement learning to overcome the unique challenges of underwater communication constraints and dynamic environmental disturbances. By integrating value-gradient guidance into the denoising process, the system achieves more stable and precise cooperative target tracking for autonomous fleets.
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