Reproduce “Adaptive AUV Hunting Policy with Covert Communication via Diffusion Model” on OpenResearch
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About this paper
Xu Guo, Xiangwang Hou, Minrui Xu, Jianrui Chen, Jingjing Wang, Jun Du, Yong Ren
A new multi-AUV hunting framework ensures inter-agent communication remains covert against target eavesdropping, achieving successful encirclement and obstacle avoidance. The Adaptive Multi-Agent Diffusion Policy (AMADP), trained via offline reinforcement learning, outperforms state-of-the-art benchmarks in hunting success rate and convergence speed, consistently maintaining communication covertness.
Key insights
- Maintaining covert communication between hunter AUVs is critical for mission success against intelligent, eavesdropping targets.
- Diffusion models can effectively generate diverse and optimal trajectories for complex continuous control problems in multi-agent reinforcement learning.
- Offline reinforcement learning is a practical and data-efficient approach for training AUV policies in challenging underwater environments.
Summary by alphaXiv.
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