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Multi-AUV Ad-hoc network-based Target Tracking: A Value Gradient Guidance Multi-Agent Diffusion Reinforcement Learning Approach
Not provided in the abstract
multi-agent reinforcement learningunderwater roboticscooperative tracking
2608.12436
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6h ago80%
Abstract
This paper presents a novel approach for multi-AUV ad-hoc network-based target tracking using a value-gradient-guided multi-agent diffusion reinforcement learning algorithm.
Reality Card
Core Claim
The proposed VGG-MADiffRL algorithm achieves faster convergence, higher tracking accuracy, and smoother training dynamics in cooperative tracking scenarios.
Method / Result
VGG-MADiffRL consistently outperforms existing methods in terms of convergence speed and tracking accuracy.
Limitations
The paper does not provide detailed information on the experimental setup, which may hinder reproducibility.