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Journal of System Simulation

Abstract

Abstract: A cooperative hunting method based on improved multi-agent deep deterministic policy gradient (MADDPG) is proposed to address the challenges of non-stationary environments, sparse rewards, and partial observations in the cooperative hunting of targets with unknown motion patterns by multiple unmanned surface vessels (USVs). A Markov game model for hunting is built, and a multidimensional guided reward shaping function incorporating collision avoidance, hunting success, and energy consumption penalties is designed to mitigate the sparse reward problem. ResNet residual networks are introduced to construct the Actor and Critic networks and thus enhance the representation capability of deep networks for complex state-action relationships. A prioritized experience replay mechanism is adopted, where key samples are weighted and sampled based on temporal-difference errors to improve sample efficiency and training stability. Simulation experiments demonstrate that the improved algorithm outperforms the traditional MADDPG and MAPPO algorithms in terms of convergence speed, the reward value, and training stability. In 1 000 Monte Carlo tests, the hunting success rate reaches 92.3% with only 13 collisions, which verifies the effectiveness and superiority of the proposed method in multi-USV cooperative hunting tasks.

First Page

2905

Last Page

2923

CLC

TP301

Recommended Citation

Zhang Yonghao, Xue Changyou, Fan Xueman, et al. Cooperative Hunting Method for Multiple USVs Based on Improved MADDPG[J]. Journal of System Simulation, 2026, 38(10): 2905-2923.

Corresponding Author

Xue Changyou

DOI

10.16182/j.issn1004731x.joss.26-0399

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