Multi-AUV Hunting Strategy Based on Regularized Competitor Model in Deep Reinforcement Learning
Yancheng Sui, Zhuo Wang, Guiqiang Bai, Hao Lu · Journal of Marine Science and Engineering · 2025
Reinforcement learning has made significant progress in single-agent applications, but it still faces various challenges in multi-agent scenarios. This study investigates the application of reinforcement learning algorithms in a competitive game scenario of multi-autonomous underwater vehicle (multi-AUV) hunting the evaders. We introduce an optimality operator and redefine the objective function of multi-agent reinforcement learning (MARL), transforming the uncertain states of other agents into solvable inference problems, namely the Regularized Competitor Model (RCM). Leveraging RCM, multi-agent systems can optimize strategies in competitive game training more efficiently. We verify and analyze the performance of the proposed algorithm in a multi-AUV hunting scenario. Simulation results demonstrate that the proposed algorithm exhibits strong adaptability and a higher success rate than the baseline in hunting the evaders.