Dynamic Target Assignment by Unmanned Surface Vehicles Based on Reinforcement Learning

Tao Hu, Xiaoxue Zhang (), Xueshan Luo and Tao Chen
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Tao Hu: National Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha 410073, China
Xiaoxue Zhang: National Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha 410073, China
Xueshan Luo: National Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha 410073, China
Tao Chen: National Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha 410073, China

Mathematics, 2024, vol. 12, issue 16, 1-20

Abstract: Due to the dynamic complexities of the multi-unmanned vessel target assignment problem at sea, especially when addressing moving targets, traditional optimization algorithms often fail to quickly find an adequate solution. To overcome this, we have developed a multi-agent reinforcement learning algorithm. This approach involves defining a state space, employing preferential experience replay, and integrating self-attention mechanisms, which are applied to a novel offshore unmanned vessel model designed for dynamic target allocation. We have conducted a thorough analysis of strike positions and times, establishing robust mathematical models. Additionally, we designed several experiments to test the effectiveness of the algorithm. The proposed algorithm improves the quality of the solution by at least 30% in larger scale scenarios compared to the genetic algorithm (GA), and the average solution speed is less than 10% of the GA, demonstrating the feasibility of the algorithm in solving the problem.

Keywords: moving targets; weapon-target assignment; unmanned surface vessels; reinforcement learning; multi agent (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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