Research on Game Confrontation of Unmanned Surface Vehicles Swarm Based on Multi-Agent Deep Reinforcement Learning

• Published in: 水下无人系统学报 Vol. 32; no. 1; pp. 79 - 86
• Main Authors: Changdong YU, Xinyang LIU, Cong CHEN, Dianyong LIU, Xiao LIANG
• Format: Journal Article
• Language: Chinese
• Published: Science Press (China) 01.02.2024
• Subjects: deep reinforcement learning; game confrontation; intelligent decision-making; multi-agent deep deterministic policy gradient algorithm; unmanned surface vehicle swarm
• ISSN: 2096-3920
• DOI: 10.11993/j.issn.2096-3920.2023-0159

Based on the background of future modern maritime combats, a multi-agent deep reinforcement learning scheme was proposed to complete the cooperative round-up task in the swarm game confrontation of unmanned surface vehicles (USVs). First, based on different combat modes and application scenarios, a multi-agent deep deterministic policy gradient algorithm based on distributed execution was determined, and its principle was introduced. Second, specific combat scenario platforms were simulated, and multi-agent network models, reward function mechanisms, and training strategies were designed. The experimental results show that the method proposed in this article can effectively solve the problem of cooperative round-up decision-making facing USVs from the enemy, and it has high efficiency in different combat scenarios. This work provides theoretical and reference value for the research on intelligent decision-making of USVs in complicated combat scenarios in the future.