基于多智能体深度强化学习的无人艇集群博弈对抗研究

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

基于未来现代化海上作战背景,提出了利用多智能体深度强化学习方案来完成无人艇群博弈对抗中的协同围捕任务。首先,根据不同的作战模式和应用场景,提出基于分布式执行的多智能体深度确定性策略梯度算法,并对其原理进行了介绍;其次,模拟具体作战场景平台,设计多智能体网络模型、奖励函数机制以及训练策略。实验结果表明,文中方法可以有效应对敌方无人艇的协同围捕决策问题,在不同作战场景下具有较高的效率,为未来复杂作战场景下无人艇智能决策研究提供理论参考价值。

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.