首页|基于多智能体深度强化学习的无人艇集群博弈对抗研究

基于多智能体深度强化学习的无人艇集群博弈对抗研究

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  • 国家科技期刊平台
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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.
Research on Game Confrontation of Unmanned Surface Vehicles Swarm Based on Multi-Agent Deep Reinforcement Learning
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.

unmanned surface vehicle swarmmulti-agent deep deterministic policy gradient algorithmdeep reinforcement learningintelligent decision-makinggame confrontation

于长东、刘新阳、陈聪、刘殿勇、梁霄

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大连海事大学人工智能学院,辽宁大连, 116026

哈尔滨工程大学智能海洋航行器技术全国重点实验室,黑龙江哈尔滨, 150001

大连海事大学船舶与海洋工程学院,辽宁大连, 116026

无人艇集群 多智能体深度确定性策略梯度算法 深度强化学习 智能决策 博弈对抗

国家自然科学基金国家基础科研计划项目辽宁省应用基础研究计划项目大连市科技创新基金中央高校基本科研业务费专项智能海洋航行器技术全国重点实验室支持项目

52271302JCKY2022410C0122023JH2/1013001982021JJ12GX01731320235122024-HYHXQ-WDZC08

2024

10.11993/j.issn.2096-3920.2023-0159

水下无人系统学报
中国船舶重工集团公司第七〇五研究所

水下无人系统学报

CSTPCD
影响因子：0.251
ISSN：2096-3920
年,卷(期)：2024.32(1)
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