通过图形神经网络和强化学习进行自主空中作战决策
Lin Huo1, Chudi Wang2, Yue Han3
1Shenyang Aerospace University, 37 Daoyi South Street, Shenyang, Liaoning, 110136, China.
Scientific reports
|May 9, 2025
概括
本研究介绍了GraphZero-PPO,这是一种新的智能空中作战方法,使用多代理增强学习 (MADRL) 和图形结构. 它增强了复杂的大规模场景中的决策,实现了高胜率和快速响应.
科学领域:
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
- 航空航天工程 航空航天工程
背景情况:
- 由于复杂的空中相互作用,智能空中战斗是多代理系统的一个不断增长的领域.
- 大规模的空中战斗在可扩展性,计算复杂性和协作决策方面提出了挑战.
- 现有的方法与这些环境的动态和不确定的性质作斗争.
研究的目的:
- 为多飞机系统提出一种新的自主决策方法.
- 解决大规模空战中的可扩展性和决策挑战.
- 在动态和不确定的环境中加强协作决策.
主要方法:
- 在零顺序优化下使用图形和多代理强化学习 (MADRL) 开发了一种多飞机自主决策方法.
- 实现了GraphZero-PPO算法,集成了GraphSAGE和零顺序优化.
- 利用图形结构,以适应多代理系统的高动态和高维特征.
主要成果:
- 该GraphZero-PPO算法证明了有效适应大规模空战环境.
- 在1v1和8v8模拟场景中实现了高胜率.
- 展示了快速决策的表现,特别是在导弹发射方面.
结论:
- 拟议的基于图形的MADRL方法有效地应对大规模智能空中战斗的挑战.
- GraphZero-PPO为提高自主决策和战斗效率提供了一个有希望的解决方案.
- 该方法的适应性和效率是该领域的关键贡献.
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