通过层次化的强化学习来加强多无人机空中作战决策
Huan Wang1,2, Jintao Wang3
1College of Artificial Intelligence and Automation, Hohai University, Changzhou, 213200, China. whuan@hhu.edu.cn.
Scientific reports
|February 23, 2024
概括
本研究引入了一种新的等级增强学习方法,用于在无人机战斗中进行自主决策. 该方法增强了战略学习,并在复杂的空中作战模拟中表现出卓越的性能.
科学领域:
- 机器人和人工智能 机器人和人工智能
- 航空航天工程 航空航天工程
- 计算智能是一种计算智能.
背景情况:
- 自主决策对于无人机空中战斗至关重要.
- 当前的基于规则的算法在复杂的多无人机战斗场景中扎.
- 在动态战斗环境中优化自主系统仍然是一个重大挑战.
研究的目的:
- 为多个无人机空中作战决策提出一种新的等级增强学习 (HRL) 方法.
- 在复杂的战斗环境中解决现有方法的局限性.
- 提高自主无人机战术的效率和有效性.
主要方法:
- 设计了一个基于战术行动类型的等级决策网络,以简化机动选择.
- 分解高质量的战斗经验,增加有价值的训练数据,促进战略学习.
- 使用JSBSim无人机模拟平台验证了算法的性能.
主要成果:
- 拟议的HRL算法与基线方法相比显示出更高的性能.
- 在均和不利的空中战斗场景中都实现了有效的决策.
- 该方法成功地简化了决策空间,并增强了战略学习.
结论:
- 新的等级增强学习方法在多无人机空中战斗中取得了重大进展.
- 这种方法为空中作战中复杂的自主决策提供了更有效的解决方案.
- 这些发现表明了智能无人机系统未来研究的有希望方向.
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