地理协调的等级增强学习框架,用于空中战斗战术追求
Ruihai Chen1, Hao Li2, Guanwei Yan2
1School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China.
Entropy (Basel, Switzerland)
|October 28, 2023
概括
本研究介绍了空中作战训练的层次增强学习框架,克服了维度挑战. 该方法在战术追击场景中提高了代理人的表现,超过了基线方法.
科学领域:
- 人工智能的人工智能
- 航空航天工程 航空航天工程
- 计算科学 计算科学
背景情况:
- 空战战术追逐呈现一个高维状态空间,导致训练强化学习中的非融合问题.
- 现有的方法难以应对现实世界空战场景的复杂性,限制了训练的有效性.
研究的目的:
- 提出一种新的空中作战训练框架,利用层次增强学习 (HRL).
- 解决维度的诅咒,改善复杂的战术追求环境中的培训趋同.
- 在模拟空中战斗中增强代理商的性能和决策能力.
主要方法:
- 实施一个层次化的强化学习框架来分解复杂的任务.
- 将3D空战问题转化为可管理的2D子问题,以提高学习效率.
- 集成基于元学习的算法和专门设计的奖励函数,以优化代理学习.
主要成果:
- 与基线方法相比,拟议的HRL框架证明了优越的培训性能.
- 减小维度技术有效地减轻了维度的诅咒.
- 超级学习算法和奖励功能进一步提高了在战术追逐场景中代理人的表现.
结论:
- 层次增强学习为复杂的空中作战训练提供了有效的解决方案.
- 该框架成功地解决了与大型国家空间和非融合相关的挑战.
- 开发的方法为提高航空航天应用中的自主代理能力提供了一个有希望的方向.
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