超音速车辆的智能机动策略在三人追逐逃跑游戏中通过深度强化学习来实现
Tian Yan1, Zijian Jiang1, Tong Li1
1Unmanned System Research Institute, Northwestern Polytechnical University, Xi'an, China.
Frontiers in neuroscience
|March 1, 2024
概括
本研究开发了一种智能机动策略,用于高超音速车辆 (HV) 使用深度增强学习 (DRL) 来逃避双拦截器攻击. 改进的TD3算法在复杂的场景中提高了逃避成功,融合速度和能源效率.
科学领域:
- 航空航天工程 航空航天工程
- 人工智能的人工智能
- 控制理论 控制理论
背景情况:
- 超音速车辆 (HV) 面临着先进的协作拦截技术带来的越来越大的威胁.
- 开发有效的逃避策略对于对抗协调攻击的HV生存能力至关重要.
- 现有的逃避方法与压缩的机动窗口以及双拦截器战略的复杂性增加而扎.
研究的目的:
- 为高超音速飞行器 (HV) 设计一个智能机动策略,以避免两个拦截器的协作拦截.
- 为了提高深度强化学习 (DRL) 对实时,复杂的逃避场景的有效性.
- 提高HV逃避策略的普遍性和能源效率.
主要方法:
- 模拟了三人追逐和逃避 (PE) 问题作为马尔科夫决策过程.
- 应用并改进了对连续动作空间的双延迟深确定性梯度 (TD3) 算法.
- 结合了双重训练策略来管理两个拦截器和回归神经网络以提高概括性.
主要成果:
- 改进的TD3算法在具有挑战性的条件下有效地规避了协作拦截.
- 数字模拟验证了对趋同速度和概括能力的显著改进.
- 该战略成功地平衡了错误距离和能源消耗,实现了节能效应.
结论:
- 拟议的基于DRL的智能机动策略为对抗双拦截器威胁的HV回避提供了强大的解决方案.
- 增强的TD3算法在速度,概括和能源效率方面提供了更好的性能.
- 这项研究有助于推进反超音速协作拦截技术的发展.
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