基于深度增强学习和变压器网络的空中作战智能机动决策方法
Wentao Li1, Feng Fang1, Dongliang Peng1
1School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China.
Entropy (Basel, Switzerland)
|January 8, 2025
概括
本研究介绍了使用变压器网络进行自主机动决策的深度强化学习 (DRL) 方法. 它提高了准确性和稳定性,特别是在复杂的环境中缺少对手信息时.
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
- 控制系统 控制系统
背景情况:
- 在复杂的电磁环境中,传统的机动决策在不完整的情境数据下失败.
- 在缺少对手数据时,依赖准确信息限制了传统方法.
研究的目的:
- 开发一种自主机动决策方法,能够对缺失的对手信息保持稳定.
- 通过深度强化学习在动态和不确定的环境中提高决策准确性.
主要方法:
- 将变压器网络集成到演员和批评网络的深度强化学习 (DRL) 架构中.
- 利用变压器识别时间序列轨迹数据中的依赖性,以弥补信息丢失的能力.
- 实施有效的决策奖励,优先抽样和动态学习率调整,以应对DLR培训挑战.
主要成果:
- 拟议的DRL方法与变压器集成显著优于传统的DRL算法.
- 在涉及缺少对手信息的模拟中显示出更高的胜利率.
- 尽管引入了变压器网络,但在代理培训中提高了稳定性和效率.
结论:
- 开发的自主机动决策方法有效地处理缺少的对手信息.
- 变压器增强的DRL为复杂,不确定的环境中可靠的决策提供了有希望的解决方案.
- 拟议的培训改进确保了DRL代理人的稳定和高效的学习.
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