基于深度强化学习的决策,用于复杂的干扰波形
Yuting Xu1, Chao Wang1, Jiakai Liang1
1Key Laboratory of RF Circuits and Systems, Ministry of Education, Hangzhou Dianzi University, Hangzhou 310018, China.
Entropy (Basel, Switzerland)
|July 8, 2023
概括
这项研究介绍了一种新的深度强化学习算法,用于认知电子战中的智能干扰决策. 采用沃尔珀廷格架构的增强软演员-关键 (SAC) 算法在复杂场景中提高了干扰精度和速度.
科学领域:
- 认知电子战 认知电子战
- 人工智能在国防中的作用
- 智能干扰决策 智能干扰决策
背景情况:
- 智能通信干扰决策对于认知电子战至关重要.
- 复杂的,与适应性沟通各方不合作的场景对传统的强化学习构成挑战.
- 现有的方法在趋同和高交互要求方面扎,限制了现实世界的适用性.
研究的目的:
- 为智能干扰开发一个先进的深度强化学习算法.
- 在复杂的电子战环境中解决传统增强学习的局限性.
- 通过改进决策来提高干扰的准确性,速度和连续性.
主要方法:
- 提出了一种基于深度强化学习和最大原则的新算法:软演员-批判性 (SAC).
- 在SAC算法中集成了一个改进的Wolpertinger架构.
- 在一个复杂的,非合作的环境中,在各种干扰场景中评估算法.
主要成果:
- 拟议的SAC算法在各种干扰场景中表现出色.
- 实现了准确,快速和连续的干扰,优于传统方法.
- 减少了所需的交互次数,使其更适合于现实世界的战争.
结论:
- 增强的SAC算法与Wolpertinger架构提供了一个强大的解决方案,用于智能干扰决策.
- 这种方法在复杂的认知电子战中显著改善了传统的强化学习.
- 该算法为实现有效和高效的电子干扰提供了一种可行的方法.
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