反向增强学习的应用,以估计无人机群中的干扰
Keum Joo Kim1, Eugene Santos1, Hien Nguyen2
1Thayer Engineering School, Dartmouth College, 15 Thayer Drive, Hanover, NH 03755, USA.
Entropy (Basel, Switzerland)
|July 8, 2023
概括
这项研究引入了一个计算框架,通过分析移动和干扰来了解无人机群的意图. 不同质的无人机群体表现出增加的干扰和性能,受战斗战略和指挥风格的影响.
科学领域:
- 机器人技术和自主系统
- 人工智能的人工智能
- 计算科学 计算科学
背景情况:
- 无人机的能力不断增强,但对复杂任务的自主性有限,导致漏洞.
- 意想不到的干扰会对无人机群的性能和在动态环境中的适应性产生重大影响.
研究的目的:
- 开发一个计算框架,从移动数据中推断无人机群的意图.
- 量化和分析无人机操作中的干扰现象.
主要方法:
- 利用机器学习,包括深度学习,从运动可预测性推断干扰.
- 采用双重过渡模型和反向强化学习来推导奖励分布.
- 在各种无人机场景中计算和干扰,使用不同的策略.
主要成果:
- 无人机群体经历了更大的干扰,性能和与更大的异质性.
- 干扰方向 (正/负) 对策略组合的敏感性比群体均性更高.
结论:
- 该框架有效地推断出无人机群的意图,并量化干扰.
- 了解干扰动态对于提高无人机群的自主性和任务成功至关重要.
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