分散的共识推断为多重约束无人机追逐逃避游戏的基层强化学习
概括
一个新的基于共识推断的层次强化学习 (CI-HRL) 框架改善了多个无人机的合作逃避和形成覆盖. 这种方法提高了复杂的追逐逃跑游戏中的群体协调和任务完成.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 人工智能的人工智能
- 多代理系统 多代理系统
背景情况:
- 多个四旋翼无人机 (UAV) 系统对于追逐逃跑游戏 (MC-PEG) 等应用至关重要.
- 合作逃避和形成覆盖 (CEFC) 是一个具有挑战性的MC-PEG任务,特别是有限的通信.
- 高维的复杂性来自于在障碍物,敌人,目标和阵营动态中协调无人机.
研究的目的:
- 为CEFC在多无人机系统中的任务提出一个新的两级框架,即基于共识推断的层次增强学习 (CI-HRL).
- 在通信有限的环境和高维问题空间中应对挑战.
- 增强无人机群体的协作逃避和任务完成能力.
主要方法:
- 制定了两级框架:CI-HRL,其中一个是针对目标定位的高级政策,另一个是针对导航和形成的低级政策.
- 为高层政策引入了以共识为导向的多代理沟通 (ConsMAC),以实现地方国家的全球认知和共识.
- 采用了基于培训的多个代理的深度决定性政策梯度 (AT-M) 和低级别控制的政策蒸.
主要成果:
- CI-HRL在提高群体的协作逃避和任务完成方面表现出卓越的表现.
- 该ConsMAC模块有效地使代理商能够汇总邻居消息并建立共识.
- 软件在循环 (SITL) 模拟验证了框架在复杂场景中的有效性.
结论:
- 拟议的CI-HRL框架为具有挑战性的多UAV合作逃避和形成覆盖任务提供了强大的解决方案.
- 具有专业沟通和控制政策的等级方法显著改善了群体的表现.
- 这项研究促进了多代理系统在动态和受限制环境中的功能.
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