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基于多代理深度增强学习的多无人机重新部署优化,面向群体性能恢复

Qilong Wu¹, Zitao Geng¹, Yi Ren¹

¹School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China.

Sensors (Basel, Switzerland)

|December 9, 2023

概括

本研究引入了多个无人机系统 (多个UAV) 的新策略,以便在车辆丢失后快速重新部署. 多代理的深度强化学习方法提高了群体的表现,并确保了最佳的群体形成.

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
分布式系统分布式系统

背景情况:

分布式人工智能对于多无人机 (Multi-UAV) 操作至关重要.
在车辆销毁后,多无人机的最佳重新部署带来了重大分布式重新配置 (DR) 挑战.

研究的目的:

为多个无人机系统开发一种基于深度强化学习的新型多代理分布式重新配置策略 (DRS).
优化多个无人机组的重新部署,以提高群体性能.

主要方法:

设计了一个双层的DRS框架,利用QMIX网络进行群体级重新部署,并使用单个深度Q网络进行单组调整.
用Python进行模拟,以评估拟议的战略.

主要成果:

拟议的DRS有效地优化了多个无人机组的重新部署,从而提高了群体性能.
该战略通过案例研究验证,在大规模场景中展示了高质量的绩效.

结论:

开发的基于深度强化学习的多代理DRS为多无人机系统的动态重新配置提供了有效的解决方案.
这种方法在复杂的操作环境中提高了多无人机群的弹性和性能.

关键词:

无人机群重新部署无人机群重新部署分布式重新配置策略.多代理的深度强化学习学习.无人驾驶飞行器 (UAV) 是一种无人驾驶飞行器.

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