Multi-Agent Deep Reinforcement Learning for Trajectory Design and Power Allocation in Multi-UAV Networks

Multi-Agent Deep Reinforcement Learning for Trajectory Design and Power Allocation in Multi-UAV Networks

Unmanned aerial vehicle (UAV) is regarded as an effective technology in future wireless networks. However, due to the non-convexity feature of joint trajectory design and power allocation (JTDPA) issue, it is challenging to attain the optimal joint policy in multi-UAV networks. In this article, a mu...

Full description

Bibliographic Details
Main Authors: Nan Zhao, Zehua Liu, Yiqiang Cheng
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
UAV networks
trajectory design
power allocation
multi-agent deep reinforcement learning
Online Access:https://ieeexplore.ieee.org/document/9152044/

Internet

https://ieeexplore.ieee.org/document/9152044/

Similar Items