Multi-Agent Reinforcement Learning for UAVs 3D Trajectory Designing and Mobile Ground Users Scheduling with No-Fly Zones
Author:
Affiliation:
1. Wuhan University,School of Electronic Information,Wuhan,China,430072
2. Avic Xi’an Aircraft Industry (Group) Company ltd,Xian,China,000768
Funder
National Natural Science Foundation of China
State Grid Hubei Electric Power Co
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10233255/10233122/10233375.pdf?arnumber=10233375
Reference14 articles.
1. Dueling network architectures for deep reinforcement learning;wang;Proc ICML,2016
2. Hybrid Offline-Online Design for UAV-Enabled Data Harvesting in Probabilistic LoS Channels
3. Joint Communication Scheduling and Velocity Control in Multi-UAV-Assisted Sensor Networks: A Deep Reinforcement Learning Approach
4. Three-Dimension Trajectory Design for Multi-UAV Wireless Network With Deep Reinforcement Learning
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