… reinforcement learning (MADRL) algorithm named Soft Deep Recurrent Graph Network (SDRGN),
which could learn a … years, Adhoc network for multi-UAV deployment, known as Flying …

… Flying Ad Hoc NETworks (FANETs) have emerged as a promising technology derived from
Mobile Ad Hoc … “A survey on applications of reinforcement learning in flying ad-hoc networks,” …

… frameworks and network models for flying ad hoc networks: a … integrated aeronautical ad
hoc networks relying on real flight data in … for delay-tolerant aeronautical ad hoc network,” IEEE …

… Flying Ad-hoc Networks (FANETs) are becoming increasingly popular for … Reinforcement
Learning, which introduces a novel UAV mobility prediction algorithm using Deep Learning (DL-…

… in this research work for flying ad-hoc networks. Most of the existing research models are
focused over mobility in flying ad-hoc networks and not considered other parameters like …

… In this paper, we propose a topology maintenance algorithm suitable for high-speed flying
ad hoc networks. By amalgamating the motion states and remaining energy of neighboring …

… Q-learning is a type of reinforcement learning algorithm used … We are introducing Flying Ad
Hoc Networks (FANET) to … , Q learning-based area coverage is obtained from the flying drone …

… the development of flying ad hoc network (FANET) technology. In a network environment with
… In the method described in this section, the entire ad hoc network is constructed as a multi…

… ) protocol and a Self-learning Routing Protocol based on Reinforcement Learning (RLSRP)
[… Markov decision process and uses reinforcement learning to find the shortest path between …

… task-driven networking framework for Flying Ad-hoc Networks (… To capture the task-driven
requirement of the flying multi-agent … Machine learning, including deep reinforcement learning …