… , Networked MG generalizes the MG framework to model cooperative agents with different
reward functions by leveraging shared information through a communication network (see …

… network edge using a deep reinforcement learning framework with Wolpertinger architecture.
In particular, we propose deep actorcritic reinforcement learning … , communication networks…

… and toward more versatile and diverse network approaches is required. … communication
network, which aims to combine terrestrial and several non-terrestrial communication networks. …

… communication of multiple UAVs with limited bandwidth via spectrum interaction between
UAVs. By introducing reinforcement learning … issues in UAV communication networks,” IEEE …

… communication networks have drawn significant attention from both academia and industry
in the past decade. Conventional vehicular networks … (V2X) communications. In the era of 5G, …

… to surveying the application of reinforcement learning methods in different wireless IoT …
scheduling optimization in a maritime communications network based on Software Defined …

… • We consider a semantic communication network in which a base station (BS) uses
semantic communication techniques to extract the meaning of the text data and transmits it to its …

… in an RIS-assisted cellular network endowed with an RIS … based on deep reinforcement
learning is proposed, in which … shift configuration using a neural network. Due to the intractability …

… who want to start working in reinforcement learning. The remaining document is organized
as … many challenges and problems related to communication and networking [152]. Modern …

… and Software-Defined Networking Intelligent Routing (RSIR). … routing algorithm based on
Reinforcement Learning (RL) that … networks through sdn spf protocol computer communications …