… Based on V2V communication, content or energy can be shared among vehicles. … network,
we consider a more general and complex scenario where D2D and V2V communication are …

… using deep reinforcement learning (deep-RL) for solving wireless networking problems with
… framework for vehicle-to-vehicle communications is proposed based on deep reinforcement …

… A standard SG is capable of carrying out a variety of tasks requiring the upgrade of both
the communication networks’ measuring and coordination facilities. It is essential to deploy …

… a communication network is a natural application for reinforcement learning algorithms. …
networks, we believe this paper has shown that adaptive routing is a natural domain for …

… the fact that communications among UAVs may not be feasible or reliable. The UAVs learn
the optimal task allocation using a distributed reinforcement learning algorithm. Convergence …

… We consider a backscatter communication network having a source and multiple backscatter
transmitters and receivers, as shown in the Fig. 1. The backscatter devices receive the RF …

… in reinforcement learning (RL), we investigate whether a centralized learning scheduler can
… to vehicles for outof-coverage V2V communication. Specifically, we use the actorcritic RL …

… networks. In this paper, we present a reinforcement 6 learning (RL) based random linear
network … In this 8 scheme, the network coding policy, including the encoded packet 9 number, …

… Reinforcement Learning (MTDRL) approach to reduce the action space of the joint network
… running on a common network infrastructure, ie, a communication network. The substrate …

… communication impairments. In this work, we propose a joint design that combines goal-oriented
communication and networked … joint optimization of communication and control tasks …