… works that integrated reinforcement and deep reinforcement learning algorithms for
vehicular networks management with an emphasis on vehicular telecommunications issues. …

… work for MEC-enabled vehicular networks to provide low-latency and reliable computing
services. To overcome the complexity brought by the dynamic network topology, we propose a …

… In this paper, we study how to decide the traffic signals’ duration based on the collected
data from different sensors and vehicular networks. We propose a deep reinforcement learning …

… , and efficient vehicular network. Using the recent advances in training deep neural networks,
we exploit the deep reinforcement learning model, namely deep Q-network, which learns a …

… The background on reinforcement learning is presented in Section III. In Section IV, we … In
Section V, we define the deep reinforcement learning model of vehicular networks. Section VI …

… -based vehicular network architecture with one MEC server to support different vehicular …
Through enabling computing and storing capabilities at the edge of the core network, the MEC …

… In our work we consider channel allocation in vehicular networks based on their SMF and
its priority. SMF is calculated based on their mobility rate and geographic position of vehicles. …

… 1, we consider a terrestrial vehicular network with a medium density of vehicles. Traditionally,
such a network suffers from frequent disconnections that disrupt communications between …

… networks and vehicular networks and the deep reinforcement learning methods used in these
networks. … Task offloading in cellular networks and vehicular networks There are extensive …

… a MEC-enabled vehicular network with n BSs along … vehicle selects the BS providing the
strongest average signal strength as its serving BS. While the serving MEC server of the vehicle …