… mobility vehicular networks, where multiple V2V links attempt to share the frequency spectrum
preoccupied by V2I links. To support diverse service requirements in vehicular networks, …

… Specifically, a convolutional neural network (… vehicular networks to provide low-latency
and reliable computing services. To overcome the complexity brought by the dynamic network …

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

… 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 …

… a multi-objective reinforcement learning strategy, called … collaborative vehicle selection is
used to find the target vehicle … allocation uses the reinforcement learning to achieve the optimal …

… reinforcement learning (MARL) with the vehicular networks and how it improves the performance
of the vehicular networks … non-stationary environment of vehicular networks that find an …

… 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. …

… 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 …

… packet transmissions for all vehicle user equipment-pairs (… reinforcement learning techniques
to address the partial observability and the curse of high dimensionality in local network …

… 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 …