… , 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 …

… In this paper, we study a UAV-assisted vehicular network where the UAV … /vehicles and the
state transitions. Secondly, we solve the target problem using a deep reinforcement learning …

… 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 …

… 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, …

… Moreover, the resource allocation policy is designed by considering the vehicle’s … on the
vehicular networks because of their high complexity. We develop a deep reinforcement learning …

… We present an end-to-end framework for solving the Vehicle Routing Problem (VRP)
using reinforcement learning. In this approach, we train a single policy model that finds near-…

… scheme to evaluate the direct trust of each vehicle. Meanwhile, a reinforcement learning
method also is used in this article to compute any vehicle’s indirect trust value. In order to solve …

… solution based on the reinforcement learning technique. The proposed protocol employs a
Q-learning algorithm to determine the efficient intermediate forwarder vehicle and the optimal …

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

… 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 …