… Channel selection is a challenging task in cognitive radio vehicular networks. Vehicles have
to … For this purpose, a deep reinforcement learning algorithm namely deep reinforcement …

… 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 …

… access rates of secondary vehicles in Cognitive Vehicular Networks, where the channel
capacity … In this function, a Deep Q Network method with a modified reward function (IDQN) is …

… We consider a cognitive-enabled vehicular communication network withNR RSUs along
the road as shown in Fig. 1. AllNR RSUs and their users under an MBS can communicate …

… scheme for cognitive radio-enabled vehicular ad hoc networks (CR… as a cognitive radio-enabled
vehicular ad hoc networks (CR-… QoS of SVs in a deep Q-network method with a modified …

… of a connected vehicular network using deep reinforcement learning,” … Juang, “Deep
reinforcement learning based resource … in vehicular networks based on multi-agent reinforcement …

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

… data transmission in cognitive vehicular networks. By integrating a … -enabled vehicular
networks, by jointly considering cognitive … vehicles using a deep reinforcement learning approach. …

… long short-term memory and deep reinforcement learning techniques to address the partial
observability and the curse of high dimensionality in local network state space faced by each …

… to enhance network performance. In this article, a multi-agent deep reinforcement learning-…
(QoS) requirements in heterogeneous vehicular networks. In the proposed framework, the …