We investigate cellular networks supported by on-street parked vehicles, termed as Vehicular Relay Nodes (VeRN), which have been proposed to boost performance and scale the network to varying demands. We study the problems of Call Admission and Call Assignment, which are key to efficient operation. To achieve an efficient and practical solution, we propose to decouple this problem and solve it via two weakly-coupled algorithms. For Admission, we formulate a non-trivial Markov Decision Process, introducing a dynamic operator that stochastically accounts for the possible assignments. For the Assignment problem, we derive a local Deep Reinforcement Learning algorithm using Imitation Learning, while introducing several novel improvements. Performance evaluation shows that these strategies offer a significant improvement over baselines.