  Vehicle-to-vehicle (V2V) communications have distinct challenges that need to
be taken into account when scheduling the radio resources. Although centralized
schedulers (e.g., located on base stations) could be utilized to deliver high
scheduling performance, they cannot be employed in case of coverage gaps. To
address the issue of reliable scheduling of V2V transmissions out of coverage,
we propose Vehicular Reinforcement Learning Scheduler (VRLS), a centralized
scheduler that predictively assigns the resources for V2V communication while
the vehicle is still in cellular network coverage. VRLS is a unified
reinforcement learning (RL) solution, wherein the learning agent, the state
representation, and the reward provided to the agent are applicable to
different vehicular environments of interest (in terms of vehicular density,
resource configuration, and wireless channel conditions). Such a unified
solution eliminates the necessity of redesigning the RL components for a
different environment, and facilitates transfer learning from one to another
similar environment. We evaluate the performance of VRLS and show its ability
to avoid collisions and half-duplex errors, and to reuse the resources better
than the state of the art scheduling algorithms. We also show that pre-trained
VRLS agent can adapt to different V2V environments with limited retraining,
thus enabling real-world deployment in different scenarios.
