# VRLS: A Unified Reinforcement Learning Scheduler for Vehicle-to-Vehicle Communications

## TL;DR

VRLS is a reinforcement learning-based scheduler designed for vehicle-to-vehicle communications that predicts resource allocation, adapts across environments, and improves collision avoidance and resource utilization.

## Contribution

It introduces a unified RL scheduler for V2V communications that works across various environments and enables transfer learning for practical deployment.

## Key findings

- VRLS outperforms existing schedulers in collision avoidance.
- Pre-trained VRLS adapts to new environments with limited retraining.
- VRLS reduces resource wastage and improves efficiency.

## Abstract

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.