Poster: Simulator for Reinforcement Learning-Based Resource Management in Vehicular Edge Computing

We aim to investigate Reinforcement Learning (RL) methods for efficient resource management in Vehicular Edge Computing (VEC). To this end, we present an open-source, modular, lightweight, discrete-event simulation framework which integrates state-of-the-art tools for improved performance evaluation. By integrating realistic mobility traces, our approach presents an opportunity to evaluate the performance and scalability of different RL-based task scheduling and resource allocation policies in diverse scenarios. This offers flexibility and insights into the generalizability of RL-based scheduling policies. We make the framework available as open-source to foster broader accessibility, support research in the field. We present early results to demonstrate the potential of this simulator.