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.
Poster: Simulator for Reinforcement Learning-Based Resource Management in Vehicular Edge Computing
2025-06-02
217118 byte
Conference paper
Electronic Resource
English
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