An optimized Multi Agent Reinforcement Learning solution for edge caching in the Internet of Vehicles

The Internet of Vehicles has evolved significantly with the integration of intelligent technologies, transforming vehicular networks by enhancing communication, resource management, and decision-making at the network’s edge. With the increasing complexity of vehicular environments and data demands, efficient caching mechanisms have become essential to ensure seamless service delivery and optimized resource usage. In this paper, we present LF-MARLEC, a Leader Follower Multi-Agent Reinforcement Learning solution for Edge Caching within the Internet of Vehicles. Our approach introduces a hierarchical distribution of action importance, enabling more effective decision-making at the network edge. Extensive experiments, conducted using widely adopted simulation tools such as SUMO and Veins, demonstrate that our approach substantially enhances caching performance and overall system efficiency. Specifically, our approach achieves nearly 9% reduction in content distribution delay and over 11% improvement in cache hit rate compared to state-of-the-art methods, thereby enhancing the effectiveness of intelligent edge caching in Internet of Vehicles environments. The source code is publicly available at: https://github.com/amine9008/RL-EDGE-CACHING.