Meta-reinforcement learning for edge caching in vehicular networks

Abstract

Content caching to local repositories closer to the user can significantly improve the utilization of the backbone network, reduce latency, and improve reliability. Nevertheless, proactive caching in fast varying environments, especially in vehicular networks has many challenges including the change of the popularity of data with time as well as the changing popularity due to the change of requesting vehicles associated with the roadside units (RSUs). Learning techniques, especially reinforcement learning (RL), play a significant role in caching. Nevertheless, faster adaptation to reach the optimal policy with the dynamic nature of vehicular caching is still highly needed. In this paper, we propose a meta-reinforcement learning (Meta-RL) algorithm for proactive caching and caching replacement techniques based on model agnostic meta-learning (MAML) that can learn and adapt to new tasks faster and can improve the overall hit rate. Simulation results show that the proposed meta-RL algorithm improves the efficiency of the content caching system and provides faster convergence. The proposed meta-RL exhibits a particularly superior performance in the case of changing popularity of cached data.