Deep Reinforcement Learning for Vehicular Edge Computing

TLDR

Smart vehicles promise comfort and safety, yet running computing‑intensive applications on resource‑constrained vehicles remains a major challenge. The authors aim to develop an intelligent offloading system for vehicular edge computing powered by deep reinforcement learning. They model communication and computation as finite Markov chains, formulate a joint QoE‑maximizing optimization, decompose it into two sub‑problems, and solve scheduling with a two‑sided matching scheme while allocating resources via deep reinforcement learning. Experiments demonstrate the proposed system’s effectiveness and superiority over existing approaches.

Abstract

The development of smart vehicles brings drivers and passengers a comfortable and safe environment. Various emerging applications are promising to enrich users’ traveling experiences and daily life. However, how to execute computing-intensive applications on resource-constrained vehicles still faces huge challenges. In this article, we construct an intelligent offloading system for vehicular edge computing by leveraging deep reinforcement learning. First, both the communication and computation states are modelled by finite Markov chains. Moreover, the task scheduling and resource allocation strategy is formulated as a joint optimization problem to maximize users’ Quality of Experience (QoE). Due to its complexity, the original problem is further divided into two sub-optimization problems. A two-sided matching scheme and a deep reinforcement learning approach are developed to schedule offloading requests and allocate network resources, respectively. Performance evaluations illustrate the effectiveness and superiority of our constructed system.