Adaptive Resource Allocation for Mobile Edge Computing in Internet of Vehicles: A Deep Reinforcement Learning Approach

Mobile edge computing (MEC) has emerged in recent years as an effective solution to the challenge of limited vehicle resources in the Internet of Vehicles (IoV), especially for computation-intensive vehicle tasks. This paper investigates a multi-user MEC system with an active task model in high-dynamic IoV scenarios. To improve the MEC performance regarding system capacity, task service delay, and energy consumption, we design an adaptive joint resource allocation scheme based on deep reinforcement learning (DRL), which includes uplink, computing, and downlink resource allocation. Further, a multi-actor parallel twin delayed deep deterministic policy gradient (MAPTD3) algorithm is devised to jointly and adaptively optimize these strategies during each time slot. Finally, numerical results demonstrate that the proposed adaptive joint resource allocation scheme improves system performance significantly while satisfying task delay and system resource constraints. In addition, the space complexity of the designed optimization algorithm is lower than that of conventional DRL algorithms.