Deep Reinforcement Learning-Based Computation Offloading in MEC-Empowered Vehicular Networks

With the development of autonomous driving technology, Multi-Access Edge Computing (MEC) is an effective paradigm to support delay-sensitive applications in vehicular networks. However, achieving the real-time offloading strategy and resource allocation in MEC-empowered vehicular networks becomes a challenge. In this paper, we first formulate an offloading optimization problem to minimize system latency and energy consumption. To obtain the optimal policy in real time, the formulated problem is transformed into a Markov Decision Process (MDP) and then solved by the proposed Attention and Feature Fusion Deep Deterministic Policy Gradient (AFF-DDPG) algorithm, where a multi-head attention mechanism is combined with feature fusion to improve the accuracy of the decision. In addition, the exploration ability and learning efficiency of the AFF-DDPG algorithm are further enhanced by exploiting Ornstein-Uhlenbeck (OU) noise and the priority experience replay mechanism. The simulation results show that the proposed AFF-DDPG algorithm achieves a 9.44 % improvement over the DDPG algorithm.