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Learning IoV in Edge: Deep Reinforcement Learning for Edge Computing Enabled Vehicular Networks

The development of artificial intelligence, wireless communication and smart sensor platform facilities the emergence of multitude of novel vehicular applications in recent years. These new vehicular applications usually are realized with BIG models, which are normally delay sensitive and computation intensive. To alleviate the heavy pressure on the resource-constrained vehicles, computation offloading has been regarded as a promising approach to circumvent this challenge. In this paper, we take the deep learning model as an BIG model example to investigate the computation offloading in a vehicular cloud network. Specifically, task division technology is utilized to decomposed the BIG model into several components, in which there are dependencies between multiple components. To satisfy the requirements of delay-sensitive vehicular applications, it is crucial to propose an efficient computation offloading scheme. To solve it, a novel deep reinforcement learning algorithm is proposed, wherein a common deep learning model is maintained by all agents. The reward mechanism is elaborately designed to combine the long-term reward and short-term reward. In the final, the proposed algorithm’s effectiveness is verified by the experimental simulations.