| Authors: | |
| Wenhan Zhan | |
| Chunbo Luo | |
| Jin Wang | |
| Geyong Min | |
| Hancong Duan |
One Sentence Summary:
investigate the computation offloading problem in a typical VEC scenario
Abstract:
-Inspired by mobile edge computing (MEC), vehicular edge computing (VEC) enables vehicle terminals to support resource-hungry on-vehicle applications with significantly lower latency and less energy consumption. In this paper, we investigate the computation offloading problem in a typical VEC scenario, where a vehicle offloads its computation tasks to the VEC servers deployed in the road side unit (RSU) to minimize its long-term user cost. The mobility of the vehicle coupled with the high dynamics of the environment makes the problem particularly difficult. To tackle this challenge, a deep reinforcement learning (DRL) based offloading method is proposed, which approximates the offloading policy (OP) by a deep neural network (DNN) and trains the DNN with the proximal policy optimization (PPO) algorithm without a priori knowledge of the environment dynamics. Extensive simulation experiments and comprehensive comparison with six baseline algorithms demonstrate that it can achieve the lowest user cost in most cases. Index Terms-Mobile edge computing, reinforcement learning, computation offloading, vehicular networks, Internet of things.