中圖分類號(hào)： TP311
文獻(xiàn)標(biāo)識(shí)碼： A
DOI：10.16157/j.issn.0258-7998.212133
中文引用格式： 高宇豆，黃祖源，王海燕，等. 一種基于深度強(qiáng)化學(xué)習(xí)的任務(wù)卸載方法[J].電子技術(shù)應(yīng)用，2022，48(8)：29-33.
英文引用格式： Gao Yudou，Huang Zuyuan，Wang Haiyan，et al. Task offloading based on deep reinforcement learning for Internet of Vehicles[J]. Application of Electronic Technique，2022，48(8)：29-33.

Task offloading based on deep reinforcement learning for Internet of Vehicles

Abstract： With the rapid development of Internet of Vehicular, more and more vehicles′ applications are computation-intensive and delay-sensitive. Resource-constrained vehicles cannot provide the required amount of computation and storage resources for these applications. Edge computing(EC) is expected to be a promising solution to meet the demand of low latency by providing computation and storage resources to vehicles at the network edge. This computing paradigm of offloading tasks to the edge servers can not only overcome the restrictions of limited capacity on vehicles，but also avoid the high latency caused by offloading tasks to the remote cloud. In this paper, an efficient task offloading algorithm based on deep reinforcement learning is proposed to minimize the average completion time of applications. Firstly, the multi-task offloading strategy problem is formalized as an optimization problem. Secondly, a deep reinforcement learning is leveraged to obtain an optimized offloading strategies with the lowest completion time. Finally, the experimental results show that the performance of the proposed algorithm is better than other baselines.

Key words : task offloading；Internet of Vehicles；edge computing；deep learning；reinforcement learning