基于深度强化学习的车联网任务卸载研究

Yaoping Zeng, Yanwei Hu, Ting Yang
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引用次数: 0

摘要

移动边缘计算(MEC)是一项很有前途的技术,可以促进车联网(IoV)环境下的计算卸载和资源分配。当移动设备无法满足自身数据处理需求时,将任务卸载给MEC服务器,可以有效缓解网络压力,满足多任务计算需求,保证服务质量(QoS)。通过多用户和多mec服务器,提出了基于改进深度强化学习策略(IDRLP)的Q-Learning任务卸载策略,以获得任务卸载和资源分配的最优策略。仿真结果表明,即使在不同的任务、用户和数据大小下,与其他基准方案相比,所提出的算法在延迟、能耗和系统加权代价方面具有更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Task Offloading Based on Deep Reinforcement Learning for Internet of Vehicles
Mobile Edge Computing (MEC) is a promising technology that facilitates the computational offloading and resource allocation in the Internet of Vehicles (IoV) environment. When the mobile device is not capable enough to meet its own demands for data processing, the task will be offloaded to the MEC server, which can effectively relieve the network pressure, meet the multi-task computing requirements, and ensure the quality of service (QoS). Via multi-user and multi-MEC servers, this paper proposes the Q-Learning task offloading strategy based on the improved deep reinforcement learning policy(IDRLP) to obtain an optimal strategy for task offloading and resource allocation. Simulation results suggest that the proposed algorithm compared with other benchmark schemes has better performance in terms of delay, energy consumption and system weighted cost, even with different tasks, users and data sizes.
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