支持V2V移动边缘计算的智能交通DQN

2023 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2023-06-01 DOI:10.1109/SMARTCOMP58114.2023.00048

Xiaoming Guo, Xiao Hong

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引用次数: 0

摘要

介绍了一种面向未来智能交通特殊场景的深度强化学习模型。该场景描述了一个移动边缘计算平台，该平台由一组自组织的互联车辆托管，用于共享计算资源。提出的DQN模型是为了解决计算能力和流量状态之间的权衡问题。结果表明这种权衡的存在，需要在一些领域进行进一步的研究。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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DQN for Smart Transportation Supporting V2V Mobile Edge Computing

The paper introduces a deep reinforcement learning model for a special scenario in future smart transportation. The scenario describes a mobile edge computing platform hosted by a group of self-organized connected vehicles for sharing computation resources. The presented DQN model is to solve the trade-offs between the computing capability and the traffic state. Results show the existence of the trade-off and the need for future research in a few areas.

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2023 IEEE International Conference on Smart Computing (SMARTCOMP)

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