English(EN) Adaptive Multi-Discriminator WGAN Framework for Resource-Constrained Internet of Vehicles Using Reinforcement Learning and Game Theory

新型WGAN框架结合强化学习与博弈论，用于资源受限的车辆

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-09 04:00

研究人员开发了一个自适应多判别器 Wasserstein GAN (MD-WGAN) 框架，专为资源受限的车联网 (IoV) 环境设计。该框架整合了强化学习与博弈论，将机器学习工作负载作为网络服务进行管理，以应对动态拓扑和竞争性优化目标等挑战。该系统利用带有生成器和深度Q网络代理的路边单元来选择判别器并管理分布式训练，而博弈论协调则分配训练周期。在NGSIM轨迹数据上的评估表明，MD-WGAN实现了与最先进GAN相当的预测精度，同时显著提高了资源效率，包括降低了CPU利用率和内存使用量。 AI

影响这项研究可能有助于在互联汽车等移动和资源受限环境中更有效地部署先进的AI模型。

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Farhoud Jafari Kaleibar, Amr M. Zaki, Marin Litoiu · 2026-10-09 04:00

面向资源受限车联网的自适应多判别器WGAN框架：基于强化学习与博弈论

arXiv:2610.10926v1 Announce Type: new Abstract: Managing machine learning workloads as a network service introduces a resource-orchestration problem distinct from conventional model training; which nodes should be allocated to a task, how communication and computation budgets sho…

本文如何被排名

报道来源 [1]

面向资源受限车联网的自适应多判别器WGAN框架：基于强化学习与博弈论

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