Distributed Multi-Agent Reinforcement Learning for Cooperative Low-Carbon Control of Traffic Network Flow Using Cloud-Based Parallel Optimization

IF 7.9 1区工程技术 Q1 ENGINEERING, CIVIL IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-09-10 DOI:10.1109/tits.2024.3452430

Yongnan Zhang, Yonghua Zhou, Hamido Fujita

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利用基于云的并行优化实现交通网络流量的分布式多代理强化学习低碳协同控制

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IEEE Transactions on Intelligent Transportation Systems 工程技术-工程：电子与电气

CiteScore

14.80

自引率

12.90%

发文量

1872

审稿时长

7.5 months

期刊介绍： The theoretical, experimental and operational aspects of electrical and electronics engineering and information technologies as applied to Intelligent Transportation Systems (ITS). Intelligent Transportation Systems are defined as those systems utilizing synergistic technologies and systems engineering concepts to develop and improve transportation systems of all kinds. The scope of this interdisciplinary activity includes the promotion, consolidation and coordination of ITS technical activities among IEEE entities, and providing a focus for cooperative activities, both internally and externally.

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