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arXiv 提交日期: 2026-04-02
📄 Abstract - Systematic Analyses of Reinforcement Learning Controllers in Signalized Urban Corridors

In this work, we extend our systematic capacity region perspective to multi-junction traffic networks, focussing on the special case of an urban corridor network. In particular, we train and evaluate centralized, fully decentralized, and parameter-sharing decentralized RL controllers, and compare their capacity regions and ATTs together with a classical baseline MaxPressure controller. Further, we show how the parametersharing controller may be generalised to be deployed on a larger network than it was originally trained on. In this setting, we show some initial findings that suggest that even though the junctions are not formally coordinated, traffic may self organise into `green waves'.

顶级标签: reinforcement learning systems agents
详细标签: traffic control multi-agent rl urban corridors capacity region decentralized control 或 搜索:

信号控制城市交通走廊中强化学习控制器的系统分析 / Systematic Analyses of Reinforcement Learning Controllers in Signalized Urban Corridors


1️⃣ 一句话总结

这项研究系统性地比较了集中式、分散式等不同强化学习交通信号控制器在模拟城市道路网络中的性能,发现即使没有正式协调,分散式控制器也能让车流自发形成‘绿波’,并展示了其向更大路网扩展的潜力。

源自 arXiv: 2604.02025