对比多种强化学习控车策略，发现无协调也能形成绿波通行。
Systematic Analyses of Reinforcement Learning Controllers in Signalized Urban Corridors
- 用集中、去中心化及参数共享三种强化学习控制器对比性能
- 参数共享控制器在更大网络上部署仍能实现接近最优的平均行程时间
- 即使未正式协调，交通系统也可能自发形成绿波，适合智能交通研究者
本文将系统的容量区域视角拓展至多交叉口城市走廊网络。我们训练并评估了集中式、完全去中心化以及参数共享的去中心化强化学习控制器，并与经典基准方法MaxPressure控制器一起比较其容量区域和平均行程时间（ATT）。此外，我们展示了参数共享控制器可泛化到比训练时更大的网络。在此设置下，初步发现表明：尽管各路口未正式协调，交通流仍可能自发组织形成‘绿波’现象。
原文摘要 · Abstract (English)
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'.
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