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This paper trains and evaluates centralized, fully decentralized, and parameter-sharing decentralized reinforcement learning (RL) controllers for traffic signal control in urban corridors. The study compares the capacity regions and average travel times (ATTs) of these RL controllers against a MaxPressure baseline. Results indicate that parameter-sharing controllers can generalize to larger networks and may induce self-organized "green waves," even without explicit coordination.
Parameter-sharing RL controllers can generalize to larger traffic networks and induce self-organized "green waves," suggesting a path to scalable and adaptive traffic management.
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'.
