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Systematic Analyses of Reinforcement Learning Controllers in Signalized Urban Corridors

Xiaofei Song, Kerstin Eder, Jonathan Lawry, R. Eddie Wilson

Abstract

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'.

Systematic Analyses of Reinforcement Learning Controllers in Signalized Urban Corridors

Abstract

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'.

Paper Structure

This paper contains 4 sections, 2 figures.

Figures (2)

  • Figure 1: Multi-junction corridor simulation set-up
  • Figure 2: (a-d) Capacity regions for MaxPressure and RL controllers. (e) Average travel times (ATT). (f) Zero-stop ratios for $n=13$ junctions, suggesting green wave coordination at $l=700\,$m.