Systematic Analyses of Reinforcement Learning Controllers in Signalized Urban Corridors
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915909446139904 |
|---|---|
| author | Song, Xiaofei Eder, Kerstin Lawry, Jonathan Wilson, R. Eddie |
| author_facet | Song, Xiaofei Eder, Kerstin Lawry, Jonathan Wilson, R. Eddie |
| contents | 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'. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_02025 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Systematic Analyses of Reinforcement Learning Controllers in Signalized Urban Corridors Song, Xiaofei Eder, Kerstin Lawry, Jonathan Wilson, R. Eddie Artificial Intelligence Machine Learning Multiagent Systems Systems and Control 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'. |
| title | Systematic Analyses of Reinforcement Learning Controllers in Signalized Urban Corridors |
| topic | Artificial Intelligence Machine Learning Multiagent Systems Systems and Control |
| url | https://arxiv.org/abs/2604.02025 |