Model-Based Graph Reinforcement Learning for Inductive Traffic Signal Control
We introduce MuJAM, an adaptive traffic signal control method which leverages model-based reinforcement learning to 1) extend recent generalization efforts (to road network architectures and traffic distributions) further by allowing a generalization to the controllers’ constraints (cycli...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Open Journal of Intelligent Transportation Systems |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10470423/ |
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| Summary: | We introduce MuJAM, an adaptive traffic signal control method which leverages model-based reinforcement learning to 1) extend recent generalization efforts (to road network architectures and traffic distributions) further by allowing a generalization to the controllers’ constraints (cyclic and acyclic policies), 2) improve performance and data efficiency over related model-free approaches, and 3) enable explicit coordination at scale for the first time. In a zero-shot transfer setting involving both road networks and traffic settings never experienced during training, and in a larger transfer experiment involving the control of 3,971 traffic signal controllers in Manhattan, we show that MuJAM, using both cyclic and acyclic constraints, outperforms domain-specific baselines as well as a recent transferable approach. |
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| ISSN: | 2687-7813 |