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Title	Learning Decentralized Traffic Signal Controllers With Multi-Agent Graph Reinforcement Learning
ID_Doc	34880
Authors	Zhang Y.; Yu Z.; Zhang J.; Wang L.; Luan T.H.; Guo B.; Yuen C.
Year	2024
Published	IEEE Transactions on Mobile Computing, 23, 6
DOI	http://dx.doi.org/10.1109/TMC.2023.3332081
Abstract	This paper considers optimal traffic signal control in smart cities, which has been taken as a complex networked system control problem. Given the interacting dynamics among traffic lights and road networks, attaining controller adaptivity and scalability stands out as a primary challenge. Capturing the spatial-temporal correlation among traffic lights under the framework of Multi-Agent Reinforcement Learning (MARL) is a promising solution. Nevertheless, existing MARL algorithms ignore effective information aggregation which is fundamental for improving the learning capacity of decentralized agents. In this paper, we design a new decentralized control architecture with improved environmental observability to capture the spatial-temporal correlation. Specifically, we first develop a topology-aware information aggregation strategy to extract correlation-related information from unstructured data gathered in the road network. Particularly, we transfer the road network topology into a graph shift operator by forming a diffusion process on the topology, which subsequently facilitates the construction of graph signals. A diffusion convolution module is developed, forming a new MARL algorithm, which endows agents with the capabilities of graph learning. Extensive experiments based on both synthetic and real-world datasets verify that our proposal outperforms existing decentralized algorithms. © 2002-2012 IEEE.
Author Keywords	Graph learning; intelligent transportation systems; MARL; traffic signal control

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