Improving the Generalizability and Robustness of Large-Scale Traffic Signal Control

Improving the Generalizability and Robustness of Large-Scale Traffic Signal Control

A number of deep reinforcement-learning (RL) approaches propose to control traffic signals. Compared to traditional approaches, RL approaches can learn from higher-dimensionality input road and vehicle sensors and better adapt to varying traffic conditions resulting in reduced travel times (in simul...

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Bibliographic Details
Main Authors: Tianyu Shi, Francois-Xavier Devailly, Denis Larocque, Laurent Charlin
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Open Journal of Intelligent Transportation Systems
Subjects:
Distributional reinforcement learning
graph neural networks
policy ensemble
robustness
generalizability
traffic signal control
Online Access:https://ieeexplore.ieee.org/document/10315958/

Internet

https://ieeexplore.ieee.org/document/10315958/

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