Traffic Light Control Using Hierarchical Reinforcement Learning and Options Framework

IEEE Access

Joao Paulo R. R. Leite¹  Edmilson M. Moreira²  Dimitrius F. Borges³  Otavio A. S. Carpinteiro³ 

[1] , Itajub&x00E1;, Minas Gerais, Brazil;Institute of Systems Engineering and Information Technology, Federal University of Itajub&x00E1;

关键词: Intelligent systems;    machine learning;    reinforcement learning;    simulation;    traffic control;   

DOI  :  10.1109/ACCESS.2021.3096666

来源: DOAJ

【 摘 要 】

The number of vehicles worldwide has grown rapidly over the past decade, impacting how urban traffic is managed. Traffic light control is a well-known problem and, although an increasing number of technologies are used to solve it, it still poses challenges and opportunities, especially when considering the inefficiency of the popular fixed-time traffic controllers. This study aims to apply Hierarchical Reinforcement Learning (HRL) and Options Framework to control a signalized vehicular intersection and compare its performance with that of a fixed-time traffic controller, configured using the Webster Method. HRL combines the ability to learn and make decisions while taking observations from the environment in real-time. These capabilities bring a significant adaptive power to a highly dynamic problem. The test scenarios were built using the SUMO simulation tool. According to our results, HRL presents better performance than those of its own isolated sub-policies and the fixed-time model, indicating a simple and efficient alternative.

【 授权许可】

Unknown