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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.

Traffic Light Control Using Hierarchical Reinforcement Learning and Options Framework