This study proposes and develops two reinforcement learning adaptive traffic signal controllers, analyzes their learned policies, and compares them to a ...
This study proposes and develops two reinforcement learning adaptive traffic signal controllers, analyzes their learned policies, and compares them to a ...
Aug 28, 2024 · We evaluate the performance of the algorithm by using seven different evaluation metrics. The recent researches focused on either phase ...
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Sep 10, 2024 · In this paper, we propose a solution to traffic congestion using reinforcement learning. We define the state as a scalar representing the queue length.
Oct 15, 2023 · In this work, the teacher-student framework is used for traffic signal control, where only a single reward function is designed to guide the student agent.
Apr 22, 2020 · This study proposes and develops two reinforcement learning adaptive traffic signal controllers, analyzes their learned policies, and compares ...
Our project focuses on implementing a learning algorithm that will allow traffic control devices to study traffic patterns/behaviors for a given intersection.
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Dec 11, 2015 · The motivation of this study is to design, simulate and analyze an adaptive traffic signal control system that can out-perform the legacy ...
Sep 1, 2024 · This paper explores the use of Reinforcement Learning (RL) to enhance traffic signal operations at intersections, aiming to reduce congestion without extensive ...
We propose three different architectures for TSC RL agents and compare them against the currently used commercial systems MOVA, SurTrac and Cyclic controllers.
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