UniTSA: A universal reinforcement learning framework for V2X traffic signal control
Traffic congestion is a persistent problem in urban areas, which calls for the development of
effective traffic signal control (TSC) systems. While existing Reinforcement Learning (RL) …
effective traffic signal control (TSC) systems. While existing Reinforcement Learning (RL) …
A general scenario-agnostic reinforcement learning for traffic signal control
Reinforcement learning (RL) can automatically learn a better policy through a trial-and-error
paradigm and has been adopted to revolutionize and optimize traditional traffic signal …
paradigm and has been adopted to revolutionize and optimize traditional traffic signal …
Microscopic model-based rl approaches for traffic signal control generalize better than model-free rl approaches
There have been many recent advances in the Traffic Signal Control literature that use
reinforcement learning, most of which is undertaken using the model-free approach …
reinforcement learning, most of which is undertaken using the model-free approach …
Reinforcement learning for traffic signal control: Incorporating a virtual mesoscopic model for depicting oversaturated traffic conditions
H Lee, Y Han, Y Kim - Engineering Applications of Artificial Intelligence, 2023 - Elsevier
Recently, with increasing urban traffic congestion, there has been an upsurge in studies on
reinforcement learning for traffic signal control (RL-TSC), which enables efficient traffic …
reinforcement learning for traffic signal control (RL-TSC), which enables efficient traffic …
Oam: An option-action reinforcement learning framework for universal multi-intersection control
Efficient traffic signal control is an important means to alleviate urban traffic congestion.
Reinforcement learning (RL) has shown great potentials in devising optimal signal plans …
Reinforcement learning (RL) has shown great potentials in devising optimal signal plans …
Efficient pressure: Improving efficiency for signalized intersections
Since conventional approaches could not adapt to dynamic traffic conditions, reinforcement
learning (RL) has attracted more attention to help solve the traffic signal control (TSC) …
learning (RL) has attracted more attention to help solve the traffic signal control (TSC) …
Toward Efficient Traffic Signal Control: Smaller Network Can Do More
Reinforcement learning (RL)-based traffic signal control (TSC) optimizes signal switches
through RL agents, adapting to intersection updates. Yet, existing RL-based TSC methods …
through RL agents, adapting to intersection updates. Yet, existing RL-based TSC methods …
Toward a thousand lights: Decentralized deep reinforcement learning for large-scale traffic signal control
Traffic congestion plagues cities around the world. Recent years have witnessed an
unprecedented trend in applying reinforcement learning for traffic signal control. However …
unprecedented trend in applying reinforcement learning for traffic signal control. However …
[HTML][HTML] A scalable approach to optimize traffic signal control with federated reinforcement learning
Intelligent Transportation has seen significant advancements with Deep Learning and the
Internet of Things, making Traffic Signal Control (TSC) research crucial for reducing …
Internet of Things, making Traffic Signal Control (TSC) research crucial for reducing …
The real deal: A review of challenges and opportunities in moving reinforcement learning-based traffic signal control systems towards reality
Traffic signal control (TSC) is a high-stakes domain that is growing in importance as traffic
volume grows globally. An increasing number of works are applying reinforcement learning …
volume grows globally. An increasing number of works are applying reinforcement learning …