NDLI: Traffic light control using deep policy-gradient and value-function-based reinforcement learning

Content Provider	IET Digital Library
Author	Mousavi, Seyed Sajad Schukat, Michael Howley, Enda
Abstract	Recent advances in combining deep neural network architectures with reinforcement learning (RL) techniques have shown promising potential results in solving complex control problems with high-dimensional state and action spaces. Inspired by these successes, in this study, the authors built two kinds of RL algorithms: deep policy-gradient (PG) and value-function-based agents which can predict the best possible traffic signal for a traffic intersection. At each time step, these adaptive traffic light control agents receive a snapshot of the current state of a graphical traffic simulator and produce control signals. The PG-based agent maps its observation directly to the control signal; however, the value-function-based agent first estimates values for all legal control signals. The agent then selects the optimal control action with the highest value. Their methods show promising results in a traffic network simulated in the simulation of urban mobility traffic simulator, without suffering from instability issues during the training process.
Starting Page	417
Ending Page	423
Page Count	7
ISSN	1751956X
Volume Number	11
e-ISSN	17519578
Issue Number	Issue 7, Sep (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/11/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2017.0153
Journal	IET Intelligent Transport Systems
Publisher Date	2017-08-11
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Action Space Adaptive Control Adaptive Traffic Light Control Agents Complex Control Problem Control Engineering Computing Control Signal Deep Neural Network Architectures Deep Policy-gradient RL Algorithm Digital Simulation Gradient Method Graphical Traffic Simulator High-dimensional State Space Knowledge Engineering Technique Learning in AI Optimal Control Optimisation Technique PG-based Agent Maps Road Traffic Control Road Traffic System Control Self Adjusting Control System Traffic Engineering Computing Traffic Intersection Traffic Light Control Traffic Signal Training Process Urban Mobility Traffic Simulator Value-function-based Agent RL Algorithm Value-function-based Reinforcement Learning
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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