traffic light control using deep policy-gradient and value-function based reinforcement learning
View/ Open
Full Text
Date
2017-08-11Author
Mousavi, Seyed Sajad
schukat, Michael
Howley, Enda
Metadata
Show full item recordUsage
This item's downloads: 0 (view details)
Cited 91 times in Scopus (view citations)
Recommended Citation
Mousavi, Seyed Sajad; schukat, Michael; Howley, Enda (2017). traffic light control using deep policy-gradient and value-function based reinforcement learning . IET Intelligent Transport Systems 11 (7), 417-423
Published Version
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.
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Ireland
Related items
Showing items related by title, author, creator and subject.
-
Parallel reinforcement learning for traffic signal control
Mannion, Patrick; Duggan, Jim; Howley, Enda (Elsevier BV, 2015-01-01) -
Traffic prediction framework for OpenStreetMap using deep learning based complex event processing and open traffic cameras
Yadav, Piyush; Sarkar, Dipto; Salwala, Dhaval; Curry, Edward (Dagstuhl Research Online Publication Server (DROPS), 2020-09-25)Displaying near-real-time traffic information is a useful feature of digital navigation maps. However, most commercial providers rely on privacy-compromising measures such as deriving location information from cellphones ... -
Influence agenda setting through Twitter for health promotion
Albalawi, Yousef (2016-05-12)We are in what is known as the new media era, and it impacts all dimensions and aspects of people’s lives. Through advanced technology and the internet, new media continues evolving to change people’s lives so that they ...