Reinforcement learning-based route guidance system with dynamic traffic condition

This paper proposes a method of using reinforcement learning to solve dynamic route planning problems, and the change from static learning rate to dynamic learning rate is capable of dealing with emergent congestion. Firstly, some conventional algorithms and reinforcement learning methods are introd...

Full description

Saved in:

Bibliographic Details
Main Author:	Li, Yuzhen
Other Authors:	Wang Dan Wei
Format:	Thesis-Master by Coursework
Language:	English
Published:	Nanyang Technological University 2022
Subjects:	Engineering::Electrical and electronic engineering
Online Access:	https://hdl.handle.net/10356/155442
Tags:	Add Tag No Tags, Be the first to tag this record!
Institution:	Nanyang Technological University
Language:	English

id	sg-ntu-dr.10356-155442
record_format	dspace
spelling	sg-ntu-dr.10356-1554422023-07-04T16:14:51Z Reinforcement learning-based route guidance system with dynamic traffic condition Li, Yuzhen Wang Dan Wei School of Electrical and Electronic Engineering EDWWANG@ntu.edu.sg Engineering::Electrical and electronic engineering This paper proposes a method of using reinforcement learning to solve dynamic route planning problems, and the change from static learning rate to dynamic learning rate is capable of dealing with emergent congestion. Firstly, some conventional algorithms and reinforcement learning methods are introduced in chapter 2. Chapter 3 will discuss the software tool used for creating environment simulation and some basis of reinforcement learning. Then, the comparison of conventional reinforcement learning and proposed method is shown in detail on chapter 4. Lastly, chapter 6 is aimed at discussing some problems of proposed method and future works how to solve this problem. Master of Science (Computer Control and Automation) 2022-02-25T02:49:15Z 2022-02-25T02:49:15Z 2021 Thesis-Master by Coursework Li, Y. (2021). Reinforcement learning-based route guidance system with dynamic traffic condition. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/155442 https://hdl.handle.net/10356/155442 en application/pdf Nanyang Technological University
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
language	English
topic	Engineering::Electrical and electronic engineering
spellingShingle	Engineering::Electrical and electronic engineering Li, Yuzhen Reinforcement learning-based route guidance system with dynamic traffic condition
description	This paper proposes a method of using reinforcement learning to solve dynamic route planning problems, and the change from static learning rate to dynamic learning rate is capable of dealing with emergent congestion. Firstly, some conventional algorithms and reinforcement learning methods are introduced in chapter 2. Chapter 3 will discuss the software tool used for creating environment simulation and some basis of reinforcement learning. Then, the comparison of conventional reinforcement learning and proposed method is shown in detail on chapter 4. Lastly, chapter 6 is aimed at discussing some problems of proposed method and future works how to solve this problem.
author2	Wang Dan Wei
author_facet	Wang Dan Wei Li, Yuzhen
format	Thesis-Master by Coursework
author	Li, Yuzhen
author_sort	Li, Yuzhen
title	Reinforcement learning-based route guidance system with dynamic traffic condition
title_short	Reinforcement learning-based route guidance system with dynamic traffic condition
title_full	Reinforcement learning-based route guidance system with dynamic traffic condition
title_fullStr	Reinforcement learning-based route guidance system with dynamic traffic condition
title_full_unstemmed	Reinforcement learning-based route guidance system with dynamic traffic condition
title_sort	reinforcement learning-based route guidance system with dynamic traffic condition
publisher	Nanyang Technological University
publishDate	2022
url	https://hdl.handle.net/10356/155442
_version_	1772827712317030400

Reinforcement learning-based route guidance system with dynamic traffic condition

Similar Items