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Evolving controllers for simulated car racing using differential evolution

This paper presents an initial approach for creating autonomous controllers for the car racing game using a hybrid technique. The Differential Evolution (DE) algorithm is combined with Feed-forward Artificial Neural Networks (FFANNs) to generate the required intelligent controllers in a well-known c...

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Main Authors: Shi, Jun Long, Chin, Kim On, Teo, Jason Tze Wi, Tan, Tse Guan, Rayner Alfred, Patricia Anthony
Format: Article
Language:English
Published: Penerbit UKM 2013
Online Access:https://eprints.ums.edu.my/id/eprint/19638/1/Evolving%20controllers%20for%20simulated%20car%20racing%20using%20differential%20evolution.pdf
https://eprints.ums.edu.my/id/eprint/19638/
http://ejournal.ukm.my/apjitm/article/view/4291
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spelling my.ums.eprints.196382018-03-29T02:11:30Z https://eprints.ums.edu.my/id/eprint/19638/ Evolving controllers for simulated car racing using differential evolution Shi, Jun Long Chin, Kim On Teo, Jason Tze Wi Tan, Tse Guan Rayner Alfred Patricia Anthony This paper presents an initial approach for creating autonomous controllers for the car racing game using a hybrid technique. The Differential Evolution (DE) algorithm is combined with Feed-forward Artificial Neural Networks (FFANNs) to generate the required intelligent controllers in a well-known car racing game, namely The Open Racing Car Simulator (TORCS). TORCS is used as a platform in most of the IEEE conference competitions. The main objective of this research is to test the feasibility of the DE implementation in TORCS platform. The literature showed that the application of DE in Real Time Strategy game returned promising results in evolving the required strategy gaming controllers. Interestingly, there is still no study thus far that has been conducted in applying DE into TORCS game platform. This research result shows that DE performed well in TORCS even though a very simple fitness function was used. This indicates that DE has well-tuned the neural network weights to generate optimal and sub-optimal controllers in TORCS. Penerbit UKM 2013 Article PeerReviewed text en https://eprints.ums.edu.my/id/eprint/19638/1/Evolving%20controllers%20for%20simulated%20car%20racing%20using%20differential%20evolution.pdf Shi, Jun Long and Chin, Kim On and Teo, Jason Tze Wi and Tan, Tse Guan and Rayner Alfred and Patricia Anthony (2013) Evolving controllers for simulated car racing using differential evolution. Asia-Pacific Journal of Information Technology and Multimedia, 2 (1). pp. 57-68. ISSN 2289-2192 http://ejournal.ukm.my/apjitm/article/view/4291
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
description This paper presents an initial approach for creating autonomous controllers for the car racing game using a hybrid technique. The Differential Evolution (DE) algorithm is combined with Feed-forward Artificial Neural Networks (FFANNs) to generate the required intelligent controllers in a well-known car racing game, namely The Open Racing Car Simulator (TORCS). TORCS is used as a platform in most of the IEEE conference competitions. The main objective of this research is to test the feasibility of the DE implementation in TORCS platform. The literature showed that the application of DE in Real Time Strategy game returned promising results in evolving the required strategy gaming controllers. Interestingly, there is still no study thus far that has been conducted in applying DE into TORCS game platform. This research result shows that DE performed well in TORCS even though a very simple fitness function was used. This indicates that DE has well-tuned the neural network weights to generate optimal and sub-optimal controllers in TORCS.
format Article
author Shi, Jun Long
Chin, Kim On
Teo, Jason Tze Wi
Tan, Tse Guan
Rayner Alfred
Patricia Anthony
spellingShingle Shi, Jun Long
Chin, Kim On
Teo, Jason Tze Wi
Tan, Tse Guan
Rayner Alfred
Patricia Anthony
Evolving controllers for simulated car racing using differential evolution
author_facet Shi, Jun Long
Chin, Kim On
Teo, Jason Tze Wi
Tan, Tse Guan
Rayner Alfred
Patricia Anthony
author_sort Shi, Jun Long
title Evolving controllers for simulated car racing using differential evolution
title_short Evolving controllers for simulated car racing using differential evolution
title_full Evolving controllers for simulated car racing using differential evolution
title_fullStr Evolving controllers for simulated car racing using differential evolution
title_full_unstemmed Evolving controllers for simulated car racing using differential evolution
title_sort evolving controllers for simulated car racing using differential evolution
publisher Penerbit UKM
publishDate 2013
url https://eprints.ums.edu.my/id/eprint/19638/1/Evolving%20controllers%20for%20simulated%20car%20racing%20using%20differential%20evolution.pdf
https://eprints.ums.edu.my/id/eprint/19638/
http://ejournal.ukm.my/apjitm/article/view/4291
_version_ 1760229607821279232
score 13.211869

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