Policy abstraction for transfer learning using learning vector quantization in reinforcement learning framework

People grow up every day exposed to the infinite state space environment interacting with active biological subjects and machines. There are routines that are always expected and unpredicted events that are not completely known beforehand as well. When people interact with the future routines, they...

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Main Author:	Ahmad Afif, Mohd Faudzi
Format:	Thesis
Language:	English English English
Published:	2015
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/13521/ http://umpir.ump.edu.my/id/eprint/13521/ http://umpir.ump.edu.my/id/eprint/13521/1/FKEE%20-%20AHMAD%20AFIF%20MOHD%20FAUDZI.PDF http://umpir.ump.edu.my/id/eprint/13521/2/FKEE%20-%20AHMAD%20AFIF%20MOHD%20FAUDZI%20-%20CHAP%201.PDF http://umpir.ump.edu.my/id/eprint/13521/3/FKEE%20-%20AHMAD%20AFIF%20MOHD%20FAUDZI%20-%20CHAP%203.PDF

id	ump-13521
recordtype	eprints
spelling	ump-135212016-06-24T00:42:05Z http://umpir.ump.edu.my/id/eprint/13521/ Policy abstraction for transfer learning using learning vector quantization in reinforcement learning framework Ahmad Afif, Mohd Faudzi TK Electrical engineering. Electronics Nuclear engineering People grow up every day exposed to the infinite state space environment interacting with active biological subjects and machines. There are routines that are always expected and unpredicted events that are not completely known beforehand as well. When people interact with the future routines, they do not require the same effort as they do during the first time. Based on experience, irrelevant information that does not affect the achievement is ignored. For example, a new worker in his/her first day will carefully recognize the road to his/her office, including the road's name, signboards, and buildings as well as focusing on the traffic. After several months he/she, possibly, will focus only on buildings and traffic. Furthermore, when people interact with an unpredicted event, they will usually try to cope with the situation using their knowledge that is acquired from their past experience. For example, an accident happened and the worker's daily route was jammed, here, he/she will try to find the alternate route based on the distance and the location of his/her office. This shows that people have an ability to benefit from their previous experience and knowledge for the future. Furthermore, the knowledge is not stored in a concrete or very detailed form, but in an abstract form that is ready to be used for routine events and also to be used for assisting in unknown events. Such abilities are obviously acquired through the most significant ability of a human being, which is learning ability from its successes and failures. 2015-08 Thesis NonPeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/13521/1/FKEE%20-%20AHMAD%20AFIF%20MOHD%20FAUDZI.PDF application/pdf en http://umpir.ump.edu.my/id/eprint/13521/2/FKEE%20-%20AHMAD%20AFIF%20MOHD%20FAUDZI%20-%20CHAP%201.PDF application/pdf en http://umpir.ump.edu.my/id/eprint/13521/3/FKEE%20-%20AHMAD%20AFIF%20MOHD%20FAUDZI%20-%20CHAP%203.PDF Ahmad Afif, Mohd Faudzi (2015) Policy abstraction for transfer learning using learning vector quantization in reinforcement learning framework. PhD thesis, Kyushu University. http://iportal.ump.edu.my/lib/item?id=chamo:92980&theme=UMP2
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English English English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Ahmad Afif, Mohd Faudzi Policy abstraction for transfer learning using learning vector quantization in reinforcement learning framework
description	People grow up every day exposed to the infinite state space environment interacting with active biological subjects and machines. There are routines that are always expected and unpredicted events that are not completely known beforehand as well. When people interact with the future routines, they do not require the same effort as they do during the first time. Based on experience, irrelevant information that does not affect the achievement is ignored. For example, a new worker in his/her first day will carefully recognize the road to his/her office, including the road's name, signboards, and buildings as well as focusing on the traffic. After several months he/she, possibly, will focus only on buildings and traffic. Furthermore, when people interact with an unpredicted event, they will usually try to cope with the situation using their knowledge that is acquired from their past experience. For example, an accident happened and the worker's daily route was jammed, here, he/she will try to find the alternate route based on the distance and the location of his/her office. This shows that people have an ability to benefit from their previous experience and knowledge for the future. Furthermore, the knowledge is not stored in a concrete or very detailed form, but in an abstract form that is ready to be used for routine events and also to be used for assisting in unknown events. Such abilities are obviously acquired through the most significant ability of a human being, which is learning ability from its successes and failures.
format	Thesis
author	Ahmad Afif, Mohd Faudzi
author_facet	Ahmad Afif, Mohd Faudzi
author_sort	Ahmad Afif, Mohd Faudzi
title	Policy abstraction for transfer learning using learning vector quantization in reinforcement learning framework
title_short	Policy abstraction for transfer learning using learning vector quantization in reinforcement learning framework
title_full	Policy abstraction for transfer learning using learning vector quantization in reinforcement learning framework
title_fullStr	Policy abstraction for transfer learning using learning vector quantization in reinforcement learning framework
title_full_unstemmed	Policy abstraction for transfer learning using learning vector quantization in reinforcement learning framework
title_sort	policy abstraction for transfer learning using learning vector quantization in reinforcement learning framework
publishDate	2015
url	http://umpir.ump.edu.my/id/eprint/13521/ http://umpir.ump.edu.my/id/eprint/13521/ http://umpir.ump.edu.my/id/eprint/13521/1/FKEE%20-%20AHMAD%20AFIF%20MOHD%20FAUDZI.PDF http://umpir.ump.edu.my/id/eprint/13521/2/FKEE%20-%20AHMAD%20AFIF%20MOHD%20FAUDZI%20-%20CHAP%201.PDF http://umpir.ump.edu.my/id/eprint/13521/3/FKEE%20-%20AHMAD%20AFIF%20MOHD%20FAUDZI%20-%20CHAP%203.PDF
first_indexed	2023-09-18T22:16:15Z
last_indexed	2023-09-18T22:16:15Z
_version_	1777415357041999872

Policy abstraction for transfer learning using learning vector quantization in reinforcement learning framework

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