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
Published:	2015
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/13521/16/Policy%20abstraction%20for%20transfer%20learning%20using%20learning%20vector%20quantization%20in%20reinforcement%20learning%20framework.pdf
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id	my-ump-ir.13521
record_format	uketd_dc
spelling	my-ump-ir.135212021-11-17T01:28:01Z Policy abstraction for transfer learning using learning vector quantization in reinforcement learning framework 2015-08 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 http://umpir.ump.edu.my/id/eprint/13521/ http://umpir.ump.edu.my/id/eprint/13521/16/Policy%20abstraction%20for%20transfer%20learning%20using%20learning%20vector%20quantization%20in%20reinforcement%20learning%20framework.pdf pdf en public phd doctoral Kyushu University Department of Electrical and Electronic Engineering
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
collection	UMPSA Institutional Repository
language	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
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
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
granting_institution	Kyushu University
granting_department	Department of Electrical and Electronic Engineering
publishDate	2015
url	http://umpir.ump.edu.my/id/eprint/13521/16/Policy%20abstraction%20for%20transfer%20learning%20using%20learning%20vector%20quantization%20in%20reinforcement%20learning%20framework.pdf
_version_	1783731986804768768

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

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