Hierarchical extreme learning machine based reinforcement learning for goal localization

The objective of goal localization is to find the location of goals in noisy environments. Simple actions are performed to move the agent towards the goal. The goal detector should be capable of minimizing the error between the predicted locations and the true ones. Few regions need to be process...

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Main Authors:	AlDahoul, Nouar, Htike, Zaw Zaw, Akmeliawati, Rini
Format:	Conference or Workshop Item
Language:	English English
Published:	IOP Publishing 2017
Subjects:	T Technology (General) TL500 Aeronautics
Online Access:	http://irep.iium.edu.my/54838/ http://irep.iium.edu.my/54838/ http://irep.iium.edu.my/54838/ http://irep.iium.edu.my/54838/2/54838-edited.pdf http://irep.iium.edu.my/54838/1/54838-Hierarchical%20extreme%20learning%20machine%20based%20reinforcement%20learning%20for%20goal%20localization_SCOPUS.pdf

id	iium-54838
recordtype	eprints
spelling	iium-548382017-06-05T02:40:27Z http://irep.iium.edu.my/54838/ Hierarchical extreme learning machine based reinforcement learning for goal localization AlDahoul, Nouar Htike, Zaw Zaw Akmeliawati, Rini T Technology (General) TL500 Aeronautics The objective of goal localization is to find the location of goals in noisy environments. Simple actions are performed to move the agent towards the goal. The goal detector should be capable of minimizing the error between the predicted locations and the true ones. Few regions need to be processed by the agent to reduce the computational effort and increase the speed of convergence. In this paper, reinforcement learning (RL) method was utilized to find optimal series of actions to localize the goal region. The visual data, a set of images, is high dimensional unstructured data and needs to be represented efficiently to get a robust detector. Different deep Reinforcement models have already been used to localize a goal but most of them take long time to learn the model. This long learning time results from the weights fine tuning stage that is applied iteratively to find an accurate model. Hierarchical Extreme Learning Machine (H-ELM) was used as a fast deep model that doesn’t fine tune the weights. In other words, hidden weights are generated randomly and output weights are calculated analytically. H-ELM algorithm was used in this work to find good features for effective representation. This paper proposes a combination of Hierarchical Extreme learning machine and Reinforcement learning to find an optimal policy directly from visual input. This combination outperforms other methods in terms of accuracy and learning speed. The simulations and results were analysed by using MATLAB. IOP Publishing 2017 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/54838/2/54838-edited.pdf application/pdf en http://irep.iium.edu.my/54838/1/54838-Hierarchical%20extreme%20learning%20machine%20based%20reinforcement%20learning%20for%20goal%20localization_SCOPUS.pdf AlDahoul, Nouar and Htike, Zaw Zaw and Akmeliawati, Rini (2017) Hierarchical extreme learning machine based reinforcement learning for goal localization. In: 3rd International Conference on Mechanical, Automotive and Aerospace Engineering 2016 (ICMAAE’16), 25th-27th July 2016, Kuala Lumpur, Malaysia. http://iopscience.iop.org/article/10.1088/1757-899X/184/1/012055/pdf 10.1088/1757-899X/184/1/012055
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	T Technology (General) TL500 Aeronautics
spellingShingle	T Technology (General) TL500 Aeronautics AlDahoul, Nouar Htike, Zaw Zaw Akmeliawati, Rini Hierarchical extreme learning machine based reinforcement learning for goal localization
description	The objective of goal localization is to find the location of goals in noisy environments. Simple actions are performed to move the agent towards the goal. The goal detector should be capable of minimizing the error between the predicted locations and the true ones. Few regions need to be processed by the agent to reduce the computational effort and increase the speed of convergence. In this paper, reinforcement learning (RL) method was utilized to find optimal series of actions to localize the goal region. The visual data, a set of images, is high dimensional unstructured data and needs to be represented efficiently to get a robust detector. Different deep Reinforcement models have already been used to localize a goal but most of them take long time to learn the model. This long learning time results from the weights fine tuning stage that is applied iteratively to find an accurate model. Hierarchical Extreme Learning Machine (H-ELM) was used as a fast deep model that doesn’t fine tune the weights. In other words, hidden weights are generated randomly and output weights are calculated analytically. H-ELM algorithm was used in this work to find good features for effective representation. This paper proposes a combination of Hierarchical Extreme learning machine and Reinforcement learning to find an optimal policy directly from visual input. This combination outperforms other methods in terms of accuracy and learning speed. The simulations and results were analysed by using MATLAB.
format	Conference or Workshop Item
author	AlDahoul, Nouar Htike, Zaw Zaw Akmeliawati, Rini
author_facet	AlDahoul, Nouar Htike, Zaw Zaw Akmeliawati, Rini
author_sort	AlDahoul, Nouar
title	Hierarchical extreme learning machine based reinforcement learning for goal localization
title_short	Hierarchical extreme learning machine based reinforcement learning for goal localization
title_full	Hierarchical extreme learning machine based reinforcement learning for goal localization
title_fullStr	Hierarchical extreme learning machine based reinforcement learning for goal localization
title_full_unstemmed	Hierarchical extreme learning machine based reinforcement learning for goal localization
title_sort	hierarchical extreme learning machine based reinforcement learning for goal localization
publisher	IOP Publishing
publishDate	2017
url	http://irep.iium.edu.my/54838/ http://irep.iium.edu.my/54838/ http://irep.iium.edu.my/54838/ http://irep.iium.edu.my/54838/2/54838-edited.pdf http://irep.iium.edu.my/54838/1/54838-Hierarchical%20extreme%20learning%20machine%20based%20reinforcement%20learning%20for%20goal%20localization_SCOPUS.pdf
first_indexed	2023-09-18T21:17:32Z
last_indexed	2023-09-18T21:17:32Z
_version_	1777411662804942848

Hierarchical extreme learning machine based reinforcement learning for goal localization

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