A core task abstraction approach to hierarchical reinforcement learning [Extended abstract]

We propose a new, core task abstraction (CTA) approach to learning the relevant transition functions in model-based hierarchical reinforcement learning. CTA exploits contextual independences of the state variables conditional on the task-specific actions; its promising performance is demonstrated th...

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Main Authors:	LI, Zhuoru, NARAYAN, Akshay, Tze-Yun LEONG
Format:	text
Language:	English
Published:	Institutional Knowledge at Singapore Management University 2016
Subjects:	hierarchical reinforcement learning core task abstraction Computer Sciences Numerical Analysis and Scientific Computing Theory and Algorithms
Online Access:	https://ink.library.smu.edu.sg/sis_research/3431 https://ink.library.smu.edu.sg/context/sis_research/article/4432/viewcontent/Acoretaskabstractionapproachto.pdf
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Institution:	Singapore Management University
Language:	English

id	sg-smu-ink.sis_research-4432
record_format	dspace
spelling	sg-smu-ink.sis_research-44322020-03-25T03:17:07Z A core task abstraction approach to hierarchical reinforcement learning [Extended abstract] LI, Zhuoru NARAYAN, Akshay Tze-Yun LEONG, We propose a new, core task abstraction (CTA) approach to learning the relevant transition functions in model-based hierarchical reinforcement learning. CTA exploits contextual independences of the state variables conditional on the task-specific actions; its promising performance is demonstrated through a set of benchmark problems. 2016-05-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/3431 https://ink.library.smu.edu.sg/context/sis_research/article/4432/viewcontent/Acoretaskabstractionapproachto.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University hierarchical reinforcement learning core task abstraction Computer Sciences Numerical Analysis and Scientific Computing Theory and Algorithms
institution	Singapore Management University
building	SMU Libraries
continent	Asia
country	Singapore Singapore
content_provider	SMU Libraries
collection	InK@SMU
language	English
topic	hierarchical reinforcement learning core task abstraction Computer Sciences Numerical Analysis and Scientific Computing Theory and Algorithms
spellingShingle	hierarchical reinforcement learning core task abstraction Computer Sciences Numerical Analysis and Scientific Computing Theory and Algorithms LI, Zhuoru NARAYAN, Akshay Tze-Yun LEONG, A core task abstraction approach to hierarchical reinforcement learning [Extended abstract]
description	We propose a new, core task abstraction (CTA) approach to learning the relevant transition functions in model-based hierarchical reinforcement learning. CTA exploits contextual independences of the state variables conditional on the task-specific actions; its promising performance is demonstrated through a set of benchmark problems.
format	text
author	LI, Zhuoru NARAYAN, Akshay Tze-Yun LEONG,
author_facet	LI, Zhuoru NARAYAN, Akshay Tze-Yun LEONG,
author_sort	LI, Zhuoru
title	A core task abstraction approach to hierarchical reinforcement learning [Extended abstract]
title_short	A core task abstraction approach to hierarchical reinforcement learning [Extended abstract]
title_full	A core task abstraction approach to hierarchical reinforcement learning [Extended abstract]
title_fullStr	A core task abstraction approach to hierarchical reinforcement learning [Extended abstract]
title_full_unstemmed	A core task abstraction approach to hierarchical reinforcement learning [Extended abstract]
title_sort	core task abstraction approach to hierarchical reinforcement learning [extended abstract]
publisher	Institutional Knowledge at Singapore Management University
publishDate	2016
url	https://ink.library.smu.edu.sg/sis_research/3431 https://ink.library.smu.edu.sg/context/sis_research/article/4432/viewcontent/Acoretaskabstractionapproachto.pdf
_version_	1770573200483155968

A core task abstraction approach to hierarchical reinforcement learning [Extended abstract]

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