Motivated learning as an extension of reinforcement learning

We have developed a unified framework to conduct computational experiments with both learning systems: Motivated learning based on Goal Creation System, and reinforcedment learning using RL Q-Learning Algorithm. Future work includes combining motivated learning to set abstract motivations and manage...

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Main Authors:	STARZYK, Janusz, RAIF, Pawel, TAN, Ah-hwee
Format:	text
Language:	English
Published:	Institutional Knowledge at Singapore Management University 2010
Subjects:	Learning systems motivated learning reinforcement learning Artificial Intelligence and Robotics Theory and Algorithms
Online Access:	https://ink.library.smu.edu.sg/sis_research/6573 https://ink.library.smu.edu.sg/context/sis_research/article/7576/viewcontent/10.1.1.418.3487.pdf
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Institution:	Singapore Management University
Language:	English

id	sg-smu-ink.sis_research-7576
record_format	dspace
spelling	sg-smu-ink.sis_research-75762022-01-13T08:08:30Z Motivated learning as an extension of reinforcement learning STARZYK, Janusz RAIF, Pawel TAN, Ah-hwee We have developed a unified framework to conduct computational experiments with both learning systems: Motivated learning based on Goal Creation System, and reinforcedment learning using RL Q-Learning Algorithm. Future work includes combining motivated learning to set abstract motivations and manage goals with reinforcement learning to learn proper actions. This will allow testing of motivated learning on typical reinforcement learning benchmarks with large dimensionality of the state/action spaces. 2010-01-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6573 https://ink.library.smu.edu.sg/context/sis_research/article/7576/viewcontent/10.1.1.418.3487.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 Learning systems motivated learning reinforcement learning Artificial Intelligence and Robotics 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	Learning systems motivated learning reinforcement learning Artificial Intelligence and Robotics Theory and Algorithms
spellingShingle	Learning systems motivated learning reinforcement learning Artificial Intelligence and Robotics Theory and Algorithms STARZYK, Janusz RAIF, Pawel TAN, Ah-hwee Motivated learning as an extension of reinforcement learning
description	We have developed a unified framework to conduct computational experiments with both learning systems: Motivated learning based on Goal Creation System, and reinforcedment learning using RL Q-Learning Algorithm. Future work includes combining motivated learning to set abstract motivations and manage goals with reinforcement learning to learn proper actions. This will allow testing of motivated learning on typical reinforcement learning benchmarks with large dimensionality of the state/action spaces.
format	text
author	STARZYK, Janusz RAIF, Pawel TAN, Ah-hwee
author_facet	STARZYK, Janusz RAIF, Pawel TAN, Ah-hwee
author_sort	STARZYK, Janusz
title	Motivated learning as an extension of reinforcement learning
title_short	Motivated learning as an extension of reinforcement learning
title_full	Motivated learning as an extension of reinforcement learning
title_fullStr	Motivated learning as an extension of reinforcement learning
title_full_unstemmed	Motivated learning as an extension of reinforcement learning
title_sort	motivated learning as an extension of reinforcement learning
publisher	Institutional Knowledge at Singapore Management University
publishDate	2010
url	https://ink.library.smu.edu.sg/sis_research/6573 https://ink.library.smu.edu.sg/context/sis_research/article/7576/viewcontent/10.1.1.418.3487.pdf
_version_	1770575993682001920

Motivated learning as an extension of reinforcement learning

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