Online feature selection for model-based reinforcement learning

We propose a new framework for learning the world dynamics of feature-rich environments in model-based reinforcement learning. The main idea is formalized as a new, factored state-transition representation that supports efficient online-learning of the relevant features. We construct the transition...

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Main Authors:	Nguyen, Trung Thanh, Li, Zhuoru, Silander, Tomi, Tze-Yun LEONG
Format:	text
Language:	English
Published:	Institutional Knowledge at Singapore Management University 2013
Subjects:	Artificial intelligence; Software engineering High dimensional spaces Learning techniques Model-based reinforcement learning Online feature selection Relevant features Theoretical guarantees Transition model World dynamics Databases and Information Systems
Online Access:	https://ink.library.smu.edu.sg/sis_research/3030
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Institution:	Singapore Management University
Language:	English

id	sg-smu-ink.sis_research-4030
record_format	dspace
spelling	sg-smu-ink.sis_research-40302016-02-05T06:30:05Z Online feature selection for model-based reinforcement learning Nguyen, Trung Thanh Li, Zhuoru Silander, Tomi Tze-Yun LEONG, We propose a new framework for learning the world dynamics of feature-rich environments in model-based reinforcement learning. The main idea is formalized as a new, factored state-transition representation that supports efficient online-learning of the relevant features. We construct the transition models through predicting how the actions change the world. We introduce an online sparse coding learning technique for feature selection in high-dimensional spaces. We derive theoretical guarantees for our framework and empirically demonstrate its practicality in both simulated and real robotics domains. Copyright 2013 by the author(s). 2013-06-21T07:00:00Z text https://ink.library.smu.edu.sg/sis_research/3030 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Artificial intelligence; Software engineering High dimensional spaces Learning techniques Model-based reinforcement learning Online feature selection Relevant features Theoretical guarantees Transition model World dynamics Databases and Information Systems
institution	Singapore Management University
building	SMU Libraries
continent	Asia
country	Singapore Singapore
content_provider	SMU Libraries
collection	InK@SMU
language	English
topic	Artificial intelligence; Software engineering High dimensional spaces Learning techniques Model-based reinforcement learning Online feature selection Relevant features Theoretical guarantees Transition model World dynamics Databases and Information Systems
spellingShingle	Artificial intelligence; Software engineering High dimensional spaces Learning techniques Model-based reinforcement learning Online feature selection Relevant features Theoretical guarantees Transition model World dynamics Databases and Information Systems Nguyen, Trung Thanh Li, Zhuoru Silander, Tomi Tze-Yun LEONG, Online feature selection for model-based reinforcement learning
description	We propose a new framework for learning the world dynamics of feature-rich environments in model-based reinforcement learning. The main idea is formalized as a new, factored state-transition representation that supports efficient online-learning of the relevant features. We construct the transition models through predicting how the actions change the world. We introduce an online sparse coding learning technique for feature selection in high-dimensional spaces. We derive theoretical guarantees for our framework and empirically demonstrate its practicality in both simulated and real robotics domains. Copyright 2013 by the author(s).
format	text
author	Nguyen, Trung Thanh Li, Zhuoru Silander, Tomi Tze-Yun LEONG,
author_facet	Nguyen, Trung Thanh Li, Zhuoru Silander, Tomi Tze-Yun LEONG,
author_sort	Nguyen, Trung Thanh
title	Online feature selection for model-based reinforcement learning
title_short	Online feature selection for model-based reinforcement learning
title_full	Online feature selection for model-based reinforcement learning
title_fullStr	Online feature selection for model-based reinforcement learning
title_full_unstemmed	Online feature selection for model-based reinforcement learning
title_sort	online feature selection for model-based reinforcement learning
publisher	Institutional Knowledge at Singapore Management University
publishDate	2013
url	https://ink.library.smu.edu.sg/sis_research/3030
_version_	1770572797818437632

Online feature selection for model-based reinforcement learning

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