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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Bibliographic Details
Main Authors: Nguyen, Trung Thanh, Li, Zhuoru, Silander, Tomi, Tze-Yun LEONG
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2013
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Online Access:https://ink.library.smu.edu.sg/sis_research/3030
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Institution: Singapore Management University
Language: English

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