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Authors: Saba Q. Yahyaa and Bernard Manderick

Affiliation: Vrije Universiteit Brussel, Belgium

Keyword(s): Online reinforcement learning, value function approximation, (kernel-based) least squares policy iteration, approximate linear dependency kernel sparsification, knowledge gradient exploration policy.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Evolutionary Computing ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems

Abstract: We present online kernel-based LSPI (or least squares policy iteration) which is an extension of offline kernel based LSPI. Online kernel-based LSPI combines characteristics of both online LSPI and offline kernel-based LSPI to improve the convergence rate as well as the optimal policy performances of the online LSPI. Online kernel-based LSPI uses knowledge gradient policy as an exploration policy and the approximate linear dependency based kernel sparsification method to select features automatically. We compare the optimal policy performance of online kernel-based LSPI and online LSPI on 5 discrete Markov decision problems, where online kernel-based LSPI outperforms online LSPI.

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Paper citation in several formats:
Q. Yahyaa, S. and Manderick, B. (2014). Online Knowledge Gradient Exploration in an Unknown Environment. In Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-015-4; ISSN 2184-433X, SciTePress, pages 5-13. DOI: 10.5220/0004718700050013

@conference{icaart14,
author={Saba {Q. Yahyaa}. and Bernard Manderick.},
title={Online Knowledge Gradient Exploration in an Unknown Environment},
booktitle={Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2014},
pages={5-13},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004718700050013},
isbn={978-989-758-015-4},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - Online Knowledge Gradient Exploration in an Unknown Environment
SN - 978-989-758-015-4
IS - 2184-433X
AU - Q. Yahyaa, S.
AU - Manderick, B.
PY - 2014
SP - 5
EP - 13
DO - 10.5220/0004718700050013
PB - SciTePress

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