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Authors: Richardson Ribeiro 1 ; Fábio Favarim 1 ; Marco A. C. Barbosa 1 ; André Pinz Borges 2 ; Osmar Betazzi Dordal 2 ; Alessandro L. Koerich 2 and Fabrício Enembreck 2

Affiliations: 1 Federal Technological University of Paraná, Brazil ; 2 Pontificial Catholical University of Paraná, Brazil

Keyword(s): Intelligent Agents, Reinforcement Learning, Dynamic Environments.

Related Ontology Subjects/Areas/Topics: Agents ; Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Enterprise Information Systems ; Intelligent Agents ; Internet Technology ; Strategic Decision Support Systems ; Web Information Systems and Technologies

Abstract: This paper presents an approach for speeding up the convergence of adaptive intelligent agents using reinforcement learning algorithms. Speeding up the learning of an intelligent agent is a complex task since the choice of inadequate updating techniques may cause delays in the learning process or even induce an unexpected acceleration that causes the agent to converge to a non- satisfactory policy. We have developed a technique for estimating policies which combines instance-based learning and reinforcement learning algorithms in Markovian environments. Experimental results in dynamic environments of different dimensions have shown that the proposed technique is able to speed up the convergence of the agents while achieving optimal action policies, avoiding problems of classical reinforcement learning approaches.

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Paper citation in several formats:
Ribeiro, R.; Favarim, F.; A. C. Barbosa, M.; Pinz Borges, A.; Betazzi Dordal, O.; L. Koerich, A. and Enembreck, F. (2012). Unified Algorithm to Improve Reinforcement Learning in Dynamic Environments - An Instance-based Approach. In Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 2: ICEIS; ISBN 978-989-8565-10-5; ISSN 2184-4992, SciTePress, pages 229-238. DOI: 10.5220/0004000002290238

@conference{iceis12,
author={Richardson Ribeiro. and Fábio Favarim. and Marco {A. C. Barbosa}. and André {Pinz Borges}. and Osmar {Betazzi Dordal}. and Alessandro {L. Koerich}. and Fabrício Enembreck.},
title={Unified Algorithm to Improve Reinforcement Learning in Dynamic Environments - An Instance-based Approach},
booktitle={Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 2: ICEIS},
year={2012},
pages={229-238},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004000002290238},
isbn={978-989-8565-10-5},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 2: ICEIS
TI - Unified Algorithm to Improve Reinforcement Learning in Dynamic Environments - An Instance-based Approach
SN - 978-989-8565-10-5
IS - 2184-4992
AU - Ribeiro, R.
AU - Favarim, F.
AU - A. C. Barbosa, M.
AU - Pinz Borges, A.
AU - Betazzi Dordal, O.
AU - L. Koerich, A.
AU - Enembreck, F.
PY - 2012
SP - 229
EP - 238
DO - 10.5220/0004000002290238
PB - SciTePress

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