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Authors: Abdelkrim Nemra ; Hacene Rezine and Abdelkrim Souici

Affiliation: Unit of Control, Robotic and Productic Laboratory, Polytechnical Military School, France

Keyword(s): Reinforcement learning, Fuzzy controllers’ Genetic algorithm, mobile robotic.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Decision Support Systems ; Expert Systems ; Health Information Systems ; Hybrid Learning Systems ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Symbolic Systems

Abstract: An efficient genetic reinforcement learning algorithm for designing Fuzzy Inference System (FIS) with out any priory knowledge is proposed in this paper. Reinforcement learning using Fuzzy Q-Learning (FQL) is applied to select the consequent action values of a fuzzy inference system, in this method, the consequent value is selected from a predefined value set which is kept unchanged during learning and if the optimal solution is not present in the randomly generated set, then the performance may be poor. Also genetic algorithms (Genetic Algorithm) are performed to on line search for better consequent and premises parameters based on the learned Q-values as adaptation function. In Fuzzy-Q-Learning Genetic Algorithm (FQLGA), memberships (premises) parameters are distributed equidistant and the consequent parts of fuzzy rules are randomly generated. The algorithm is validated in simulation and experimentation on mobile robot reactive navigation behaviors.

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Paper citation in several formats:
Nemra, A.; Rezine, H. and Souici, A. (2007). GENETIC REINFORCEMENT LEARNING OF FUZZY INFERENCE SYSTEM APPLICATION TO MOBILE ROBOTIC. In Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 978-972-8865-82-5; ISSN 2184-2809, SciTePress, pages 206-213. DOI: 10.5220/0001645902060213

@conference{icinco07,
author={Abdelkrim Nemra. and Hacene Rezine. and Abdelkrim Souici.},
title={GENETIC REINFORCEMENT LEARNING OF FUZZY INFERENCE SYSTEM APPLICATION TO MOBILE ROBOTIC},
booktitle={Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2007},
pages={206-213},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001645902060213},
isbn={978-972-8865-82-5},
issn={2184-2809},
}

TY - CONF

JO - Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - GENETIC REINFORCEMENT LEARNING OF FUZZY INFERENCE SYSTEM APPLICATION TO MOBILE ROBOTIC
SN - 978-972-8865-82-5
IS - 2184-2809
AU - Nemra, A.
AU - Rezine, H.
AU - Souici, A.
PY - 2007
SP - 206
EP - 213
DO - 10.5220/0001645902060213
PB - SciTePress