GENETIC REINFORCEMENT LEARNING OF FUZZY INFERENCE SYSTEM APPLICATION TO MOBILE ROBOTIC

Abdelkrim Nemra, Hacene Rezine, Abdelkrim Souici

2007

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.

Paper Citation

in Harvard Style

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, pages 206-213. DOI: 10.5220/0001645902060213

in Bibtex Style

@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},
}

in EndNote Style

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
AU - Nemra A.
AU - Rezine H.
AU - Souici A.
PY - 2007
SP - 206
EP - 213
DO - 10.5220/0001645902060213