Self-Organizing Interval Type-2 Fuzzy Q-learning for reinforcement fuzzy control

Author

Hsu, Chia-Hung ; Juang, Chia-Feng

Author_Institution

Dept. of Electr. Eng., Nat. Chung-Hsing Univ., Taichung, Taiwan

fYear

2011

fDate

9-12 Oct. 2011

Firstpage

2033

Lastpage

2038

Abstract

This paper proposes a reinforcement fuzzy control method using Self-Organizing Interval Type-2 Fuzzy Q-learning (SOIT2FQ). The fuzzy rules are of zero-order Takagi-Sugeno-Kang (TSK) type and the antecedent part uses interval type-2 fuzzy sets in order to improve fuzzy controller robustness. There are no fuzzy rules initially. The SOIT2FQ generates all fuzzy rules during control process using an online rule generation algorithm. The consequent part of each generated fuzzy rule is selected from a predefined discrete set containing all candidate values. The SOIT2FQ selects the consequent candidate values according to their Q-values in order to obtain a successful interval type-2 fuzzy controller. The SOIT2FQ is applied to reinforcement truck-backing control problem in clean and noisy environments, where only two reinforcement signals “success” and “failure” are used for training. Simulations show effectiveness and efficiency of the SOIT2FQ. Comparisons with type-1 fuzzy controller verify the noise robustness ability of the SOIT2FQ-designed interval type-2 fuzzy controller.

Keywords

control system synthesis; fuzzy control; fuzzy set theory; learning (artificial intelligence); robust control; self-organising feature maps; Q-value; SOIT2FQ-designed interval type-2 fuzzy controller robustness; discrete set; fuzzy rule; interval type-2 fuzzy set; noise robustness ability; online rule generation algorithm; reinforcement fuzzy control process; reinforcement signal; reinforcement truck-backing control problem; self organizing interval type-2 fuzzy Q-learning; type-1 fuzzy controller; zero order Takagi-Sugeno-Kang type rule; Firing; Frequency selective surfaces; Fuzzy control; Fuzzy sets; Learning; Learning systems; Trajectory; fuzzy Q-learning; fuzzy control; interval type-2 fuzzy sets; reinforcement learning; type-2 fuzzy systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference on

Conference_Location

Anchorage, AK

ISSN

1062-922X

Print_ISBN

978-1-4577-0652-3

Type

conf

DOI

10.1109/ICSMC.2011.6083971

Filename

6083971