Online tuning of fuzzy inference systems using dynamic fuzzy Q-learning.
This paper presents a dynamic fuzzy Q-learning (DFQL) method that is capable of tuning fuzzy inference systems (FIS) online. A novel online self-organizing learning algorithm is developed so that structure and parameters identification are accomplished automatically and simultaneously based only on Q-learning. Self-organizing fuzzy inference is introduced to calculate actions and Q-functions so as to enable us to deal with continuous-valued states and actions. Fuzzy rules provide a natural mean of incorporating the bias components for rapid reinforcement learning. Experimental results and comparative studies with the fuzzy Q-learning (FQL) and continuous-action Q-learning in the wall-following task of mobile robots demonstrate that the proposed DFQL method is superior.
Intelligent Systems Center, Singapore 637533. emjer@ntu.edu.sg
Meng Joo
MJ
Er
Chang
C
Deng
D000465:Algorithms; D001185:Artificial Intelligence; D005246:Feedback; D017143:Fuzzy Logic; D016571:Neural Networks, Computer; D009862:Online Systems; D010363:Pattern Recognition, Automated; D012371:Robotics / Q000379:methods*