Real-time dynamic fuzzy Q-learning and control of mobile robots
ICECS'03 Proceedings of the 2nd WSEAS International Conference on Electronics, Control and Signal Processing
An online Bayesian Ying-Yang learning applied to fuzzy CMAC
Neurocomputing
Reinforcement distribution in fuzzy Q-learning
Fuzzy Sets and Systems
Wall-following control of an infrared sensors guided wheeled mobile robot
International Journal of Intelligent Systems Technologies and Applications
Ant colony optimization incorporated with fuzzy Q-learning for reinforcement fuzzy control
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Exploration and exploitation balance management in fuzzy reinforcement learning
Fuzzy Sets and Systems
Fuzzy Sets and Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Reinforcement self-organizing interval type-2 fuzzy system with ant colony optimization
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Fuzzy decision tree function approximation in reinforcement learning
International Journal of Artificial Intelligence and Soft Computing
Efficient parametric adjustment of fuzzy inference system using unconstrained optimization
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
A hybrid self-learning approach for generating fuzzy inference systems
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Intelligent fuzzy q-learning control of humanoid robots
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Similarity of learned helplessness in human being and fuzzy reinforcement learning algorithms
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Computational intelligence models for image processing and information reasoning
Hi-index | 0.00 |
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.