An approach to tune fuzzy controllers based on reinforcement learning for autonomous vehicle control

Authors:
Xiaohui Dai;Chi-Kwong Li;A. B. Rad
Affiliations:
Rockwell Autom. Res. Center, Shanghai, China;-;-
Venue:
IEEE Transactions on Intelligent Transportation Systems
Year:
2005

Citing 0
Cited 10

H∞ reinforcement learning control of robot manipulators using fuzzy wavelet networks

Fuzzy Sets and Systems
Efficient Parametric Adjustment of Fuzzy Inference System Using Error Backpropagation Method

ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
A novel technique to design a fuzzy logic controller using Q(λ)-learning and genetic algorithms in the pursuit-evasion game

SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
A novel hybrid learning technique applied to a self-learning multi-robot system

SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
An experimental adaptive fuzzy controller for differential games

SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Efficient parametric adjustment of fuzzy inference system using unconstrained optimization

IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
A Reinforcement Learning Adaptive Fuzzy Controller for Differential Games

Journal of Intelligent and Robotic Systems
Self-learning fuzzy logic controllers for pursuit-evasion differential games

Robotics and Autonomous Systems
A hybrid cognitive/reactive intelligent agent autonomous path planning technique in a networked-distributed unstructured environment for reinforcement learning

The Journal of Supercomputing
Towards a Multiple-Lookahead-Levels agent reinforcement-learning technique and its implementation in integrated circuits

The Journal of Supercomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we suggest a new approach for tuning parameters of fuzzy controllers based on reinforcement learning. The architecture of the proposed approach is comprised of a Q estimator network (QEN) and a Takagi-Sugeno-type fuzzy inference system (TSK-FIS). Unlike other fuzzy Q-learning approaches that select an optimal action based on finite discrete actions, the proposed controller obtains the control output directly from TSK-FIS. With the proposed architecture, the learning algorithms for all the parameters of the QEN and the FIS are developed based on the temporal-difference (TD) methods as well as the gradient-descent algorithm. The performance of the proposed design technique is illustrated by simulation studies of a vehicle longitudinal-control system.