Q(λ)-learning fuzzy logic controller for differential games
ISDA(2010)
Abstract
This paper addresses the problem of tuning the input and the output parameters of a fuzzy logic controller. A novel technique that combines Q(λ)-learning with a fuzzy inference system as a function approximation is proposed. The system learns autonomously without supervision or a priori training data. The proposed technique is applied to two different differential games. The proposed technique is compared with the classical control strategy, Q(λ)-learning only, and the technique proposed in [1] in which a neural network is used as a function approximation for Q-learning. Computer simulations show the usefulness of the proposed technique.
MoreTranslated text
Key words
adaptive control,control system synthesis,differential games,function approximation,fuzzy control,fuzzy reasoning,learning systems,q(λ)-learning fuzzy logic controller,q-learning,fuzzy inference system,neural network,differential game,q(λ)-learning,reinforcement learning,q learning,intelligent systems,games,mobile robots,mathematical model,computer simulation,artificial neural networks
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined