2010-12-01
Q(λ)-learning fuzzy logic controller for differential games
Publication
Publication
Presented at the
2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10 (November 2010), Cairo
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.
| Additional Metadata | |
|---|---|
| Differential game, Function approximation, Fuzzy control, Q(λ)- learning, Reinforcement learning | |
| dx.doi.org/10.1109/ISDA.2010.5687283 | |
| 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10 | |
| Organisation | Department of Systems and Computer Engineering |
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Desouky, S.F. (Sameh F.), & Schwartz, H.M. (2010). Q(λ)-learning fuzzy logic controller for differential games. In Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10 (pp. 109–114). doi:10.1109/ISDA.2010.5687283
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