2010-12-01
Q(λ)-learning fuzzy logic controller for a multi-robot system
Publication
Publication
Presented at the
2010 IEEE International Conference on Systems, Man and Cybernetics, SMC 2010 (October 2010), Istanbul
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 function approximation (fuzzy inference system) is proposed. The system learns autonomously without supervision or a priori training data. The proposed technique is applied to a pursuit-evasion differential game in which both the pursuer and the evader self-learn their control strategies. 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, Multi-robot, Pursuit-evasion, Q(λ)-learning, Reinforcement learning | |
| doi.org/10.1109/ICSMC.2010.5641791 | |
| 2010 IEEE International Conference on Systems, Man and Cybernetics, SMC 2010 | |
| 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 a multi-robot system. In Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics (pp. 4075–4080). doi:10.1109/ICSMC.2010.5641791
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