ACM Computing Surveys (CSUR)
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Practical Reinforcement Learning in Continuous Spaces
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Generalized relevance learning vector quantization
Neural Networks - New developments in self-organizing maps
Experiments with Reinforcement Learning in Problems with Continuous State and Action Spaces
Experiments with Reinforcement Learning in Problems with Continuous State and Action Spaces
Making reinforcement learning work on real robots
Making reinforcement learning work on real robots
A Reinforcement Learning Approach to Online Clustering
Neural Computation
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Fuzzy inference system learning by reinforcement methods
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A survey of fuzzy clustering algorithms for pattern recognition. I
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An integrated approach to fuzzy learning vector quantization and fuzzy c-means clustering
IEEE Transactions on Fuzzy Systems
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In reinforcement learning, it is important to get nearly right answers early. Good prediction early can reduce the prediction error afterward and accelerate learning speed. We propose Fuzzy Q-Map, function approximation algorithm based on on-line fuzzy clustering in order to accelerate learning. Fuzzy Q-Map can handle the uncertainty owing to the absence of environment model. Appling membership function to reinforcement learning can reduce the prediction error and destructive interference phenomenon caused by changes of the distribution of training data. In order to evaluate fuzzy Q-Map's performance, we experimented on the mountain car problem and compared it with CMAC. CMAC achieves the prediction rate 80% from 250 training data, Fuzzy Q-Map learns faster and keep up the prediction rate 80% from 250 training data. Fuzzy Q-Map may be applied to the field of simulation that has uncertainty and complexity.