Dynamic programming: deterministic and stochastic models
Dynamic programming: deterministic and stochastic models
C4.5: programs for machine learning
C4.5: programs for machine learning
An Upper Bound on the Loss from Approximate Optimal-Value Functions
Machine Learning
Tree based discretization for continuous state space reinforcement learning
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neural Network Control of Robot Manipulators and Nonlinear Systems
Neural Network Control of Robot Manipulators and Nonlinear Systems
Neuro-Dynamic Programming
Machine Learning
Temporal credit assignment in reinforcement learning
Temporal credit assignment in reinforcement learning
On growing better decision trees from data
On growing better decision trees from data
Least-squares policy iteration
The Journal of Machine Learning Research
Restricted gradient-descent algorithm for value-function approximation in reinforcement learning
Artificial Intelligence
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Applying neural network to reinforcement learning in continuous spaces
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Fuzzy inference system learning by reinforcement methods
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Fuzzy decision trees: issues and methods
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Online tuning of fuzzy inference systems using dynamic fuzzy Q-learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks
IEEE Transactions on Fuzzy Systems
A Markov Game-Adaptive Fuzzy Controller for Robot Manipulators
IEEE Transactions on Fuzzy Systems
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Recent results on reinforcement learning regarding the convergence of control algorithms with function approximators, have shown that decision tree based reinforcement learning provides good learning performance and more reliable convergence than the neural network approach. It scales better to larger input spaces with lower memory requirements, and can solve problems that are infeasible using table lookup. However, decision tree based reinforcement learning can deal with only discrete actions. In realistic applications, it is imperative to deal with continuous states and actions. In this paper, we have proposed fuzzy decision tree based reinforcement learning that takes care of the limitations of decision tree based learning. We compare our approach with decision tree based function approximator on two bench mark problems: inverted pendulum stabilisation problem and two-link robot manipulator tracking problem.