Radial basis functions for multivariable interpolation: a review
Algorithms for approximation
Technical Note: \cal Q-Learning
Machine Learning
The Convergence of TD(λ) for General λ
Machine Learning
A reinforcement learning-based architecture for fuzzy logic control
International Journal of Approximate Reasoning - Special issue on fuzzy logic and neural networks for pattern recognition and control
TD(λ) Converges with Probability 1
Machine Learning
Learning to act using real-time dynamic programming
Artificial Intelligence - Special volume on computational research on interaction and agency, part 1
Temporal difference learning and TD-Gammon
Communications of the ACM
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Multistage Fuzzy Control: A Prescriptive Approach
Multistage Fuzzy Control: A Prescriptive Approach
Machine Learning
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Dynamic Programming
Adaptive Radial Basis Decomposition by Learning Vector Quantization
Neural Processing Letters
On characteristics of markov decision processes and reinforcement learning in large domains
On characteristics of markov decision processes and reinforcement learning in large domains
On the convergence of stochastic iterative dynamic programming algorithms
Neural Computation
Function approximation via tile coding: automating parameter choice
SARA'05 Proceedings of the 6th international conference on Abstraction, Reformulation and Approximation
Online tuning of fuzzy inference systems using dynamic fuzzy Q-learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
Fuzzy epoch-incremental reinforcement learning algorithm
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
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Q-learning is one of the most popular reinforcement learning methods that allows an agent to learn the relationship between interval-valued state and action spaces, through a direct interaction with the environment. Fuzzy Q-learning is an extension to this algorithm to enable it to evolve fuzzy inference systems (FIS) which range on continuous state and action spaces. In a FIS, the interaction among fuzzy rules plays a primary role to achieve good performance and robustness. Learning a system where this interaction is present gives to the learning mechanism problems due to eventually incoherent reinforcements coming to the same rule due to its interaction with other rules. In this paper, we will introduce different strategies to distribute reinforcement to reduce this undesired effect and to stabilize the obtained reinforcement. In particular, we will present two strategies: the former focuses on rewarding the actions chosen by each rule during the cooperation phase, the latter on rewarding the rules presenting actions closer to those actually executed rather than the rules that contributed to generate such actions.