Dynamic programming: deterministic and stochastic models
Dynamic programming: deterministic and stochastic models
Proceedings of the seventh international conference (1990) on Machine learning
Neurocomputing: foundations of research
Applying genetics to fuzzy logic
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IEEE Transactions on Computers - Special issue on artificial neural networks
Practical Issues in Temporal Difference Learning
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Associative Reinforcement Learning: Functions in k-DNF
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TD(λ) Converges with Probability 1
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Reinforcement learning with replacing eligibility traces
Machine Learning - Special issue on reinforcement learning
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Learning to Predict by the Methods of Temporal Differences
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A New Approach to Fuzzy Classifier Systems
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Learning Control Under Extreme Uncertainty
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UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Dynamic Programming
Efficient Exploration In Reinforcement Learning
Efficient Exploration In Reinforcement Learning
Connectionist Learning for Contro: An Overview
Connectionist Learning for Contro: An Overview
Temporal credit assignment in reinforcement learning
Temporal credit assignment in reinforcement learning
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Application of reinforcement learning in robot soccer
Engineering Applications of Artificial Intelligence
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Fuzzy Actor-Critic Learning (FACL) and Fuzzy Q-learning (FQL) are reinforcement learning methods based on Dynamic Programming (DP) principles. In this chapter, they are used to tune on line the conclusion part of Fuzzy Inference Systems (FIS). The only information available for learning is the system feedback, which describes in terms of reward and punishment the task the fuzzy agent has to realize. At each time step, the agent receives a reinstate. The problem involves optimizing not only the direct reinforcement, but also the total amount of reinforcements the agent can receive in the future. To illustrate the use of these two learning methods, we first applied them to a problem in which we have to find a fuzzy controller to drive a boat from one bank to another, across a river with a strong non-linear current. Then, we used the well-known Cart-Pole Balancing and Mountain-Car problems to be able to compare our methods to other reinforcement learning methods, and focus on important characteristic aspects of FACL and FQL. The experimental studies had shown the superiority of these methods with respect to the other related methods we can find in the literature. We also found that our generic methods we can find in the literature. We also found that our generic methods allow us to learn every kind of reinforcement learning problem (continuous states, discrete/continuous actions, various types of reinforcement functions). Thanks to this flexibility, these learning methods have been applied successfully in an industrial problem, to discover a policy for pighouse environment control.