Reinforcement Learning by an Accuracy-Based Fuzzy Classifier System with Real-valued Output

The issue of finding fuzzy models with an interpretability as good as possible without decreasing the accuracy is one of the main research topics on genetic fuzzy systems. When they are used to perform on-line reinforcement learning by means of Michigan-style fuzzy classifier systems, this issue becomes even more difficult. Indeed, rule generalization (description of state-action relationships with rules as compact as possible) has received a great deal of attention in the discrete-valued learning classifier system field (e.g., XCS is the subject of extensive ongoing research). However, the same issue does not appear to have received a similar level of attention in the case of Michigan-style fuzzy classifier system. This may be due to the difficulty in extending the discrete-valued system operation to the continuous case. The intention of this contribution is to propose an approach to properly develop a fuzzy XCS system for immediate-reward problems.