Adaptive Fuzzy Control with Reinforcement Learning

Non-adaptive fuzzy logic controllers can become adaptive by learning from experience in the framework of reinforcement learning. In this paper, we discuss fuzzy reinforcement learning as a hybrid approach which provides a unified framework for including two types of prior knowledge: knowledge for control action selection and knowledge for performance evaluation. We describe GARIC, an architecture for combining fuzzy logic control and reinforcement learning, and apply it to cart-pole balancing and the Space Shuttle attitude control.