Efficient implementation of dynamic fuzzy Q-learning

This paper presents a dynamic fuzzy Q-learning (DFQL) method that is capable of tuning the fuzzy inference systems (FIS) online. On-line self-organizing learning is developed so that structure and parameters identification are accomplished automatically and simultaneously. Self-organizing fuzzy inference is introduced to calculate actions and Q-functions so as to enable us to deal with continuous-valued states and actions. We provide the conditions of the convergence of the algorithm. Furthermore, the learning methods based on bias component and eligibility traces for rapid reinforcement learning are discussed.