Abstract
Fuzzy Q-learning has been studied that can treat a continuous state, since Q-learning treats only a discrete state. Dynamic Fuzzy Q-Learning (DFQL) has been proposed, where a new pair of state and actions is dynamically added to a given initial table of Q value. We propose a more flexible dynamic fuzzy Q-learning with facilities of tuning states of fuzzy sets and removing pairs of state and actions. We tune the center values and widths of fuzzy sets with TD (Temporal Difference) error of V value, which is evaluated value of states. We apply forgetting learning to fuzzy sets and V value and remove unnecessary fuzzy sets and unnecessary pairs of state and actions. We apply the method to the pursuit problem in a continuous environment.
