Fuzzy interpolation-based Q-learning with continuous states and actions

This paper proposes a new method of Q-learning where fuzzy inference is introduced to calculate the Q-function that evaluates the state/action pairs so as to enable us to deal with continuous-valued pairs and continuous-valued states and actions. In this method, the Q-function is updated using the steepest descent method. Our proposed method is applied to a cart-pole balancing system, which demonstrates considerable improvements in its control performance with the aid of the fuzzy inference.