Fuzzy Rule Interpolation-based Q-learning

Reinforcement learning is a well known topic in computational intelligence. It can be used to solve control problems in unknown environments without defining an exact method on how to solve problems in various situations. Instead the goal is defined and all the actions done in the different states are given feedback, called reward or punishment (positive or negative reward). Based on these rewards the system can learn which action is considered the best in a given state. A method called Q-learning can be used for building up the state-action-value function. This method uses discrete states. With the application of fuzzy reasoning the method can be extended to be used in continuous environment, called Fuzzy Q-learning (FQ-Learning). Traditional Fuzzy Q-learning uses 0-order Takagi-Sugeno fuzzy inference. The main goal of this paper is to introduce Fuzzy Rule Interpolation (FRI), namely the FIVE (Fuzzy rule Interpolation based on Vague Environment) to be the model applied with Q-learning (FRIQ-learning). The paper also includes an application example: the well known cart pole (reversed pendulum) problem is used for demonstrating the applicability of the FIVE model in Q-learning.