Reinforcement Distribution in Continuous State Action Space Fuzzy Q-Learning: A Novel Approach.

Fuzzy Q-learning extends the Q-learning algorithm to work in presence of continuous state and action spaces. A Takagi-Sugeno Fuzzy Inference System (FIS) is used to infer the continuous executed action and its action-value, by means of cooperation of several rules. Different kinds of evolution of the parameters of the FIS are possible, depending on different strategies of distribution of the reinforcement signal. In this paper, we compare two strategies: the classical one, focusing on rewarding the rules that have proposed the actions composed to produce the actual action, and a new one we are introducing, where reward goes to the rules proposing actions closest the ones actually executed. Keywords: Reinforcement Learning, Fuzzy Q-learning, Fuzzy logic, continuous state-action space, reinforcement distribution. [ABSTRACT FROM AUTHOR]