A fuzzy Q-learning approach to navigation of an autonomous robot

The proposed algorithm takes advantage of coupling fuzzy logic and Q-learning to fulfill requirements of autonomous navigations. Fuzzy if-then rules provide a reliable decision making framework to handle uncertainties, and also allow incorporation of heuristic knowledge. Dynamic structure of Q-learning makes it a promising tool to adjust fuzzy inference parameters when little or no prior knowledge is available about the world. To robot, the world is modeled into a set of state-action pairs. For each fuzzified state, there are some suggested actions. States are related to their corresponding actions via fuzzy if-then rules based on human reasoning. The robot selects the most encouraged action for each state through online experiences. Efficiency of the proposed method is validated through experiments on a simulated Khepera robot.