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Fuzzy-Q learning for autonomous robot systems

It is desirable for autonomous robot systems to posses the ability to behave in a smooth and continuous fashion when interacting with an unknown environment. Since Q-learning is normally used for optimizing a series of discrete actions, it may be undesirable when applied to a real environment which involves continuous states and actions. In this paper, we propose a new method of Q-learning that incorporates a fuzzy interpolation technique which is used to approximate a continuous state. Our learning method can estimate current state by its neighboring states and has the ability to learn its actions similar to that of Q-learning. Thus, our method can enable robots to react smoothly in a real environment. Simulation results involving an autonomous robot are given to show the validity of our method.