Fast learning automata for high-speed real-time applications

A new learning automaton which is capable of supporting high speed real-time applications is introduced. The proposed learning automaton has a unique characteristic: it is capable of performing both probability updating and action selection with a computational complexity which is independent of the number of actions. Apart from its low computational complexity, the proposed automaton is capable of achieving a high performance when operating in nonstationary stochastic environments.