Fast learning automata for high-speed real-time applications

A new learning automation which is capable of supporting high speed-real-time applications is introduced. The proposed learning automation has 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 automation is capable of achieving a high performance when operating in nonstationary stochastic environments