Absolute Expediency of Q-and S-Model Learning Algorithms

Abstract

A class of nonlinear learning algorithms for the Q-and S-model stochastic automaton-random environment setup are described. Necessary and sufficient conditions for absolute expediency of these algorithms are derived. Various algorithms that are so far reported in literature can be obtained as special cases of the general algorithm given in this correspondence.