asymptotic behavior learning automata operating in state dependent nonstationary environments

in this paper we intorduce a new state dependent nonstationary environment and study the asymptotic behavior of SLrI learning algorithm operating under the proposed environment it is shown that the SLR-I automaton operating in the proposed nontationary environment equalizes the expected penalty strengths of actions this model was motivated by applications of learning automata in call admission in cellular networks