Stochastic automata models capable of exhibiting unconditional learning behaviour are proposed and their behaviour analysed when operating in a stationary random environment about which they have no a priori knowledge. The automaton expected expected penalty is used as a performance index to assess its capability to acquire information pertaining to the unknown features of the environment. The optimization of the performance index provides a measure of the learning capacity of the automaton. The interaction between an automaton and a random media is also considered in the context of the two-armed bandit problem. Computer simulation results show that automata structures discussed in this paper compare well with two-armed bandit models.