Abstract
Learning automata select an action from a finite set of their available actions and update their strategy on the basis of response received from the random environment using what is known as a reinforcement scheme. As an environment changes, the ordering of the actions with the performance criterion may vary. If a learning automaton with a fixed strategy is used in such an environment, it may become less expedient with time and even inexpedient. However, using the learning scheme that has sufficient flexibility to track the better actions, makes the performance improved.
In this paper, a variable structure learning automaton network with periodic random environment is proposed. The results of some numerical simulations shown that our model can be used for tracking some periodic non-stationary environments that an upper bound on the period is known.
