To obtain the probability distribution of actions we use the standard reinforcement learning approach. We apply the soft-max function to the action preference function ℎ( , , ) and use the -greedy approach that guarantees continuous exploration of the state space. In this -greedy approach, the user defines probability to sample actions from the uniform distribution so the probability for the action to be selected in state with the policy vector is as follows: