Before revision:  Furthermore, we discuss the resulted post-learning policies , show that they are almost optimal in the underlying game sense, and provide numerical hints of almost welfare-optimal of the resulted policies .
After revision:  Furthermore, we discuss the resulted post-learning policies , show that they are almost optimal in the underlying game sense, i.e., they form a Nash equilibrium. Furthermore, we propose in this work a Q-learning algorithm, requiring predictive observation of two subsequent opponent's actions, yielding an optimal strategy given that the latter applies a stationary strategy, and discuss the existence of the Nash equilibrium in the underlying information asymmetrical game .