Development of reinforcement learning methods in control and decision making in the large scale dynamic game environments

In this paper, an analytical comparison is done between dynamic programming and reinforcement learning methods in dynamic two-player games. The emphasis is on the large number of states and actions available for each player and different conflictive optimization objectives of these games that make them complicated in modeling and analysis. Optimization and decision making is done through quantifying a modified Q-learning algorithm. By this method, it is shown that the information processing in large scale-long stage games will take shorter times and will result in lower decision costs whereas dynamic programming methods cannot handle them across long time-horizons