The application of imperfect information game theory in social games

Abstract

The research on game AI has always been one of the focuses of artificial intelligence research. More complex real-world problems can be solved by the study of game AI. Since our cognition and information acquisition of the world are imperfect, we proposed single deep counterfactual regret minimization method with advantage baselines, a deep reinforcement learning algorithm based on imperfect information game. The new approach combines the single deep counterfactual regret minimization approach with the baseline network to achieve a better performance than the benchmark in poker. At last, this paper applies the new algorithm to two Chinese poker games with imperfect information game, and obtains better performance. Meanwhile, it also provides the possibility for the extension to real life.