Solving the optimization problem to approach a Nash Equilibrium point plays an important role in imperfect information games,e.g.,StarCraft and poker.Neural Fictitious Self-Play (NFSP) is an effective algorithm that learns approxi-mate Nash Equilibrium of imperfect-information games from purely self-play without prior domain knowledge.However,it needs to train a neural network in an off-policy manner to approximate the action values.For games with large search spaces,the training may suffer from unnecessary exploration and sometimes fails to converge.In this paper,we propose a new Neural Fictitious Self-Play algorithm that combines Monte Carlo tree search with NFSP,called MC-NFSP,to improve the performance in real-time zero-sum imperfect-information games.With experiments and empirical analysis,we demon-strate that the proposed MC-NFSP algorithm can approximate Nash Equilibrium in games with large-scale search depth while the NFSP can not.Furthermore,we develop an Asynchronous Neural Fictitious Self-Play framework (ANFSP).It uses asyn-chronous and parallel architecture to collect game experience and improve both the training efficiency and policy quality.The experiments with th e games with hidden state informa-tion (Texas Hold\'em),and the FPS (firstperson shooter) games demonstrate effectiveness of our algorithms.