  Counterfactual Regret Minimization (CFR)} is the popular method for finding
approximate Nash equilibrium in two-player zero-sum games with imperfect
information. CFR solves games by travsersing the full game tree iteratively,
which limits its scalability in larger games. When applying CFR to solve
large-scale games in previously, large-scale games are abstracted into
small-scale games firstly. Secondly, CFR is used to solve the abstract game.
And finally, the solution strategy is mapped back to the original large-scale
game. However, this process requires considerable expert knowledge, and the
accuracy of abstraction is closely related to expert knowledge. In addition,
the abstraction also loses certain information, which will eventually affect
the accuracy of the solution strategy. Towards this problem, a recent method,
\textit{Deep CFR} alleviates the need for abstraction and expert knowledge by
applying deep neural networks directly to CFR in full games. In this paper, we
introduces \textit{Neural Network Counterfactual Regret Minimization (NNCFR)},
an improved variant of \textit{Deep CFR} that has a faster convergence by
constructing a dueling netwok as the value network. Moreover, an evaluation
module is designed by combining the value network and Monte Carlo, which
reduces the approximation error of the value network. In addition, a new loss
function is designed in the procedure of training policy network in the
proposed \textit{NNCFR}, which can be good to make the policy network more
stable. The extensive experimental tests are conducted to show that the
\textit{NNCFR} converges faster and performs more stable than \textit{Deep
CFR}, and outperforms \textit{Deep CFR} with respect to exploitability and
head-to-head performance on test games.
