  Counterfactual regret minimization (CFR) is a popular method to deal with
decision-making problems of two-player zero-sum games with imperfect
information. Unlike existing studies that mostly explore for solving larger
scale problems or accelerating solution efficiency, we propose a framework,
RLCFR, which aims at improving the generalization ability of the CFR method. In
the RLCFR, the game strategy is solved by the CFR in a reinforcement learning
framework. And the dynamic procedure of iterative interactive strategy updating
is modeled as a Markov decision process (MDP). Our method, RLCFR, then learns a
policy to select the appropriate way of regret updating in the process of
iteration. In addition, a stepwise reward function is formulated to learn the
action policy, which is proportional to how well the iteration strategy is at
each step. Extensive experimental results on various games have shown that the
generalization ability of our method is significantly improved compared with
existing state-of-the-art methods.
