  In general, two-agent decision-making problems can be modeled as a two-player
game, and a typical solution is to find a Nash equilibrium in such game.
Counterfactual regret minimization (CFR) is a well-known method to find a Nash
equilibrium strategy in a two-player zero-sum game with imperfect information.
The CFR method adopts a regret matching algorithm iteratively to reduce regret
values progressively, enabling the average strategy to approach a Nash
equilibrium. Although CFR-based methods have achieved significant success in
the field of imperfect information games, there is still scope for improvement
in the efficiency of convergence. To address this challenge, we propose a novel
CFR-based method named exponential counterfactual regret minimization (ECFR).
With ECFR, an exponential weighting technique is used to reweight the
instantaneous regret value during the process of iteration. A theoretical proof
is provided to guarantees convergence of the ECFR algorithm. The result of an
extensive set of experimental tests demostrate that the ECFR algorithm
converges faster than the current state-of-the-art CFR-based methods.
