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In general, two-agent decision-making problems can be modeled as a two-playergame, 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 Nashequilibrium strategy in a two-player zero-sum game with imperfect information.The CFR method adopts a regret matching algorithm iteratively to reduce regretvalues progressively, enabling the average strategy to approach a Nashequilibrium. Although CFR-based methods have achieved significant success inthe field of imperfect information games, there is still scope for improvementin the efficiency of convergence. To address this challenge, we propose a novelCFR-based method named exponential counterfactual regret minimization (ECFR).With ECFR, an exponential weighting technique is used to reweight theinstantaneous regret value during the process of iteration. A theoretical proofis provided to guarantees convergence of the ECFR algorithm. The result of anextensive set of experimental tests demostrate that the ECFR algorithmconverges faster than the current state-of-the-art CFR-based methods.

Solving imperfect-information games via exponential counterfactual regret minimization