Sep 20, 2021 · In this paper, we generalize MCCFR by considering any generic estimator of the sought values. We show that any choice of an estimator can be ...
Counterfactual Regret Minimization (CFR) (Zinke- vich et al. 2008) is an algorithm capable of finding effec- tive strategies in a variety of games. In 2-player ...
In large extensive form games with imperfect information,. Counterfactual Regret Minimization (CFR) is a popular, it- erative algorithm for computing ...
In this paper, we generalize MCCFR by consider-ing any generic estimator of the sought values. We show that any choice of an estimator can be used to ...
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2012. Generalized sampling and variance in counterfactual regret minimization. In AAAI. Conference on Artificial Intelligence, 1355–1361. [HartandMas-Colell2000] ...
Counterfactual Regret Minimization (CFR) is a popular, iterative algorithm for computing strategies in extensive-form games. The Monte Carlo CFR (MCCFR).
Generalized sampling and variance in counterfactual regret minimization. In Proceedings of the Twenty-Sixth Conference on Artificial. Intelligence (AAAI-12) ...
We show that any choice of an estimator can be used to probabilistically minimize regret, provided the estimator is bounded and unbiased. In addition, we relate ...
Additionally, exploring all of a traverser's actions helps reduce variance. However, external sampling may be impractical in games with extremely large ...
May 18, 2021 · Compared to outcome sam- pling algorithm, probe sampling algorithm attempts to reduce variance by replacing “zeroed-out” counterfactual values ...