Counterfactual Regret Minimization (CFR) Algorithm

A Counterfactual Regret Minimization (CFR) Algorithm is a [[]].

• See: Regret Minimization, Zero-Sum Imperfect Information Game.

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References

2009

• (Lanctot et al., 2009) ⇒ Marc Lanctot, Kevin Waugh, Martin Zinkevich, and Michael Bowling. (2009). “Monte Carlo Sampling for Regret Minimization in Extensive Games.” In: Proceedings of the 22nd International Conference on Neural Information Processing Systems. ISBN:978-1-61567-911-9
  • QUOTE: Sequential decision-making with multiple agents and imperfect information is commonly modeled as an extensive game. One efficient method for computing Nash equilibria in large, zero-sum, imperfect information games is counterfactual regret minimization (CFR). In the domain of poker, CFR has proven effective, particularly when using a domain-specific augmentation involving chance outcome sampling. In this paper, we describe a general family of domain-independent CFR sample-based algorithms called Monte Carlo counterfactual regret minimization (MCCFR) of which the original and poker-specific versions are special cases.

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