Deep Counterfactual Regret Minimization

international conference on machine learning, 2018.

Cited by: 37|Bibtex|Views24
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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

Counterfactual Regret Minimization (CFR) is the leading algorithm for solving large imperfect-information games. It iteratively traverses the game tree in order to converge to a Nash equilibrium. In order to deal with extremely large games, CFR typically uses domain-specific heuristics to simplify the target game in a process known as abs...More

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