Reduced Space and Faster Convergence in Imperfect-Information Games via Regret-Based Pruning

AAAI Workshops, 2017.

Cited by: 4|Bibtex|Views22
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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

Counterfactual Regret Minimization (CFR) is the most popular iterative algorithm for solving zero-sum imperfect-information games. Regret-Based Pruning (RBP) is an improvement that allows poorly-performing actions to be temporarily pruned, thus speeding up CFR. We introduce Total RBP, a new form of RBP that reduces the space requirements ...More

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