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• W2890002687 abstract "Counterfactual regret minimization (CFR) is a family of iterative algorithms that are the most popular and, in practice, fastest approach to approximately solving large imperfect-information games. In this paper we introduce novel CFR variants that 1) discount regrets from earlier iterations in various ways (in some cases differently for positive and negative regrets), 2) reweight iterations in various ways to obtain the output strategies, 3) use a non-standard regret minimizer and/or 4) leverage optimistic regret matching. They lead to dramatically improved performance in many settings. For one, we introduce a variant that outperforms CFR+, the prior state-of-the-art algorithm, in every game tested, including large-scale realistic settings. CFR+ is a formidable benchmark: no other algorithm has been able to outperform it. Finally, we show that, unlike CFR+, many of the important new variants are compatible with modern imperfect-information-game pruning techniques and one is also compatible with sampling in the game tree." @default.
• W2890002687 created "2018-09-27" @default.
• W2890002687 creator A5006744201 @default.
• W2890002687 creator A5023571961 @default.
• W2890002687 date "2018-09-11" @default.
• W2890002687 modified "2023-09-27" @default.
• W2890002687 title "Solving Imperfect-Information Games via Discounted Regret Minimization" @default.
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• W2890002687 hasPublicationYear "2018" @default.
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