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- W4226136593 abstract "Many recent results in imperfect information games were only formulated for, or evaluated on, poker and poker-like games such as liar's dice. We argue that sequential Bayesian games constitute a natural class of games for generalizing these results. In particular, this model allows for an elegant formulation of the counterfactual regret minimization algorithm, called public-state CFR (PS-CFR), which naturally lends itself to an efficient implementation. Empirically, solving a poker subgame with 10^7 states by public-state CFR takes 3 minutes and 700 MB while a comparable version of vanilla CFR takes 5.5 hours and 20 GB. Additionally, the public-state formulation of CFR opens up the possibility for exploiting domain-specific assumptions, leading to a quadratic reduction in asymptotic complexity (and a further empirical speedup) over vanilla CFR in poker and other domains. Overall, this suggests that the ability to represent poker as a sequential Bayesian game played a key role in the success of CFR-based methods. Finally, we extend public-state CFR to general extensive-form games, arguing that this extension enjoys some - but not all - of the benefits of the version for sequential Bayesian games." @default.
- W4226136593 created "2022-05-05" @default.
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- W4226136593 date "2021-12-20" @default.
- W4226136593 modified "2023-09-26" @default.
- W4226136593 title "Fast Algorithms for Poker Require Modelling it as a Sequential Bayesian Game" @default.
- W4226136593 doi "https://doi.org/10.48550/arxiv.2112.10890" @default.
- W4226136593 hasPublicationYear "2021" @default.
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