WEVA uses short CFR warm-up runs to build expected-value feature vectors for k-means clustering, yielding abstractions that reduce exploitability by up to 80% compared with equity- or rank-based methods across three games.
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Parallel CFR achieves 3.3-3.4x speedup and 47-54 ms per iteration for real-time depth-limited CFR on Heads-Up No-Limit Texas Hold'em with over one billion histories.
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Effective, Efficient, and General Information Abstraction for Imperfect-Information Extensive-Form Games
WEVA uses short CFR warm-up runs to build expected-value feature vectors for k-means clustering, yielding abstractions that reduce exploitability by up to 80% compared with equity- or rank-based methods across three games.
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Real-Time Parallel Counterfactual Regret Minimization
Parallel CFR achieves 3.3-3.4x speedup and 47-54 ms per iteration for real-time depth-limited CFR on Heads-Up No-Limit Texas Hold'em with over one billion histories.