Projects
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CFR Simulation
Watch a poker bot learn Kuhn poker from scratch via vanilla Counterfactual Regret Minimization. Includes a glossary and live math walkthrough.
/cfr-simulation → CFR+CFR+ on Kuhn Poker
Floor cumulative regret at zero and use weighted strategy averaging — converges roughly an order of magnitude faster than vanilla CFR. Tammelin, 2014.
/cfr-plus → DCFRDiscounted CFR
Discount past regret and strategy contributions with α/β/γ weights. The modern default for tabular CFR. Brown & Sandholm, 2019.
/dcfr → MCCFRExternal Sampling MCCFR
Sample a single chance outcome per iteration instead of walking all 6 deals. Trades variance for speed. Lanctot et al., 2009.
/mccfr → SamplingMCCFR Variants
Side-by-side comparison of Chance, External, and Outcome sampling — three ways to sample the tree, one clear winner in practice.
/mccfr-variants → ConceptDeep CFR Explained
Conceptual walkthrough of how CFR drops its lookup table for a neural-network function approximator — and why that lets it scale beyond toy games.
/deep-cfr-explained → Deep CFRDeep CFR on Kuhn Poker
Tabular regret tables replaced by a neural network. Same convergence guarantees, no per-info-set bookkeeping. Brown, Lerer, Gross, Sandholm — ICML 2019.
/deep-cfr → MetricsConvergence Metrics
Exploitability, average-strategy distance, and other indicators of how CFR approaches Nash equilibrium.
/convergence-metrics → PokerBadugi Tutorial
Rules, strategy, and hand rankings for Badugi — a four-card lowball draw game.
/badugi →