Approximate Equilibrium Play in 4-Max Shove-or-Fold Poker: A Delayed-Linear Weighted-Averaging MCCFR Study at 8BB Effective Stacks

This technical note presents validated shove and call ranges for 4-player all-in-or-fold poker at 8 big-blind effective stacks. The strategy was produced using delayed-linear weighted-averaging Monte Carlo Counterfactual Regret Minimization (MCCFR), trained from iteration zero to 4,000,000 across two independent seeds and merged through the FullHouse production adapter. The resulting Production v2 policy passed a four-stage adoption protocol incorporating structural validation, NashConv evaluation, whole-game comparison against the previous production policy, and a frozen stress-cell regression. The latter returned a mixed result and is retained as adverse evidence. The published ranges should therefore be interpreted as a validated finite-compute estimate of equilibrium play rather than a formal proof of Nash equilibrium. Mixed-strategy frequencies displayed in the PDF are rounded to the nearest 25% for readability; the underlying policy retains exact frequencies. Feedback, corrections, methodological criticism and edge-case reports are welcome.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-07
DOI
https://doi.org/10.5281/zenodo.22650827
Primary Topic
Sports Analytics and Performance
Type
article
Field-Weighted Citation Impact
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article

Approximate Equilibrium Play in 4-Max Shove-or-Fold Poker: A Delayed-Linear Weighted-Averaging MCCFR Study at 8BB Effective Stacks

Henry De Courcy Thompson
Zenodo (CERN European Organization for Nuclear Research)
Sports Analytics and Performance
article

Approximate Equilibrium Play in 4-Max Shove-or-Fold Poker: A Delayed-Linear Weighted-Averaging MCCFR Study at 8BB Effective Stacks

Henry De Courcy Thompson
article en

Abstract

This technical note presents validated shove and call ranges for 4-player all-in-or-fold poker at 8 big-blind effective stacks. The strategy was produced using delayed-linear weighted-averaging Monte Carlo Counterfactual Regret Minimization (MCCFR), trained from iteration zero to 4,000,000 across two independent seeds and merged through the FullHouse production adapter. The resulting Production v2 policy passed a four-stage adoption protocol incorporating structural validation, NashConv evaluation, whole-game comparison against the previous production policy, and a frozen stress-cell regression. The latter returned a mixed result and is retained as adverse evidence. The published ranges should therefore be interpreted as a validated finite-compute estimate of equilibrium play rather than a formal proof of Nash equilibrium. Mixed-strategy frequencies displayed in the PDF are rounded to the nearest 25% for readability; the underlying policy retains exact frequencies. Feedback, corrections, methodological criticism and edge-case reports are welcome.

Zenodo (CERN European Organization for Nuclear Research)
H.B. Fuller (United States) (US), Westinghouse Electric (United States) (US)
Openalex Percentile: Top 5%
Sports Analytics and Performance
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