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DeepStack: continual re-solving with learned counterfactual values

Contribution grade

Imperfect-information game reasoning · Human-designed research method or resource

Reference score180,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Combined continual re-solving, learned counterfactual value functions and depth/action-limited search for heads-up no-limit poker, demonstrating professional-player performance in the reported study.

Scientific scope

Combined continual re-solving, learned counterfactual value functions and depth/action-limited search for heads-up no-limit poker, demonstrating professional-player performance in the reported study. Distinct from visual-token DeepStack. Source soundness theorem assumes bounded value-function error and adequate CFR iterations; the practical sparse action-tree restriction explicitly voids that theorem's guarantee. Thus professional-match results are empirical, not a certified exact Nash solution or zero exploitability. CFR and earlier re-solving are inherited.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Distinct from visual-token DeepStack. Source soundness theorem assumes bounded value-function error and adequate CFR iterations; the practical sparse action-tree restriction explicitly voids that theorem's guarantee. Thus professional-match results are empirical, not a certified exact Nash solution or zero exploitability. CFR and earlier re-solving are inherited.

Contributors

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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