Equilibrium Approximation Quality of Current No-Limit Poker Bots
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Abstract (arXiv)
Approximating a Nash equilibrium is currently the best performing approach for creating poker-playing programs. While for the simplest variants of the game, it is possible to evaluate the quality of the approximation by computing the value of the best response strategy, this is currently not computationally feasible for larger variants of the game, such as heads-up no-limit Texas hold'em. In this paper, we present a simple and computationally inexpensive Local Best Response method for computing an approximate lower bound on the value of the best response strategy. Using this method, we show that existing poker-playing programs, based on solving abstract games, are remarkably poor Nash equilibrium approximations.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Dec 2016.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 1.
3 papers.
- 2025Beyond Outcome-Based Imperfect-Recall: Higher-Resolution Abstractions for Imperfect-Information Gamescited by 1
- 2022Finding mixed-strategy equilibria of continuous-action games without gradients using randomized policy networkscited by 0
- 2023ApproxED: Approximate exploitability descent via learned best responsescited by 0
- 2025Beyond Outcome-Based Imperfect-Recall: Higher-Resolution Abstractions for Imperfect-Information Games16 Oct 2025
- 2023ApproxED: Approximate exploitability descent via learned best responses20 Jan 2023
- 2022Finding mixed-strategy equilibria of continuous-action games without gradients using randomized policy networks29 Nov 2022