Measuring the Size of Large No-Limit Poker Games
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Abstract (arXiv)
In the field of computational game theory, games are often compared in terms of their size. This can be measured in several ways, including the number of unique game states, the number of decision points, and the total number of legal actions over all decision points. These numbers are either known or estimated for a wide range of classic games such as chess and checkers. In the stochastic and imperfect information game of poker, these sizes are easily computed in "limit" games which restrict the players' available actions, but until now had only been estimated for the more complicated "no-limit" variants. In this paper, we describe a simple algorithm for quickly computing the size of two-player no-limit poker games, provide an implementation of this algorithm, and present for the first time precise counts of the number of game states, information sets, actions and terminal nodes in the no-limit poker games played in the Annual Computer Poker Competition.
Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Feb 2013.
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Citing papers per year: 2021: 1; 2022: 3; 2023: 0; 2024: 3; 2025: 3; 2026: 4.
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- 2022Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learningcited by 129
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- 2024Layered and Staged Monte Carlo Tree Search for SMT Strategy Synthesiscited by 3
- 2025SpinGPT: A Large-Language-Model Approach to Playing Poker Correctlycited by 1
- 2025Deep (Predictive) Discounted Counterfactual Regret Minimizationcited by 1
- 2026AV-AIVAT: 74x Cheaper Agent Evaluation with Certified Anytime-Valid Stopping in Imperfect-Information Gamescited by 1
- 2026Abstraction Agentcited by 1
- 2022DecisionHoldem: Safe Depth-Limited Solving With Diverse Opponents for Imperfect-Information Gamescited by 0
- 2026Abstraction Agent3 Sep 2026
- 2026AV-AIVAT: 74x Cheaper Agent Evaluation with Certified Anytime-Valid Stopping in Imperfect-Information Games6 Aug 2026
- 2026PokerSkill: LLMs Can Play Expert-Level Poker without Training or Solvers28 May 2026
- 2026Domain-Independent Game Abstraction using Word Embedding Techniques15 May 2026
- 2025No-Regret Strategy Solving in Imperfect-Information Games via Pre-Trained Embedding15 Nov 2025
- 2025Deep (Predictive) Discounted Counterfactual Regret Minimization11 Nov 2025
- 2025SpinGPT: A Large-Language-Model Approach to Playing Poker Correctly26 Sep 2025
- 2024A Survey on Self-play Methods in Reinforcement Learning2 Aug 2024
- 2024RL-CFR: Improving Action Abstraction for Imperfect Information Extensive-Form Games with Reinforcement Learning7 Mar 2024
- 2024Layered and Staged Monte Carlo Tree Search for SMT Strategy Synthesis30 Jan 2024
- 2022Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning30 Jun 2022
- 2022Near-Optimal Learning of Extensive-Form Games with Imperfect Information3 Feb 2022
2 more
- 2022Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learningcited by 129
- 2022Near-Optimal Learning of Extensive-Form Games with Imperfect Informationcited by 26
- 2024A Survey on Self-play Methods in Reinforcement Learningcited by 26
- 2024RL-CFR: Improving Action Abstraction for Imperfect Information Extensive-Form Games with Reinforcement Learningcited by 10
- 2021Search in Imperfect Information Gamescited by 8
- 2026PokerSkill: LLMs Can Play Expert-Level Poker without Training or Solverscited by 5
- 2024Layered and Staged Monte Carlo Tree Search for SMT Strategy Synthesiscited by 3
- 2025SpinGPT: A Large-Language-Model Approach to Playing Poker Correctlycited by 1
- 2025Deep (Predictive) Discounted Counterfactual Regret Minimizationcited by 1
- 2022DecisionHoldem: Safe Depth-Limited Solving With Diverse Opponents for Imperfect-Information Gamescited by 0
- 2025No-Regret Strategy Solving in Imperfect-Information Games via Pre-Trained Embeddingcited by 0
- 2026Domain-Independent Game Abstraction using Word Embedding Techniquescited by 0
- 2026PokerSkill: LLMs Can Play Expert-Level Poker without Training or Solvers28 May 2026
- 2026Domain-Independent Game Abstraction using Word Embedding Techniques15 May 2026
- 2025No-Regret Strategy Solving in Imperfect-Information Games via Pre-Trained Embedding15 Nov 2025
- 2025Deep (Predictive) Discounted Counterfactual Regret Minimization11 Nov 2025
- 2025SpinGPT: A Large-Language-Model Approach to Playing Poker Correctly26 Sep 2025
- 2024A Survey on Self-play Methods in Reinforcement Learning2 Aug 2024
- 2024RL-CFR: Improving Action Abstraction for Imperfect Information Extensive-Form Games with Reinforcement Learning7 Mar 2024
- 2024Layered and Staged Monte Carlo Tree Search for SMT Strategy Synthesis30 Jan 2024
- 2022Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning30 Jun 2022
- 2022Near-Optimal Learning of Extensive-Form Games with Imperfect Information3 Feb 2022
- 2022DecisionHoldem: Safe Depth-Limited Solving With Diverse Opponents for Imperfect-Information Games27 Jan 2022
- 2021Search in Imperfect Information Games10 Nov 2021