Beyond Game Theory Optimal: Profit-Maximizing Poker Agents for No-Limit Holdem
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Abstract (arXiv)
Game theory has grown into a major field over the past few decades, and poker has long served as one of its key case studies. Game-Theory-Optimal (GTO) provides strategies to avoid loss in poker, but pure GTO does not guarantee maximum profit. To this end, we aim to develop a model that outperforms GTO strategies to maximize profit in No Limit Holdem, in heads-up (two-player) and multi-way (more than two-player) situations. Our model finds the GTO foundation and goes further to exploit opponents. The model first navigates toward many simulated poker hands against itself and keeps adjusting its decisions until no action can reliably beat it, creating a strong baseline that is close to the theoretical best strategy. Then, it adapts by observing opponent behavior and adjusting its strategy to capture extra value accordingly. Our results indicate that Monte-Carlo Counterfactual Regret Minimization (CFR) performs best in heads-up situations and CFR remains the strongest method in most multi-way situations. By combining the defensive strength of GTO with real-time exploitation, our approach aims to show how poker agents can move from merely not losing to consistently winning against diverse opponents.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 22 references that are in the map. First posted on arXiv 28 Sep 2025.
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References · 10
- 2017A Unified Game-Theoretic Approach to Multiagent Reinforcement Learningcited by 268
- 2019OpenSpiel: A Framework for Reinforcement Learning in Gamescited by 186
- 2016Deep Reinforcement Learning from Self-Play in Imperfect-Information Gamescited by 158
- 2020Combining Deep Reinforcement Learning and Search for Imperfect-Information Gamescited by 114
- 2018Deep Counterfactual Regret Minimizationcited by 103
- 2014Solving Large Imperfect Information Games Using CFR+cited by 78
- 2018Solving Imperfect-Information Games via Discounted Regret Minimizationcited by 68
- 2019Computing Approximate Equilibria in Sequential Adversarial Games by Exploitability Descentcited by 31
- 2020Approximate exploitability: Learning a best response in large gamescited by 23
- 2019Low-Variance and Zero-Variance Baselines for Extensive-Form Gamescited by 9
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