@article{tustanowski2025comparison,
  title = {Comparison of the Effectiveness of AI Models in Poker Games},
  author = {Mateusz TUSTANOWSKI and Adrian P. WOZNIAK},
  year = 2025,
  url = {https://ibimapublishing.com/p-articles/45AI/2025/4534725/},
  journal = {Communications of International Proceedings},
  volume = 2025 (20),
  doi = doi.org/10.5171/2025.4534725,
  abstract = {In this investigation, we evaluate the comparative efficacy of Random Forest (RF) classifiers and Deep Q-Network (DQN) agents in the context of No-Limit Texas Hold’em poker. Within a high-fidelity simulation, RF-based agents—trained through supervised learning on labeled gameplay data—demonstrated superior and more consistent profitability relative to their DQN counterparts, which were optimized via reinforcement learning [1, 2, 3]. The DQN models exhibited pronounced performance volatility and were particularly susceptible to variations in hyperparameter configuration [10]. These findings indicate that supervised approaches confer greater robustness in this domain [15]. Moreover, we propose that an integrated methodology—employing RF pretraining to establish a reliable baseline, followed by DQN fine-tuning to introduce adaptability—may yield an optimal trade-off between stability and learning flexibility.},
  keywords = {Machine Learning, Poker, Neural Networks},
  note = Article ID: 4534725
}