Comparison of the Effectiveness of AI Models in Poker Games

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.