Current poker tournament strategies often rely on the Independent Chip Model (ICM), which estimates players’ tournament equity based on chip stacks. However, traditional ICM models provide only a simplified approximation of tournament dynamics and are not directly integrated into practical decision-making agents. At the same time, recent advances in deep reinforcement learning (RL), particularly Deep Q-Networks (DQN), have demonstrated strong performance in sequential decision-making problems but have seen limited application in tournament poker settings.
To address this, this project proposes combining a learnable neural-network-based ICM module with a Deep Q-Network framework to develop more effective tournament poker agents. By training these networks using rewards that reflect tournament outcomes and prize distribution, the goal is to learn decision-making strategies that better capture tournament equity considerations and improve upon the limitations of classical ICM approaches. This hybrid architecture aims to produce stronger and more practical tournament-playing agents through deep reinforcement learning.