TexasHoldemAgent provides a modular environment built on Python to train, evaluate, and deploy an AI-powered poker player for heads-up limit Texas Hold’em. It integrates a custom simulation engine with deep reinforcement learning algorithms, including DQN, for iterative policy improvement. Key capabilities include hand state encoding, action space definition (fold, call, raise), reward shaping, and real-time decision evaluation. Users can customize learning parameters, leverage CPU/GPU acceleration, monitor training progress, and load or save trained models. The framework supports batch simulation to test various strategies, generate performance metrics, and visualize win rates, empowering researchers, developers, and poker enthusiasts to experiment with AI-driven gameplay strategies.