This project's mission is to create and use reinforcement learning (RL) methods to enhance two-player board game decision-making. By utilizing the concepts of reinforcement learning, including reward maximization, exploration, and exploitation, the objective is to develop an AI agent that can interact with the game environment and learn optimal or nearly optimal strategies.