Early poker AI relied on linear programming, which struggled with complex games like no limit Texas hold'em. The shift to reinforcement learning, combined with game theory concepts such as Nash equilibria, allowed for the development of strategies that adapt to hidden information and balance actions effectively. This approach ensures that players can achieve optimal strategies, making it possible to compete against the best in the world.
