AI Learns to Play 2-7 Triple Draw Poker Using Deep Learning

Deep learning has emerged as a promising approach to solving various problems in artificial intelligence. This method involves creating a system that can handle multiple tasks with minimal changes, much like how Google and Facebook use it for tagging photos. In a notable achievement, Google's DeepMind project taught an AI to play Atari games from the 1980s by interacting directly with the screen and console simulator.

The author of this article developed an AI to play 2-7 Triple Draw poker, a game popular at high stakes both online and offline. In 2-7 Triple Draw, players are dealt five private cards, and the goal is to form the weakest hand without pairs, straights, or flushes. The AI uses a method called bootstrapping, where it plays against itself to improve its game based on previous results. This approach allows the AI to learn and adapt continuously, much like how a human player would improve over time.

The AI analyzes the expected value of each action and makes decisions based on theoretical optimal play. For example, in a given scenario, the AI might choose to raise with a certain frequency, call with a higher frequency, and fold with a lower frequency. This strategic decision-making process is crucial for the AI to perform well in the game. The AI's ability to learn and adapt makes it a formidable opponent in 2-7 Triple Draw poker, showcasing the potential of deep learning in complex gaming environments.