Reinforcement learning for Blackjack

Abstract

This paper explores the development of an Artificial Intelligence system for an already existing framework of card games, called SKCards, and the experimental results obtained from this. The current Artificial intelligence in the SKCards Blackjack is highly flawed. Reinforcement Learning was chosen as the method to be employed. Reinforcement Learning attempts to teach a computer certain actions, given certain states, based on past experience and numerical rewards gained. The agent either assigns values to states, or actions in states. This will initially be developed for Blackjack, with possible extensions to other games. Blackjack is one of the simpler games and the only current game in the SKCards package which needs an Artificial Intelligence agent. All the other games are single player. To test the performance of the Reinforcement Learning agent, several experiments were devised and run. © 2009 Springer.