Learning How to Play Black Jack Through Reinforcement Learning

1. Learning How to Play Black Jack Through Reinforcement Learning By: Jonathan Quenzer

2. Objectives • To have a computer learn how to play Blackjack through reinforcement learning • Computer starts off with no memory. After each hand is played, the computer learns more. • Goal is to have computer make the best possible decision of how much to bet and when to hit/stay • Splitting hands and doubling down will not be included. This decreases the odds of winning.

3. The Odds of Winning • The dealer has a 5-8% advantage depending on the specific rules without using card counting • The player through correct strategy and using card counting can obtain at most a 2% advantage on the dealer

4. Experimental Setup • I wrote a Matlab program to simulate Black Jack. • Feature vectors were generated by running the program and analyzing each hand played. • All of the features were scaled to have a mean of ½, minimum of 0, and maximum of 1.

5. Feature Set Generation = Classification

6. KNN classifier • Example of 5 nearest neighbors • Neighbors sum to +3, so decide to Hit

7. Results • Computer started with no knowledge • The player gained advantage over dealer using 10 nearest neighbors

8. Results • Computer simulated three players playing 1000 hands • Computer started with large feature set from 5000 hands