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Learning How to Play Learning How to Play

Learning How to Play - PowerPoint Presentation

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Uploaded On 2015-10-27

Learning How to Play - PPT Presentation

Black Jack Through Reinforcement Learning By Jonathan Quenzer 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 ID: 174443

computer hit hand feature hit computer feature hand player stay card set jack black deck dealer advantage number cards

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Slide1

Learning How to Play Black Jack Through Reinforcement Learning

By: Jonathan

QuenzerSlide2

To have a computer learn how to play Blackjack through reinforcement learningComputer 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.

ObjectivesSlide3

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

The Odds of WinningSlide4

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

.

Experimental SetupSlide5

Feature Set Generation

Hand History

Ace

The number of cards remaining in the deck for each value of card

2

10

PT

Player Total Without Aces

PANumber of Aces in Player's HandDSCThe Dealers Showing CardHit/Stay+1 = Hit, -1 = StayWin1.5 = Black Jack, 1 = Win, 0 = LoseLose1 = Lose, 0 = WinPush1 = Push, 0 = Win

Feature Set Hit/StayAceThe number of cards remaining in the deck for each value of card2…10CC Calculated Card Count: Hi-Opt II usedPTPlayer Total Without AcesPANumber of Aces in Player's HandDSCThe Dealers Showing CardHit/Stay+1 = Hit, -1 = Stay: Opposite of Hit/Stay from Hand History if the player lost

Feature Set Bet AmountAceThe number of cards remaining in the deck for each value of card2…10CC Calculated Card Count: Hi-Opt II usedBet Min/Max+1.5 if hand was Black Jack, +1 if won, -1 if lost

= ClassificationSlide6

Example of 5 nearest neighborsNeighbors sum to +3, so decide to Hit

KNN classifierSlide7

Computer started with no knowledgeThe player gained advantage over dealer using 10 nearest neighbors

ResultsSlide8

Computer simulated three players playing 1000 handsComputer started with large feature set from 5000 hands

Results