Creating A Poekr AI In Python

Kevin Jacobs has a fairly simple framework for building poker-playing bots:

The bot uses Monte Carlo simulations running from a given state. Suppose you start with 2 high cards (two Kings for example), then the chances are high that you will win. The Monte Carlo simulation then simulates a given number of games from that point and evaluates which percentage of games you will win given these cards. If another King shows during the flop, then your chance of winning will increase. The Monte Carlo simulation starting at that point, will yield a higher winning probability since you will win more games on average.

If we run the simulations, you can see that the bot based on Monte Carlo simulations outperforms the always calling bot. If you start with a stack of $100,-, you will on average end with a stack of $120,- (when playing against the always-calling bot).

It’s a start, and an opening for more sophisticated logic and analysis.

Related Posts

Defining Result Sets With ML Services

Dave Mason covers a pain point in SQL Server Machine Learning Services: The example above is so simple, defining the RESULT SETS poses no problems. But what if the format of the output isn’t known at design time? R (or Python) might take the input data set and add, remove, or change columns conditionally. Further, […]

Read More

Interpreting P-Value Histograms

David Robinson visualizes and interprets different p-value histograms: So you’re a scientist or data analyst, and you have a little experience interpreting p-values from statistical tests. But then you come across a case where you have hundreds, thousands, or evenĀ millionsĀ of p-values. Perhaps you ran a statistical test on each gene in an organism, or on […]

Read More

Leave a Reply

Your email address will not be published. Required fields are marked *

Categories

November 2017
MTWTFSS
« Oct  
 12345
6789101112
13141516171819
20212223242526
27282930