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

Lasso and Ridge Regression in Python

Kristian Larsen shows off a few regression techniques using Python: Variables with a regression coefficient equal to zero after the shrinkage process are excluded from the model. Variables with non-zero regression coefficients variables are most strongly associated with the response variable. Therefore, when you conduct a regression model it can be helpful to do a […]

Read More

Using Cohen’s D for Experiments

Nina Zumel takes us through Cohen’s D, a useful tool for determining effect sizes in experiments: Cohen’s d is a measure of effect size for the difference of two means that takes the variance of the population into account. It’s defined asd = | μ1 – μ2 | / σpooledwhere σpooled is the pooled standard deviation over both cohorts. […]

Read More

Categories

November 2017
MTWTFSS
« Oct Dec »
 12345
6789101112
13141516171819
20212223242526
27282930