How does an AI poker bot develop a database through programming?

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Artificial Intelligence based poker bots, or AI as it is usually known, have taken online poker by storm, and you may even be playing tournament poker against them without knowing. Like all technology though, if you are planning on using a poker bot it can really help to understand how they work to get the most from them. They don’t think like a human player does, but instead rely on data to help them understand each hand played.

For good poker bots, there are usually a number of profiles and strategic approaches that can be adjusted by the user, to get subtly different play styles to suit specific games, platforms or opponents. However, when it comes to AI systems, the biggest factor in performance is the database used. That means the best way to go to get great performance is to make sure your bot has the best database possible.

Importance of Programming a Database for Artificial Intelligence Poker Bots

If we think of the AI itself as a driver in charge of a sports car, it controls the speed, direction and so on as it goes, adapting to the road conditions to make the most efficient progress. The database would be the engine, providing all the power that moves everything along.

Without a database of games to draw on, the AI cannot recognize situations, calculate probabilities for the next stage of the hand and make choices of the best strategy to adopt. Imagine that you lost your memory regarding everything to do with poker, what the best hands are, the rules, what hands are strong, how to deal with different players, all of it. Then you tried to play at an online poker table. Do you think you would win?

The answer is, no, you would be lost. You have no memory of the game to determine what to do next, and it’s the same if an AI system plays without a database which is why they are so important for success with poker bots.

 Machine Learning Programming for Poker and Chess

In finite, two-player games, such as chess and checkers, there is a strategy known as the Nash Equilibrium that represents the perfect strategy for the game. That is, by playing that way you cannot improve how you approach the game. Even in two player poker, there is some kind of equilibrium that can be thought of as a Nash equilibrium, because it is a zero-sum game. That is, for one player to win $X, the other player must lose it.

But most poker bots are working texas holdem tables with multiple players on commercial poker sites, and with many unknown (the other player’s hole cards) elements. Here, rather than identifying the Nash equilibrium as with simpler games, scientists developed AI algorithms that learned through experience. Essentially, the bot plays itself over and over again, recognizing successful strategies and building a database of hands that allow it to look for similar situations and quantify likely outcomes.

AI Modeling Used in Poker Bots

To begin with, AI poker bots were focused on the game rules and little else. They could play poker, but would be unlikely to succeed in actual games. From there, poker AI has developed rapidly, incorporating more and more data to provide a clearer picture of the game and identify appropriate strategies in real time. The first poker bot to really accomplish this was Pluribus, which could beat human players with reliability.

Not only could Pluribus play poker online and win, the AI would impliment strategies to counter a bluff, and deal with an opponent based on how they were playing at that time. From the initial blind to the final call, the architecture of this AI could take advantage of player errors and use complex algorithms to produce the desired outcomes.

Role of Data in Training Poker Bots

As we have discussed, the database is key to any poker bot being able to assess a hand and play an appropriate strategy. When initially set up, a poker bot has no understanding of the game, as it trains it builds that database, and the larger the database, the more reference points the AI has, making its choices more accurate and successful.

Data is what drives the AI’s understanding of poker, not just in the broad context of the game and its rules, but in the more focused context of the hand being played at that moment. If we think of a simple desicion to bet after the flop, a computer playing the game must pass through a number of desciion trees to come to a final action. Each of those choices, and there might be hundreds, is made by analysing available data. 

A human brain does these things without us really understanding that is what we are doing, but computer programs like AI and machine learning must be taught to do it. Data provides various options by showing what has happened before in different types of hands, so that the AI can assess what is likely to happen going forwards. The neural network and database together build gaming optimal strategies using a large number of datapoints for each choice and arrive at a solution that has the best probability of success.

Training poker bots for winning

When we talk of training an AI of any kind, this refers to the building of knowledge about a specific subject or action. For poker, that means building a database of hands played. The more scenarios, situations and strategies that the bot experiences and stores in its database, the better it can understand and recognize player strategies and develop responses.

With the latest poker bots, this is usually accomplished by the bot playing itself for thousands of hands. Very little information is programmed, the AI learns the game, and how to beat itself, by playing. The more it plays, the more it learns, and the more effective it becomes.

Techniques for Teaching the Bot to Recognize Banned Actions in their Environment

Each platform has a wide range of rules to adhere to, and they may not always be the same. Avoiding actions that are banned is crucial to a poker bot staying undetected and operating under the radar. There are a couple of approaches to this, by setting parameters within the code that prevent the bot performing banned actions, or by playing and punishing banned actions so the AI learns to avoid them.

Testing and Validation to Detect Issues

Training a poker bot is essential before you let it loose on real tables, but how do you know when it is ready for playing against human opponents on live tables? Testing and validation are processes that assess performance and overall operations, so that the bot can perform reliably, avoid banned actions and stay within the rules. It should also perform without seeming like an automated player, as most of the platforms today ban bots as part of their rules.

The key metric to test is exploitability, that is can players exploit the way the AI plays, because ultimately, that will result in losing, and probably losing badly. There are a couple of ways of doing this, some like to play the bot and see if they can find those weaknesses, which can work, but most research teams that create AI poker bots tend to compute exploitability using data from play sessions.

FAQ

What is the purpose of a database for an AI poker bot?

The database provides all the experience and game understanding the AI uses to play the game.

How does an AI poker bot collect and store data for its database?

Bots create a database of each hand as they play. In most cases, this is built from the poker bot playing itself at first, and later from each hand it plays on real tables.

What types of information are typically included in an AI poker bot’s database?

All aspects of each hand, from the buy in and pre-flop action, the flop and subsequent strategies, who wins, how much and so on. From pot odds to player data, each bot will use as much data as possible to forumlate a response.

How does the AI poker bot decide which data to prioritize and include in its database?

That depends on the AI itself and how it was created. Self-learning AI systems will focus on the data that presents the most varied within games while affecting the outcome of each hand.

Can you explain how an AI poker bot learns from its database to improve its gameplay?

AI works from datapoints. In the case of poker, this refers to the way players respond to situations, how a call or bet affects the outcome of a post flop betting round and so on. The more data points the AI has, the more accurately it can predict what will happen next.

How do developers verify the effectiveness of an AI poker bot’s database in enhancing its performance?

Tracking all kids of performance metrics should be an integral part of operating a poker bot. From win rates to aggression statistics and more, after each session you can assess bot performance and see how it is improving as its database grows.

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