The goal of this diploma thesis is finding the differences betwen human and
artifical agent in poker.
The first step was preforming research on the specific version of poker,
named No Limit Texas Holde’m and learning the rules of the game. The next
step was a creation of an intelligent poker agent, which is trained to play
the specified version of poker, using machine learning. We decided to use
an algorithm, which belongs in the family of machine learning algorithms,
known as reinforcement learning. The algorithm is called counterfactual
regret minimization. After the selection of the algorithm we trained the
intelligent agent and created two instances of the same agent. Those two
agents than played poker against each other and we were monitoring and
noting every move they made. When we generated enough games between
agents, we created a script for analysing the data. In the script we analysed
several aspects of the game, which were the basis for our conclusion on the
game style of certain virtual agent versus human player.
Our conclusion on the basis of the experiment is, that virtual agents,
trained with our algorithm, play much more actively and more aggressively
than humans. Active game in this context means, that a player plays many
games. Aggressive game means that the player bets a lot and often bluffs.
These findings are in line with the other researchers findings, which used
counter- factual regret minimization for creating an intelligent agent.
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