The current most popular variant of poker, played in casinos and seen on television, is no-limit Texas hold'em. This game and a smaller variant, limit Texas hold'em, have been used as a testbed for artificial intelligence research since 1997. Since 2006, the Annual Computer Poker Competition has allowed researchers, programmers, and poker players to play their poker programs against each other, allowing us to find out which artificial intelligence techniques work best in practice. The competition has resulted in significant advances in fields such as computational game theory, and resulted in algorithms that can find optimal strategies for games six orders of magnitude larger than was possible using earlier techniques.
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In this tutorial, you will learn step-by-step how to implement a poker bot in Python. First, we need an engine in which we can simulate our poker bot. It also has a GUI available which can graphically display a game. Both the engine and the GUI have excellent tutorials on their GitHub pages in how to use them. The choice for the engine (and/or the GUI) is arbitrary and can be replaced by any engine (and/or GUI) you like.
The task of computing approximate Nash equilibria in large zero-sum extensive-form games has received a tremendous amount of attention due mainly to the Annual Computer Poker Competition. Immediately after its inception, two competing and seemingly different approaches emerged---one an application of no-regret online learning, the other a sophisticated gradient method applied to a convex-concave saddle-point formulation. Since then, both approaches have grown in relative isolation with advancements on one side not effecting the other. In this paper, we rectify this by dissecting and, in a sense, unify the two views.