real-time equilibrium computation∗

Abstract

We present a game theory-based heads-up Texas Hold’em poker player, GS1. To overcome the computational obsta-cles stemming from Texas Hold’em’s gigantic game tree, the player employs our automated abstraction techniques to re-duce the complexity of the strategy computations. Texas Hold’em consists of four betting rounds. Our player solves a large linear program (offline) to compute strategies for the abstracted first and second rounds. After the second bet-ting round, our player updates the probability of each pos-sible hand based on the observed betting actions in the first two rounds as well as the revealed cards. Using these up-dated probabilities, our player computes in real-time an equi-librium approximation for the last two abstracted rounds. We demonstrate that our player, which incorporates very little poker-specific knowledge, is competitive with leading poker-playing programs which incorporate extensive domain knowledge, as well as with advanced human players