Generating beginner heuristics for simple texas hold'em

Abstract

Beginner heuristics for a game are simple rules that allow for effective playing. A chain of beginner heuristics of length N is the list of N rules that play the game best. Finding beginner heuristics is useful both for teaching a novice to play the game well and for understanding the dynamics of the game. We present and compare methods for finding beginner heuristics in a simple version of Poker: Pre-Flop Heads-Up Limit Texas Hold'em. We find that genetic programming outperforms greedy-exhaustive search and axis-aligned search in terms of finding well-playing heuristic chains of given length. We also find that there is a limited amount of non-transitivity when playing beginner heuristics of different lengths against each other, suggesting that while simpler heuristics are somewhat general, the more complex seem to overfit their training set.