Generating Novice Heuristics for Post-Flop Poker

Agents now exist that can play Texas Hold'em Poker at a very high level, and simplified versions of the game have been solved. However, this does not directly translate to learning heuristics humans can use to play the game. We address the problem of learning chains of human-learnable heuristics for playing heads-up limit Texas Hold'em, focusing on the post-flop stages of the game. By restricting the policy space to fast and frugal trees, which are sequences of if-then-else rules, we can learn such heuristics using several methods including genetic programming. This work builds on our previous work on learning such heuristic rule set for Blackjack and pre-flop Texas Hold'em, but introduces a richer language for heuristics.