Poker-CNN: A Pattern Learning Strategy for Making Draws and Bets in Poker Games
This work addresses the challenge of developing adaptable AI for various poker games, offering a domain-specific solution that is incremental in applying existing methods to new contexts.
The paper tackled the problem of creating a strong poker playing system by treating poker as a pattern matching problem, proposing a unified representation and CNN-based model that learns through self-play, achieving competitive performance against human experts in three poker games.
Poker is a family of card games that includes many variations. We hypothesize that most poker games can be solved as a pattern matching problem, and propose creating a strong poker playing system based on a unified poker representation. Our poker player learns through iterative self-play, and improves its understanding of the game by training on the results of its previous actions without sophisticated domain knowledge. We evaluate our system on three poker games: single player video poker, two-player Limit Texas Hold'em, and finally two-player 2-7 triple draw poker. We show that our model can quickly learn patterns in these very different poker games while it improves from zero knowledge to a competitive player against human experts. The contributions of this paper include: (1) a novel representation for poker games, extendable to different poker variations, (2) a CNN based learning model that can effectively learn the patterns in three different games, and (3) a self-trained system that significantly beats the heuristic-based program on which it is trained, and our system is competitive against human expert players.