Poker-CNN: A Pattern Learning Strategy for Making Draws and Bets in
  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.
