A Deterministic Neural Network Approach to Playing Gin Rummy

Abstract This paper describes a deterministic approach to building a fixed-strategy gin rummy player. In the paper, we develop and evaluate both heuristic and neural network models for informing draw, discard, and knock decisions in the game. In this empirical study, we test performance of the models through competitive game play, show which best inform strategy, and demonstrate statistical significance of the improvement over a simple strategy. Through this empirical study, we indicate features that we expect to be helpful in future improvements to Gin Rummy play.