Temporal difference learning of N-tuple networks for the game 2048

Abstract

The highly addictive stochastic puzzle game 2048 has recently invaded the Internet and mobile devices, stealing countless hours of players' lives. In this study we investigate the possibility of creating a game-playing agent capable of winning this game without incorporating human expertise or performing game tree search. For this purpose, we employ three variants of temporal difference learning to acquire i) action value, ii) state value, and iii) afterstate value functions for evaluating player moves at 1-ply. To represent these functions we adopt n-tuple networks, which have recently been successfully applied to Othello and Connect 4. The conducted experiments demonstrate that the learning algorithm using afterstate value functions is able to consistently produce players winning over 97% of games. These results show that n-tuple networks combined with an appropriate learning algorithm have large potential, which could be exploited in other board games.