Comparison of Expectimax and Monte Carlo algorithms in Solving the online 2048 game

• Main Authors: Efrain Noa Yarasca, khoi Nguyen
• Format: Article
• Language: Spanish
• Published: Universidad Nacional Mayor de San Marcos 2018-09-01
• Series: Pesquimat
• Subjects: 2048 game; Expectimax algorithm, Monte Carlo algorithm; heuristics
• Online Access: http://revistasinvestigacion.unmsm.edu.pe/index.php/matema/article/view/15069

In this work, two search algorithms Expectimax and Monte Carlo Tree Search (MCTS) were developed to solve the well-known “2048" puzzle online-game and compare their results. In both cases, five heuristics were employed to obtain favorable tile positions within the game. These heuristics were combined to maximize the game-score in all possible board positions. As a result, the game-score, the maximum value of tile obtained, and the computing time employed in solving the game are shown. In addition, the efficiency of each algorithm and its sub-cases are presented. This research concludes by arguing that Monte Carlo Tree Search was more efficient in higher score than Expectimax algorithm, although in a longer time. Increments in level of depth-search in Expectimax and number of moves in MCTS do not necessarily resulted in obtaining higher score.