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

Authors

  • Efrain Noa Yarasca Oregon State University, School of CCE, Corvallis, USA.
  • khoi Nguyen Oregon State University, School of CCE, Corvallis,USA.

DOI:

https://doi.org/10.15381/pes.v21i1.15069

Keywords:

2048 game, Expectimax algorithm, Monte Carlo algorithm, heuristics

Abstract

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.

Author Biographies

  • Efrain Noa Yarasca, Oregon State University, School of CCE, Corvallis, USA.
    Oregon State University, School of CCE, Corvallis, OR 97331,USA.
  • khoi Nguyen, Oregon State University, School of CCE, Corvallis,USA.
    Oregon State University, School of CCE, Corvallis, OR 97331,USA.

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Published

2018-09-10

Issue

Vol. 21 No. 1 (2018)

Section

Artículos originales

License

Copyright (c) 2018 Efrain Noa Yarasca, khoi Nguyen

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How to Cite

Comparison of Expectimax and Monte Carlo algorithms in Solving the online 2048 game. (2018). Pesquimat, 21(1), 1-10. https://doi.org/10.15381/pes.v21i1.15069