Playing Tetris Using Bandit-Based Monte-Carlo Planning

Tetris is a stochastic, open-ended board game. Existing artificial Tetris players often use different evaluation functions and plan for only one or two pieces in advance. In this paper, we developed an artificial player for Tetris using the bandit-based Monte-Carlo planning method (UCT). In Tetris, game states are often revisited. However, UCT does not keep the information of the game states explored in the previous planning episodes. We created a method to store such information for our player in a specially designed database to guide its future planning process. The planner for Tetris has a high branching factor. To improve game performance, we created a method to prune the planning tree and lower the branching factor. The experiment results show that our player can successfully play Tetris, and the performance of our player is improved as the number of the played games increases. The player can defeat a benchmark player with high probabilities.