We examined the sequence of decision problems that are encountered in the game of Tetris and found that most of the problems are easy in the following sense: One can choose well among the available actions without knowing an evaluation function that scores well in the game. This is a consequence of three conditions that are prevalent in the game: simple dominance, cumulative dominance, and noncompensation. These conditions can be exploited to develop faster and more effective learning algorithms. In addition, they allow certain types of domain knowledge to be incorporated with ease into a learning algorithm. Among the sequential decision problems we encounter, it is unlikely that Tetris is unique or rare in having these properties.
|Title of host publication||Proceedings of The 33rd International Conference on Machine Learning|
|Number of pages||9|
|Publication status||Published - 30 Jun 2016|
|Name||Proceedings of Machine Learning Research|
- sequential decision making; reinforcement learning; statistical structure of decision environments
Şimşek, Ö., Algorta, S., & Kothiyal, A. (2016). Why most decisions are easy in Tetris—And perhaps in other sequential decision problems, as well. In Proceedings of The 33rd International Conference on Machine Learning (Vol. 48, pp. 1757-1765). (Proceedings of Machine Learning Research). PMLR.