Abstract
Language | English |
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Title of host publication | Proceedings of the 33nd International Conference on Machine Learning (ICML 2016), New York City, NY, USA, June 19-24, 2016 |
Editors | Maria-Florina Balcan, Kilian Q. Weinberger |
Pages | 1757-1765 |
Number of pages | 9 |
Status | Published - 2016 |
Publication series
Name | JMLR Workshop and Conference Proceedings |
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Publisher | JMLR.org |
Volume | 48 |
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Keywords
- sequential decision making; reinforcement learning; statistical structure of decision environments
Cite this
Why most decisions are easy in Tetris—And perhaps in other sequential decision problems, as well. / Şimşek, Özgür; Algorta, Simón; Kothiyal, Amit.
Proceedings of the 33nd International Conference on Machine Learning (ICML 2016), New York City, NY, USA, June 19-24, 2016. ed. / Maria-Florina Balcan; Kilian Q. Weinberger. 2016. p. 1757-1765 (JMLR Workshop and Conference Proceedings; Vol. 48).Research output: Chapter in Book/Report/Conference proceeding › Chapter
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TY - CHAP
T1 - Why most decisions are easy in Tetris—And perhaps in other sequential decision problems, as well
AU - Şimşek, Özgür
AU - Algorta, Simón
AU - Kothiyal, Amit
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
KW - sequential decision making; reinforcement learning; statistical structure of decision environments
UR - http://proceedings.mlr.press/v48/simsek16.html
M3 - Chapter
T3 - JMLR Workshop and Conference Proceedings
SP - 1757
EP - 1765
BT - Proceedings of the 33nd International Conference on Machine Learning (ICML 2016), New York City, NY, USA, June 19-24, 2016
A2 - Balcan, Maria-Florina
A2 - Weinberger, Kilian Q.
ER -