  The game of Tetris is an important benchmark for research in artificial
intelligence and machine learning. This paper provides a historical account of
the algorithmic developments in Tetris and discusses open challenges.
Handcrafted controllers, genetic algorithms, and reinforcement learning have
all contributed to good solutions. However, existing solutions fall far short
of what can be achieved by expert players playing without time pressure.
Further study of the game has the potential to contribute to important areas of
research, including feature discovery, autonomous learning of action
hierarchies, and sample-efficient reinforcement learning.
