The Game of Tetris in Machine Learning
It addresses the problem of improving AI benchmarks for researchers, but is incremental as it primarily surveys past work.
This paper reviews the historical algorithmic developments in Tetris as a benchmark for AI and ML, noting that existing solutions still fall short of expert human performance without time pressure.
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.