# The Game of Tetris in Machine Learning

arXiv: https://arxiv.org/abs/1905.01652 · HTML: https://mlsanity.com/paper/1905.01652/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2019.

## Built on by (most cited first, 3 of 3)

- [Minimax PAC Bounds for Learning in Exogenous Contextual MDPs](https://mlsanity.com/paper/2606.25170/) (2026)
- [Same Pieces, Different Servers: A Tetris Benchmark for AI Agents as Served](https://mlsanity.com/paper/2603.02348/) (2026)
- [OpenBlock: Constructive and Verified Content Generation for Adaptive Tile-Matching Games](https://mlsanity.com/paper/2609.22177/) (2026)

## Abstract (arXiv)

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.