# Playing 2048 With Reinforcement Learning

arXiv: https://arxiv.org/abs/2110.10374 · HTML: https://mlsanity.com/paper/2110.10374/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 7 references that are in the map.
- First posted on arXiv 20 Oct 2021.

## Builds on (most cited first, 2 of 2)

- [RLCard: A Toolkit for Reinforcement Learning in Card Games](https://mlsanity.com/paper/1910.04376/) (2019)
- [Multi-Stage Temporal Difference Learning for 2048-like Games](https://mlsanity.com/paper/1606.07374/) (2016)

## Abstract (arXiv)

The game of 2048 is a highly addictive game. It is easy to learn the game, but hard to master as the created game revealed that only about 1% games out of hundreds million ever played have been won. In this paper, we would like to explore reinforcement learning techniques to win 2048. The approaches we have took include deep Q-learning and beam search, with beam search reaching 2048 28.5 of time.