# Learning to Play Stochastic Two-player Perfect-Information Games without Knowledge

arXiv: https://arxiv.org/abs/2302.04318 · HTML: https://mlsanity.com/paper/2302.04318/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 8 Feb 2023.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Ludii -- The Ludemic General Game System](https://mlsanity.com/paper/1905.05013/) (2019)
- [Polygames: Improved Zero Learning](https://mlsanity.com/paper/2001.09832/) (2020)
- [Learning to Play Two-Player Perfect-Information Games without Knowledge](https://mlsanity.com/paper/2008.01188/) (2020)
- [Minimax Strikes Back](https://mlsanity.com/paper/2012.10700/) (2020)
- [Completeness of Unbounded Best-First Game Algorithms](https://mlsanity.com/paper/2109.09468/) (2021)

## Built on by (most cited first, 1 of 1)

- [Minibal: Balanced Game-Playing Without Opponent Modeling](https://mlsanity.com/paper/2603.23059/) (2026)

## Abstract (arXiv)

In this paper, we extend the Descent framework, which enables learning and planning in the context of two-player games with perfect information, to the framework of stochastic games. We propose two ways of doing this, the first way generalizes the search algorithm, i.e. Descent, to stochastic games and the second way approximates stochastic games by deterministic games. We then evaluate them on the game EinStein wurfelt nicht! against state-of-the-art algorithms: Expectiminimax and Polygames (i.e. the Alpha Zero algorithm). It is our generalization of Descent which obtains the best results. The approximation by deterministic games nevertheless obtains good results, presaging that it could give better results in particular contexts.