# Deep Learning for General Game Playing with Ludii and Polygames

arXiv: https://arxiv.org/abs/2101.09562 · HTML: https://mlsanity.com/paper/2101.09562/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 34 references that are in the map.
- First posted on arXiv 23 Jan 2021.

## Builds on (most cited first, 12 of 12)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [OpenSpiel: A Framework for Reinforcement Learning in Games](https://mlsanity.com/paper/1908.09453/) (2019)
- [Combining Deep Reinforcement Learning and Search for Imperfect-Information Games](https://mlsanity.com/paper/2007.13544/) (2020)
- [Accelerating Self-Play Learning in Go](https://mlsanity.com/paper/1902.10565/) (2019)
- [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)
- [Multiplayer AlphaZero](https://mlsanity.com/paper/1910.13012/) (2019)
- [Ludii Game Logic Guide](https://mlsanity.com/paper/2101.02120/) (2021)

## Built on by (most cited first, 7 of 7)

- [Transfer of Fully Convolutional Policy-Value Networks Between Games and Game Variants](https://mlsanity.com/paper/2102.12375/) (2021)
- [Spatial State-Action Features for General Games](https://mlsanity.com/paper/2201.06401/) (2022)
- [AlphaZero-Inspired Game Learning: Faster Training by Using MCTS Only at Test Time](https://mlsanity.com/paper/2204.13307/) (2022)
- [Fast and Knowledge-Free Deep Learning for General Game Playing (Student Abstract)](https://mlsanity.com/paper/2312.14121/) (2023)
- [Visualising Multiplayer Game Spaces](https://mlsanity.com/paper/2202.05773/) (2022)
- [A Research Agenda for Usability and Generalisation in Reinforcement Learning](https://mlsanity.com/paper/2412.16970/) (2024)
- [Ludax: A GPU-Accelerated Domain Specific Language for Board Games](https://mlsanity.com/paper/2506.22609/) (2025)

## Abstract (arXiv)

Combinations of Monte-Carlo tree search and Deep Neural Networks, trained through self-play, have produced state-of-the-art results for automated game-playing in many board games. The training and search algorithms are not game-specific, but every individual game that these approaches are applied to still requires domain knowledge for the implementation of the game's rules, and constructing the neural network's architecture -- in particular the shapes of its input and output tensors. Ludii is a general game system that already contains over 500 different games, which can rapidly grow thanks to its powerful and user-friendly game description language. Polygames is a framework with training and search algorithms, which has already produced superhuman players for several board games. This paper describes the implementation of a bridge between Ludii and Polygames, which enables Polygames to train and evaluate models for games that are implemented and run through Ludii. We do not require any game-specific domain knowledge anymore, and instead leverage our domain knowledge of the Ludii system and its abstract state and move representations to write functions that can automatically determine the appropriate shapes for input and output tensors for any game implemented in Ludii. We describe experimental results for short training runs in a wide variety of different board games, and discuss several open problems and avenues for future research.