# Application of Self-Play Reinforcement Learning to a Four-Player Game of Imperfect Information

arXiv: https://arxiv.org/abs/1808.10442 · HTML: https://mlsanity.com/paper/1808.10442/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2018.

## Built on by (most cited first, 4 of 4)

- [Valet: A Standardized Testbed of Traditional Imperfect-Information Card Games](https://mlsanity.com/paper/2603.03252/) (2026)
- [From Pixels to Digital Agents: An Empirical Study on the Taxonomy and Technological Trends of Reinforcement Learning Environments](https://mlsanity.com/paper/2603.23964/) (2026)
- [Self-Play Reinforcement Learning under Imperfect Information in Big 2](https://mlsanity.com/paper/2605.28863/) (2026)
- [Learning to Play: Reinforcement Learning and Games](https://mlsanity.com/paper/2610.17391/) (2026)

## Abstract (arXiv)

We introduce a new virtual environment for simulating a card game known as "Big 2". This is a four-player game of imperfect information with a relatively complicated action space (being allowed to play 1,2,3,4 or 5 card combinations from an initial starting hand of 13 cards). As such it poses a challenge for many current reinforcement learning methods. We then use the recently proposed "Proximal Policy Optimization" algorithm to train a deep neural network to play the game, purely learning via self-play, and find that it is able to reach a level which outperforms amateur human players after only a relatively short amount of training time and without needing to search a tree of future game states.