# Valet: A Standardized Testbed of Traditional Imperfect-Information Card Games

arXiv: https://arxiv.org/abs/2603.03252 · HTML: https://mlsanity.com/paper/2603.03252/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 3 Mar 2026.

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

- [OpenSpiel: A Framework for Reinforcement Learning in Games](https://mlsanity.com/paper/1908.09453/) (2019)
- [Monte Carlo Tree Search: A Review of Recent Modifications and Applications](https://mlsanity.com/paper/2103.04931/) (2021)
- [Deep Counterfactual Regret Minimization](https://mlsanity.com/paper/1811.00164/) (2018)
- [RLCard: A Toolkit for Reinforcement Learning in Card Games](https://mlsanity.com/paper/1910.04376/) (2019)
- [Application of Self-Play Reinforcement Learning to a Four-Player Game of Imperfect Information](https://mlsanity.com/paper/1808.10442/) (2018)
- [Survey of Artificial Intelligence for Card Games and Its Application to the Swiss Game Jass](https://mlsanity.com/paper/1906.04439/) (2019)
- [Modeling Uncertainty: Constraint-Based Belief States in Imperfect-Information Games](https://mlsanity.com/paper/2507.19263/) (2025)

## Abstract (arXiv)

AI algorithms for imperfect-information games are typically compared using performance metrics on individual games, making it difficult to assess robustness across game choices. Card games are a natural domain for imperfect information due to hidden hands and stochastic draws. To facilitate comparative research on imperfect-information game-playing algorithms and game systems, we introduce Valet, a diverse and comprehensive testbed of 21 traditional imperfect-information card games. These games span multiple genres, cultures, player counts, deck structures, mechanics, winning conditions, and methods of hiding and revealing information. To standardize implementations across systems, we encode the rules of each game in RECYCLE, a card game description language. We empirically characterize each game's branching factor and duration using random simulations, reporting baseline score distributions for a Monte Carlo Tree Search player against random opponents to demonstrate the suitability of Valet as a benchmarking suite.