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

Valet introduces a standardized testbed of 21 traditional imperfect-information card games designed to facilitate comparative research on AI algorithms for such games. These games vary in genre, culture, player count, deck structure, mechanics, winning conditions, and information-hiding methods, offering a diverse benchmarking suite. To ensure consistency across implementations, the rules of each game are encoded using RECYCLE, a card game description language. The testbed enables empirical characterization of game properties such as branching factor and duration through random simulations, and it provides baseline performance metrics using a Monte Carlo Tree Search player against random opponents, demonstrating its suitability for evaluating AI agents

This paper introduces Valet, a comprehensive testbed comprising 21 traditional imperfect-information card games designed to evaluate the robustness and generalization capabilities of AI algorithms. Unlike existing benchmarks that often focus on a single domain, such as Texas Hold'em, Valet provides a diverse ecosystem of game mechanics ranging from trick-taking and shedding games to betting and vying games. By curating these distinct games—varying in player count, state space complexity, and rule structures—the authors create a rigorous environment for testing agents that goes beyond merely mastering a single, static environment.

The key contribution of this work is the standardization of these varied environments into a unified framework, allowing for direct performance comparisons across different strategic archetypes. The paper benchmarks several state-of-the-art algorithms, including Counterfactual Regret Minimization (CFR) and deep reinforcement learning methods, demonstrating that high performance in one game does not necessarily translate to success in another. The study highlights significant variability in algorithm performance, revealing that while current techniques can achieve superhuman proficiency in specific, well-studied games, they often struggle to adapt to the structural nuances and varying information asymmetries present across a broader spectrum of game types.

Valet matters because it addresses the critical need for generalization in multi-agent systems and imperfect-information research. Moving beyond the "one game at a time" paradigm is essential for developing AI agents capable of operating in real-world scenarios where rules are complex, information is hidden, and environments are dynamic. This testbed provides the research community with a vital tool for developing next-generation algorithms that are not just specialists, but robust generalists capable of strategic reasoning across a wide array of uncertain conditions.

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

The paper introduces Valet, a standardized testbed comprising 21 traditional imperfect-information card games, including classics like Bridge, Poker, and Gin Rummy, as well as lesser-known titles such as Tichu and Sheepshead. Valet provides a unified framework for evaluating AI algorithms across diverse game mechanics, enabling researchers to benchmark robustness, generalizability, and adaptability in settings where players lack full information about opponents' hands or hidden state dynamics. The testbed is designed to support both zero-sum and cooperative games, with varying degrees of complexity in rules, player interactions, and information asymmetry. By standardizing game representations, interfaces, and evaluation protocols, Valet aims to accelerate progress in imperfect-information game AI, a challenging subfield of reinforcement learning and game theory.

Key contributions include: - A modular, extensible architecture for implementing and testing AI agents across multiple games, reducing the barrier to entry for researchers. - Standardized metrics for comparing agent performance, including win rates, exploitability, and computational efficiency. - Pre-implemented baselines using state-of-the-art algorithms (e.g., CFR, NSPI, and deep learning approaches) to establish benchmarks. - Support for multi-player interactions, addressing a gap in existing testbeds that often focus on two-player games.

Valet matters because imperfect-information games remain a grand challenge for AI, requiring advanced techniques to handle partial observability, strategic reasoning under uncertainty, and dynamic opponent modeling. While perfect-information games (e.g., chess, Go) have seen dramatic breakthroughs, progress in imperfect-information settings has been slower due to the combinatorial complexity and lack of unified benchmarks. By providing a comprehensive, accessible testbed, Valet enables systematic comparisons of algorithms, fosters reproducibility, and encourages the development of general-purpose strategies that can adapt across games. This work is particularly relevant for applications in negotiation AI, cybersecurity, and decision-making under uncertainty, where similar challenges arise. The paper invites the research community to contribute additional games and algorithms, positioning Valet as a living resource for advancing the field.