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.

翻译：不完全信息博弈的AI算法通常通过单个游戏的性能指标进行比较，难以评估算法在不同游戏间的鲁棒性。纸牌游戏因隐藏手牌和随机抽牌机制，天然构成不完全信息的研究领域。为促进不完全信息博弈算法与游戏系统的比较研究，我们提出Valet——一个包含21种传统不完全信息纸牌游戏的多样化综合测试平台。这些游戏涵盖多种类型、文化背景、玩家数量、牌组结构、游戏机制、获胜条件以及信息隐藏与揭示方式。为实现跨系统标准化实现，我们采用纸牌游戏描述语言RECYCLE对每种游戏规则进行编码。通过随机模拟实验，我们量化了各游戏的分支因子与对局时长，并报告蒙特卡洛树搜索玩家对抗随机对手的基准得分分布，以验证Valet作为基准测试套件的适用性。