构建21种纸牌游戏测试平台，统一评估AI博弈算法性能。
Valet: A Standardized Testbed of Traditional Imperfect-Information Card Games
- 用可扩展规则语言RECYCLE统一描述21种纸牌游戏。
- 通过随机模拟测得各游戏分支因子与时长，提供基准分数分布。
- 适合对比不同算法在多样游戏环境下的鲁棒性表现。
针对不完美信息博弈中算法评估依赖单一游戏的问题，我们提出Valet——一个包含21种传统不完美信息纸牌游戏的综合性测试平台。这些游戏涵盖多种类型、文化背景、玩家数量、牌组结构、玩法机制和信息隐藏/揭示方式。为实现跨系统标准化，每款游戏规则均用RECYCLE（一种卡牌游戏描述语言）编码。通过随机模拟，我们量化了各游戏的分支因子与持续时间，并报告了蒙特卡洛树搜索（MCTS）玩家对随机对手的基线得分分布，验证了Valet作为基准测试套件的有效性。
原文摘要 · Abstract (English)
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.
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