构建了一个用于双人零和不完美信息博弈的开源训练环境。
FootsiesGym: A Fighting Game Benchmark for Two-Player Zero-Sum Imperfect-Information Games
- 基于简化2D格斗游戏设计，突出非传递性策略循环
- 提供向量化模拟器，支持标准硬件高效训练
- 适合研究强化学习在复杂博弈中的应用
我们提出了FootsiesGym，一个开源环境，用于在非平凡的双人、零和、不完美信息博弈中进行学习。该环境基于HiFight的极简2D格斗游戏Footsies，保留了格斗游戏中中立对局的循环非传递性战略互动，同时保持足够简单以实现高效分析。我们提供了向量化模拟器，可在标准硬件上实现高吞吐量训练，使环境易于使用且可复现。我们描述了环境的设计，基准测试了几种强化学习算法，并讨论了其开启的开放研究方向。代码已公开于https://github.com/como-research/FootsiesGym。
原文摘要 · Abstract (English)
We present FootsiesGym, an open-source environment for learning in a non-trivial two-player, zero-sum, imperfect-information game. Built on HiFight's minimalist 2D fighting game Footsies, it isolates the cyclic, non-transitive strategic interactions of fighting game neutral play while remaining simple enough for efficient analysis. We provide a vectorized simulator that enables high-throughput training on standard hardware, making the environment accessible and reproducible. We describe the design of the environment, benchmark several reinforcement learning algorithms, and discuss open research directions it enables. The code is available at https://github.com/como-research/FootsiesGym.
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