arXiv：cs.AI· Aryaman Reddi, Jan Peters, Carlo D'Eramo·· 6 小时前AI 评分36
BluffJAX：基于 JAX 的对抗性不完全信息博弈开源套件
BluffJAX: Adversarial Imperfect Information Games in JAX
AI 导读
BluffJAX 是一个基于 JAX 的开源对抗性不完全信息博弈套件，包含德州扑克、Kuhn Poker、Bluff、Stud Poker 和 Kemps 等游戏，面向 GPU 加速器实现高吞吐并行模拟。其在单 GPU 和多 GPU 设置下吞吐量最高可达每秒数亿样本，并提供了强化学习、树搜索和博弈求解算法的 JAX 基线结果。
正文
Abstract:We introduce BluffJAX: an open-source suite of adversarial imperfect information games in JAX. We provide canonical implementations of games designed for high simulation throughputs and parallelization on GPU accelerators. Our suite consists of well-studied benchmarks such as Texas Hold'Em Poker and Kuhn Poker, as well as games that have not been previously studied in reinforcement learning research, such as Bluff, Stud Poker, and Kemps. We hope that implementing a variety of game mechanics and difficulties will introduce new challenges and foster novel research directions in game-theoretic methods for RL. We benchmark the throughput performance and memory usage of our environments in single and multi-GPU settings, demonstrating scaling of up to hundreds of millions of samples per second, and motivating the usage of BluffJAX over related GPU and CPU-based libraries. We benchmark reinforcement learning, tree search, and game-solving algorithms in JAX in order to provide users with baseline results and facilitate future comparisons.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.07686 [cs.AI] |
| (or arXiv:2610.07686v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.07686 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Aryaman Reddi [view email]
[v1]
Tue, 6 Oct 2026 03:18:32 UTC (433 KB)
来源：arXiv：cs.AI · arxiv.org