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BluffJAX: Adversarial Imperfect Information Games in JAX — arXiv:2610.07686

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.07686
License (as archived): CC0-1.0
Archived: 2026-10-07T11:19:00.608Z
SHA-256 of the archived text: c63839bd5a742d82f4fa3bcaa07b2ed63ee6a6845872f872a7f4c9fbb496a236
Stable URL of this page: https://metraipolis.com/documento/dc_dda5dff04eea

How to cite

BluffJAX: Adversarial Imperfect Information Games in JAX — arXiv:2610.07686 — arXiv (en), https://arxiv.org/abs/2610.07686 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_dda5dff04eea (Metraipolis archive dc_dda5dff04eea, sha256 c63839bd5a742d82f4fa3bcaa07b2ed63ee6a6845872f872a7f4c9fbb496a236)

BibTeX

@misc{dcdda5dff04eea,
  title = {BluffJAX: Adversarial Imperfect Information Games in JAX — arXiv:2610.07686},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.07686},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_dda5dff04eea; sha256 c63839bd5a742d82f4fa3bcaa07b2ed63ee6a6845872f872a7f4c9fbb496a236},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.07686. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.07686 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T11:15:24.541Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).