paper-with-me

Papers

PokerKit: A Comprehensive Python Library for Fine-Grained Multi-Variant Poker Game Simulations

2023-08-08 · Juho Kim

PokerKit is an open-source Python library designed to overcome the restrictions of existing poker game simulation and hand evaluation tools, which typically support only a handful of poker variants and lack flexibility in game state control. In contrast, PokerKit significantly expands this scope by supporting an extensive array of poker variants and it provides a flexible architecture for users to define their custom games. This paper details the design and implementation of PokerKit, including its intuitive programmatic API, multi-variant game support, and a unified hand evaluation suite across different hand types. The flexibility of PokerKit allows for applications in diverse areas, such as poker AI development, tool creation, and online poker casino implementation. PokerKit's reliability has been established through static type checking, extensive doctests, and unit tests, achieving 99% code coverage. The introduction of PokerKit represents a significant contribution to the field of computer poker, fostering future research and advanced AI development for a wide variety of poker games. The source code is available at https://github.com/uoftcprg/pokerkit

📄 PDF Abstract BibTeX arXiv:2308.07327

Code (1)

uoftcprg/pokerkit 공식 구현

Methods 이 논문이 사용한 방법론

Poker Software Developer Poker is a game of skill that uses probability concepts based on true randomization and, to some extent, opponent behavioral psychology. In the current AI era, NLP and other data…
Library 설명 없음

Similar Papers 제목 키워드 기반

torchmil: A PyTorch-based library for deep Multiple Instance Learning

2025-09-09 · Francisco M. Castro-Macías, Francisco J. Sáez-Maldonado, Pablo Morales-Álvarez, Rafael Molina arxiv

Multiple Instance Learning (MIL) is a powerful framework for weakly supervised learning, particularly useful when fine-grained annotations are unavailable. Despite growing interest in deep MIL methods, the field lacks st…

Multiple Instance Learning

nerblackbox: A High-level Library for Named Entity Recognition in Python

2023-12-07 · Felix Stollenwerk

We present nerblackbox, a python library to facilitate the use of state-of-the-art transformer-based models for named entity recognition. It provides simple-to-use yet powerful methods to access data and models from a wi…

named-entity-recognitionNamed Entity Recognition

Causal-learn: Causal Discovery in Python

2023-07-31 · Yujia Zheng, Biwei Huang, Wei Chen, Joseph Ramsey 외

Causal discovery aims at revealing causal relations from observational data, which is a fundamental task in science and engineering. We describe $\textit{causal-learn}$, an open-source Python library for causal discovery…

Causal Discovery

DRO: A Python Library for Distributionally Robust Optimization in Machine Learning

2025-05-29 · Jiashuo Liu, Tianyu Wang, Henry Lam, Hongseok Namkoong 외

We introduce dro, an open-source Python library for distributionally robust optimization (DRO) for regression and classification problems. The library implements 14 DRO formulations and 9 backbone models, enabling 79 dis…

abess: A Fast Best Subset Selection Library in Python and R

2021-10-19 · Jin Zhu, Xueqin Wang, Liyuan Hu, Junhao Huang 외

We introduce a new library named abess that implements a unified framework of best-subset selection for solving diverse machine learning problems, e.g., linear regression, classification, and principal component analysis…

Model SelectionSparse LearningVariable Selection