playing-games

Game of Poker

3260 papers • 126 benchmarks • 313 datasets

This task has no description! Would you like to contribute one?

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in game-of-poker-1

Trend

Dataset

Best Model

Actions

No benchmarks available.

Libraries

i

Use these libraries to find game-of-poker-1 models and implementations

Datasets

No datasets available.

Subtasks

No subtasks available.

Most implemented papers

RLCard: A Toolkit for Reinforcement Learning in Card Games

D. Zha, Xia Hu, Kwei-Herng Lai, Yuanpu Cao, Songyi Huang, Ruzhe Wei•Wed Oct 09 2019

An overview of the key components in RLCard is provided, a discussion of the design principles, a brief introduction of the interfaces, and comprehensive evaluations of the environments are provided.

67

Content

0

Paper Graph

Deep Reinforcement Learning from Self-Play in Imperfect-Information Games

David Silver, Johannes Heinrich•Wed Mar 02 2016

This paper introduces the first scalable end-to-end approach to learning approximate Nash equilibria without prior domain knowledge, and combines fictitious self-play with deep reinforcement learning.

437 0

Paper Graph

DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker

Michael Bowling, Nolan Bard, Neil Burch, Michael Bradley Johanson, Matej Moravcík, Martin Schmid, V. Lisý, Dustin Morrill, Trevor Davis, K. Waugh•Sat Dec 31 2016

DeepStack is introduced, a new algorithm for imperfect information settings such as poker that combines recursive reasoning to handle information asymmetry, decomposition to focus computation on the relevant decision, and a form of intuition about arbitrary poker situations that is automatically learned from self-play games using deep learning

223 0

Paper Graph

Approximating Poker Probabilities with Deep Learning

B. Silva•Tue Aug 21 2018

The learned model proves to be a lightweight alternative to Monte Carlo simulation, which ultimately allows us to use the probabilities as inputs during self-play efficiently.

2 0

Paper Graph

DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning

Ji Liu, D. Zha, Xia Hu, Jingru Xie, Wenye Ma, Sheng Zhang, Xiangru Lian•Thu Jun 10 2021

Through building DouZero, it is shown that classic Monte-Carlo methods can be made to deliver strong results in a hard domain with a complex action space, and the code and an online demo are released with the hope that this insight could motivate future work.

148 0

Paper Graph

Adding a benchmark result helps the community track progress.