Card Games

14 papers with code • 0 benchmarks • 0 datasets

Card games involve playing cards: the task is to train an agent to play the game with specified rules and beat other players.

Libraries

Use these libraries to find Card Games models and implementations
3 papers
1,725

Most implemented papers

RLCard: A Toolkit for Reinforcement Learning in Card Games

datamllab/rlcard 10 Oct 2019

The goal of RLCard is to bridge reinforcement learning and imperfect information games, and push forward the research of reinforcement learning in domains with multiple agents, large state and action space, and sparse reward.

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

deepmind/open_spiel 3 Mar 2016

When applied to Leduc poker, Neural Fictitious Self-Play (NFSP) approached a Nash equilibrium, whereas common reinforcement learning methods diverged.

Latent Predictor Networks for Code Generation

deepmind/card2code ACL 2016

Many language generation tasks require the production of text conditioned on both structured and unstructured inputs.

Application of Self-Play Reinforcement Learning to a Four-Player Game of Imperfect Information

henrycharlesworth/big2_PPOalgorithm 30 Aug 2018

We introduce a new virtual environment for simulating a card game known as "Big 2".

Survey of Artificial Intelligence for Card Games and Its Application to the Swiss Game Jass

tobiasemrich/SchafkopfRL 11 Jun 2019

Jass is a very popular card game in Switzerland and is closely connected with Swiss culture.

DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning

kwai/DouZero 11 Jun 2021

Games are abstractions of the real world, where artificial agents learn to compete and cooperate with other agents.

Poker-CNN: A Pattern Learning Strategy for Making Draws and Bets in Poker Games

TopologicLogic/CFRM-ES-CFU-XGBoost 22 Sep 2015

The contributions of this paper include: (1) a novel representation for poker games, extendable to different poker variations, (2) a CNN based learning model that can effectively learn the patterns in three different games, and (3) a self-trained system that significantly beats the heuristic-based program on which it is trained, and our system is competitive against human expert players.

Q-DeckRec: A Fast Deck Recommendation System for Collectible Card Games

czxttkl/X-AI 26 Jun 2018

Deck building is a crucial component in playing Collectible Card Games (CCGs).

Approximating Poker Probabilities with Deep Learning

brandinho/Poker-Probability-Approximation 22 Aug 2018

Many poker systems, whether created with heuristics or machine learning, rely on the probability of winning as a key input.

Combinational Q-Learning for Dou Di Zhu

qq456cvb/doudizhu-C 24 Jan 2019

Deep reinforcement learning (DRL) has gained a lot of attention in recent years, and has been proven to be able to play Atari games and Go at or above human levels.