playing-games-10

Card Games

3260 papers • 126 benchmarks • 313 datasets

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

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Benchmarks

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Use these libraries to find card-games-10 models and implementations

Datasets

Cards Against Humanity

Subtasks

Game of Poker Game of Hanabi Solitaire Klondike

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.

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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.

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Latent Predictor Networks for Code Generation

A. Senior, Karl Moritz Hermann, Phil Blunsom, Tomás Kociský, Edward Grefenstette, Wang Ling, Fumin Wang•Mon Mar 21 2016

A novel neural network architecture is presented which generates an output sequence conditioned on an arbitrary number of input functions and allows both the choice of conditioning context and the granularity of generation, for example characters or tokens, to be marginalised, thus permitting scalable and effective training.

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

Henry Charlesworth•Wed Aug 29 2018

A new virtual environment for simulating a card game known as "Big 2" is introduced and the recently proposed "Proximal Policy Optimization" algorithm is used to train a deep neural network to play the game, purely learning via self-play, and finds that it outperforms amateur human players after only a relatively short amount of training time.

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

Joel Niklaus, M. Liwicki, Michele Alberti, Vinaychandran Pondenkandath, R. Ingold•Fri May 31 2019

This work provides an overview of the current state-of-the-art of Artificial Intelligence methods for card games in general and their application to the use-case of the Swiss card game Jass.

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

Zhengxing Chen, Chris Amato, Truong-Huy D. Nguyen, Seth Cooper, Yizhou Sun, M. S. El-Nasr•Mon Jun 25 2018

A new deck recommendation system, named Q-DeckRec, is proposed, which learns a deck search policy during a training phase and uses it to solve deck building problem instances and requires less computational resources to build winning-effective decks after aTraining phase compared to several baseline methods.

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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.

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Combinational Q-Learning for Dou Di Zhu

Yang You, Liangwei Li, Cewu Lu, Weiming Wang, B. Guo•Wed Jan 23 2019

This paper proposes a novel method to handle combinatorial actions, which it is called combinational Q-learning (CQL), and employs a two-stage network to reduce action space and also leverage order-invariant max-pooling operations to extract relationships between primitive actions.

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The Winnability of Klondike Solitaire and Many Other Patience Games

Charlie Blake, Ian P. Gent•Thu Jun 27 2019

Here it is shown that a single general purpose Artificial Intelligence program, called "Solvitaire", can be used to determine the winnability percentage of 45 different single-player card games with a 95% confidence interval of +/- 0.1% or better.

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Drafting in Collectible Card Games via Reinforcement Learning

A. Tavares, R. Vieira, L. Chaimowicz•Sat Oct 31 2020

Collectible card games are played by tens of millions of players worldwide. Their intricate rules and diverse cards make them much harder than traditional card games. To win, players must be proficient in two interdependent tasks: deck building and battling. In this paper, we present a deep reinforcement learning approach for deck building in arena mode - an understudied game mode present in many collectible card games. In arena, the players build decks immediately before battling by drafting one card at a time from randomly presented candidates. We investigate three variants of the approach and perform experiments on Legends of Code and Magic, a collectible card game designed for AI research. Results show that our learned draft strategies outperform those of the best agents of the game. Moreover, a participant of the Strategy Card Game AI competition improves from tenth to fourth place when coupled with our best draft agent.

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