Card Games
18 papers with code • 0 benchmarks • 1 datasets
Card games involve playing cards: the task is to train an agent to play the game with specified rules and beat other players.
Benchmarks
These leaderboards are used to track progress in Card Games
Cards Against Humanity
Latest papers
GTBench: Uncovering the Strategic Reasoning Limitations of LLMs via Game-Theoretic Evaluations
As Large Language Models (LLMs) are integrated into critical real-world applications, their strategic and logical reasoning abilities are increasingly crucial.
DanZero+: Dominating the GuanDan Game through Reinforcement Learning
The utilization of artificial intelligence (AI) in card games has been a well-explored subject within AI research for an extensive period.
Suspicion-Agent: Playing Imperfect Information Games with Theory of Mind Aware GPT-4
Unlike perfect information games, where all elements are known to every player, imperfect information games emulate the real-world complexities of decision-making under uncertain or incomplete information.
PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games
To bridge this gap, we introduce PyTAG, a Python API for interacting with the Tabletop Games framework (TAG).
Towards Computationally Efficient Responsibility Attribution in Decentralized Partially Observable MDPs
Responsibility attribution is a key concept of accountable multi-agent decision making.
DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning
kwai/DouZero
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Games are abstractions of the real world, where artificial agents learn to compete and cooperate with other agents.
Predicting Human Card Selection in Magic: The Gathering with Contextual Preference Ranking
Tibert97/Predicting-Human-Card-Selection-in-Magic-The-Gathering-with-Contextual-Preference-Ranking
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Drafting, i. e., the selection of a subset of items from a larger candidate set, is a key element of many games and related problems.
Analysis of Evolutionary Program Synthesis for Card Games
We report the results by providing a comprehensive analysis of the set of rules and their implications.
Drafting in Collectible Card Games via Reinforcement Learning
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.
RLCard: A Toolkit for Reinforcement Learning in Card Games
datamllab/rlcard
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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.
