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
Most implemented papers
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.
Predicting Human Card Selection in Magic: The Gathering with Contextual Preference Ranking
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.
DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning
Games are abstractions of the real world, where artificial agents learn to compete and cooperate with other agents.
Towards Computationally Efficient Responsibility Attribution in Decentralized Partially Observable MDPs
Responsibility attribution is a key concept of accountable multi-agent decision making.
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).
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.
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.
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.