16 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.
These leaderboards are used to track progress in Card Games
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.
When applied to Leduc poker, Neural Fictitious Self-Play (NFSP) approached a Nash equilibrium, whereas common reinforcement learning methods diverged.
Our ignorance of the winnability percentage of the game in the Windows Solitaire program, more properly called 'Klondike', has been described as "one of the embarrassments of applied mathematics".
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.