Card games involve playing cards: the task is to train an agent to play the game with specified rules and beat other players.
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.
We introduce a new virtual environment for simulating a card game known as "Big 2".
When applied to Leduc poker, Neural Fictitious Self-Play (NFSP) approached a Nash equilibrium, whereas common reinforcement learning methods diverged.
Many poker systems, whether created with heuristics or machine learning, rely on the probability of winning as a key input.