RLCard: A Toolkit for Reinforcement Learning in Card Games

2019/10/10 by Daochen Zha, Kwei-Herng Lai, Zha, Daochen +11 · 8 citations
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Artificial Intelligence (cs.AI) #Artificial Intelligence in Games #FOS: Computer and information sciences #Reinforcement Learning in Robotics

paper · pdf · doi:10.48550/arxiv.1910.04376

openalex publication_date 2019/10/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

RLCard is an open-source toolkit for reinforcement learning research in card games. It supports various card environments with easy-to-use interfaces, including Blackjack, Leduc Hold'em, Texas Hold'em, UNO, Dou Dizhu and Mahjong. 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. In this paper, we provide an overview of the key components in RLCard, a discussion of the design principles, a brief introduction of the interfaces, and comprehensive evaluations of the environments. The codes and documents are available at https://github.com/datamllab/rlcard

Citations

• Continuous control with deep reinforcement learning
• Neural Architecture Search with Reinforcement Learning
• StarCraft II: A New Challenge for Reinforcement Learning
• Unity: A General Platform for Intelligent Agents
• Neural MMO: A Massively Multiagent Game Environment for Training and Evaluating Intelligent Agents
• OpenSpiel: A Framework for Reinforcement Learning in Games

Cited by

• Observer, Not Player: Simulating Theory of Mind in LLMs through Game Observation
• PARL: Prompt-based Agents for Reinforcement Learning
• AI Agents for the Dhumbal Card Game: A Comparative Study
• LLMs as Agentic Cooperative Players in Multiplayer UNO
• Sample Efficient Reinforcement Learning via Model-Ensemble Exploration and Exploitation
• Analysis of Bluffing by DQN and CFR in Leduc Hold'em Poker
• Can Large Language Models Master Complex Card Games?
• Playing 2048 With Reinforcement Learning

Related

• Learning from Learners: Adapting Reinforcement Learning Agents to be Competitive in a Card Game
• Two-Step Reinforcement Learning for Multistage Strategy Card Game
• RL-CFR: Improving Action Abstraction for Imperfect Information Extensive-Form Games with Reinforcement Learning
• Application of Self-Play Reinforcement Learning to a Four-Player Game of Imperfect Information
• AlphaDou: High-Performance End-to-End Doudizhu AI Integrating Bidding