# RLCard: A Toolkit for Reinforcement Learning in Card Games

arXiv: https://arxiv.org/abs/1910.04376 · HTML: https://mlsanity.com/paper/1910.04376/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2019.

## Built on by (most cited first, 30 of 35)

- [Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization](https://mlsanity.com/paper/2402.17574/) (2024)
- [Suspicion-Agent: Playing Imperfect Information Games with Theory of Mind Aware GPT-4](https://mlsanity.com/paper/2309.17277/) (2023)
- [DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning](https://mlsanity.com/paper/2106.06135/) (2021)
- [A Survey on the Optimization of Large Language Model-based Agents](https://mlsanity.com/paper/2503.12434/) (2025)
- [Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environments](https://mlsanity.com/paper/2101.08152/) (2021)
- [A Survey on Self-play Methods in Reinforcement Learning](https://mlsanity.com/paper/2408.01072/) (2024)
- [Evaluating and Enhancing LLMs Agent based on Theory of Mind in Guandan: A Multi-Player Cooperative Game under Imperfect Information](https://mlsanity.com/paper/2408.02559/) (2024)
- [Sample Efficient Reinforcement Learning via Model-Ensemble Exploration and Exploitation](https://mlsanity.com/paper/2107.01825/) (2021)
- [PerfectDou: Dominating DouDizhu with Perfect Information Distillation](https://mlsanity.com/paper/2203.16406/) (2022)
- [EgoSocialArena: Benchmarking the Social Intelligence of Large Language Models from a First-person Perspective](https://mlsanity.com/paper/2410.06195/) (2024)
- [Simplifying Deep Reinforcement Learning via Self-Supervision](https://mlsanity.com/paper/2106.05526/) (2021)
- [UNO Arena for Evaluating Sequential Decision-Making Capability of Large Language Models](https://mlsanity.com/paper/2406.16382/) (2024)
- [Automatic Meta-Path Discovery for Effective Graph-Based Recommendation](https://mlsanity.com/paper/2112.12845/) (2021)
- [Can Large Language Models Master Complex Card Games?](https://mlsanity.com/paper/2509.01328/) (2025)
- [Cardiverse: Harnessing LLMs for Novel Card Game Prototyping](https://mlsanity.com/paper/2502.07128/) (2025)
- [PyTAG: Tabletop Games for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2405.18123/) (2024)
- [From Player to Master: Enhancing Test-Time Learning of LLM Agents via Reinforcement Learning over Memory](https://mlsanity.com/paper/2606.08656/) (2026)
- [Towards Personalized Preprocessing Pipeline Search](https://mlsanity.com/paper/2302.14329/) (2023)
- [PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games](https://mlsanity.com/paper/2307.09905/) (2023)
- [Empowering LLMs in Decision Games through Algorithmic Data Synthesis](https://mlsanity.com/paper/2503.13980/) (2025)
- [PolicyEvol-Agent: Evolving Policy via Environment Perception and Self-Awareness with Theory of Mind](https://mlsanity.com/paper/2504.15313/) (2025)
- [Analysis of Bluffing by DQN and CFR in Leduc Hold'em Poker](https://mlsanity.com/paper/2509.04125/) (2025)
- [Playing 2048 With Reinforcement Learning](https://mlsanity.com/paper/2110.10374/) (2021)
- [Hearts Gym: Learning Reinforcement Learning as a Team Event](https://mlsanity.com/paper/2209.05466/) (2022)
- [Two-Step Reinforcement Learning for Multistage Strategy Card Game](https://mlsanity.com/paper/2311.17305/) (2023)
- [AlphaDou: High-Performance End-to-End Doudizhu AI Integrating Bidding](https://mlsanity.com/paper/2407.10279/) (2024)
- [Improve Value Estimation of Q Function and Reshape Reward with Monte Carlo Tree Search](https://mlsanity.com/paper/2410.11642/) (2024)
- [Imitation Learning of Correlated Policies in Stackelberg Games](https://mlsanity.com/paper/2503.08883/) (2025)
- [LLMs as Agentic Cooperative Players in Multiplayer UNO](https://mlsanity.com/paper/2509.09867/) (2025)
- [AI Agents for the Dhumbal Card Game: A Comparative Study](https://mlsanity.com/paper/2510.11736/) (2025)

## Abstract (arXiv)

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