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RLCard: A Toolkit for Reinforcement Learning in Card Games

arXiv 1910.04376 ↗First posted 10 Oct 2019cs.AIcited by 35

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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

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

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Citing papers per year: 2021: 6; 2022: 2; 2023: 4; 2024: 8; 2025: 11; 2026: 4.

35 papers.

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  1. 2024Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimizationcited by 94
  2. 2023Suspicion-Agent: Playing Imperfect Information Games with Theory of Mind Aware GPT-4cited by 68
  3. 2021DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learningcited by 66
  4. 2025A Survey on the Optimization of Large Language Model-based Agentscited by 46
  5. 2021Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environmentscited by 28
  6. 2024A Survey on Self-play Methods in Reinforcement Learningcited by 26
  7. 2024Evaluating and Enhancing LLMs Agent based on Theory of Mind in Guandan: A Multi-Player Cooperative Game under Imperfect Informationcited by 20
  8. 2021Sample Efficient Reinforcement Learning via Model-Ensemble Exploration and Exploitationcited by 18
  9. 2022PerfectDou: Dominating DouDizhu with Perfect Information Distillationcited by 15
  10. 2024EgoSocialArena: Benchmarking the Social Intelligence of Large Language Models from a First-person Perspectivecited by 10
  11. 2021Simplifying Deep Reinforcement Learning via Self-Supervisioncited by 9
  12. 2024UNO Arena for Evaluating Sequential Decision-Making Capability of Large Language Modelscited by 6
23 more
  1. 2021Automatic Meta-Path Discovery for Effective Graph-Based Recommendationcited by 5
  2. 2025Can Large Language Models Master Complex Card Games?cited by 4
  3. 2025Cardiverse: Harnessing LLMs for Novel Card Game Prototypingcited by 3
  4. 2024PyTAG: Tabletop Games for Multi-Agent Reinforcement Learningcited by 2
  5. 2026From Player to Master: Enhancing Test-Time Learning of LLM Agents via Reinforcement Learning over Memorycited by 2
  6. 2023Towards Personalized Preprocessing Pipeline Searchcited by 1
  7. 2023PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Gamescited by 1
  8. 2025Empowering LLMs in Decision Games through Algorithmic Data Synthesiscited by 1
  9. 2025PolicyEvol-Agent: Evolving Policy via Environment Perception and Self-Awareness with Theory of Mindcited by 1
  10. 2025Analysis of Bluffing by DQN and CFR in Leduc Hold'em Pokercited by 1
  11. 2021Playing 2048 With Reinforcement Learningcited by 0
  12. 2022Hearts Gym: Learning Reinforcement Learning as a Team Eventcited by 0
  13. 2023Two-Step Reinforcement Learning for Multistage Strategy Card Gamecited by 0
  14. 2024AlphaDou: High-Performance End-to-End Doudizhu AI Integrating Biddingcited by 0
  15. 2024Improve Value Estimation of Q Function and Reshape Reward with Monte Carlo Tree Searchcited by 0
  16. 2025Imitation Learning of Correlated Policies in Stackelberg Gamescited by 0
  17. 2025LLMs as Agentic Cooperative Players in Multiplayer UNOcited by 0
  18. 2025AI Agents for the Dhumbal Card Game: A Comparative Studycited by 0
  19. 2025PARL: Prompt-based Agents for Reinforcement Learningcited by 0
  20. 2025Observer, Not Player: Simulating Theory of Mind in LLMs through Game Observationcited by 0
  21. 2026Valet: A Standardized Testbed of Traditional Imperfect-Information Card Gamescited by 0
  22. 2026GAE Falls Short in Imperfect-Information Self-Play Reinforcement Learningcited by 0
  23. 2026BluffJAX: Adversarial Imperfect Information Games in JAXcited by 0