RLCard: A Toolkit for Reinforcement Learning in Card Games

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

AAAI-20 Workshop on Reinforcement Learning in Games

References in corpus (10)

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Cited by in corpus (8)

• PettingZoo: Gym for Multi-Agent Reinforcement Learning
• Simplifying Deep Reinforcement Learning via Self-Supervision
• Dual Policy Distillation
• Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning
• Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environments
• Policy-GNN: Aggregation Optimization for Graph Neural Networks
• Sample Efficient Reinforcement Learning via Model-Ensemble Exploration and Exploitation
• Playing 2048 With Reinforcement Learning