RLCard: A Toolkit for Reinforcement Learning in Card Games
Published in
arXiv.org(2019)External Links:
TL
TL;DR
An overview of the key components in RLCard is provided, a discussion of the design principles, a brief introduction of the interfaces, and comprehensive evaluations of the environments are provided.
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 this https URL
References25 items
1
OpenSpiel: A Framework for Reinforcement Learning in Games
2
DeltaDou: Expert-level Doudizhu AI through Self-play
3
Neural MMO: A Massively Multiagent Game Environment for Training and Evaluating Intelligent Agents
4
Deep Counterfactual Regret Minimization
5