RLCard Environments

该数据集是由RLCard提供的开源工具包，其中包含了多种纸牌游戏环境，如二十一点、勒杜克德州扑克、德州扑克、UNO、斗地主和麻将等，专为强化学习研究而设计。该工具包提供了易于使用的接口，旨在评估在具有不完美信息特征的纸牌游戏中，最先进的强化学习算法。它包含了多个不同复杂度的环境，任务集中在纸牌游戏中的强化学习研究。

This dataset is an open-source toolkit developed by RLCard, which encompasses a wide range of card game environments including Blackjack, Leduc Hold'em, Texas Hold'em, UNO, Dou Dizhu, Mahjong and other classic card games, and is specifically tailored for reinforcement learning research. The toolkit provides easy-to-use interfaces, aiming to evaluate state-of-the-art reinforcement learning algorithms in card games with imperfect information characteristics. It includes multiple environments with varying levels of complexity, and its tasks focus on reinforcement learning research in card game scenarios.