RLCard: A Platform for Reinforcement Learning in Card Games
Daochen Zha, Kwei-Herng Lai, Songyi Huang, Yuanpu Cao, K. Rajesh Reddy, Juan Carlos Vargas, Alex Hoang Hai Nguyen, Ruzhe Wei, Junyu Guo, Xia Hu
Abstract
We present RLCard, a Python platform for reinforcement learning research and development in card games. RLCard supports various card environments and several baseline algorithms with unified easy-to-use interfaces, aiming at bridging reinforcement learning and imperfect information games. The platform provides flexible configurations of state representation, action encoding, and reward design. RLCard also supports visualizations for algorithm debugging. In this demo, we showcase two representative environments and their visualization results. We conclude this demo with challenges and research opportunities brought by RLCard. A video is available on YouTube.