RLCard: A Platform for Reinforcement Learning in Card Games
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.
Cite
Text
Zha et al. "RLCard: A Platform for Reinforcement Learning in Card Games." International Joint Conference on Artificial Intelligence, 2020. doi:10.24963/IJCAI.2020/764Markdown
[Zha et al. "RLCard: A Platform for Reinforcement Learning in Card Games." International Joint Conference on Artificial Intelligence, 2020.](https://mlanthology.org/ijcai/2020/zha2020ijcai-rlcard/) doi:10.24963/IJCAI.2020/764BibTeX
@inproceedings{zha2020ijcai-rlcard,
title = {{RLCard: A Platform for Reinforcement Learning in Card Games}},
author = {Zha, Daochen and Lai, Kwei-Herng and Huang, Songyi and Cao, Yuanpu and Reddy, Keerthana and Vargas, Juan and Nguyen, Alex and Wei, Ruzhe and Guo, Junyu and Hu, Xia},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2020},
pages = {5264-5266},
doi = {10.24963/IJCAI.2020/764},
url = {https://mlanthology.org/ijcai/2020/zha2020ijcai-rlcard/}
}