This is the GUI support for the [RLCard](https://github.com/datamllab/rlcard) project. The project provides evaluation and visualization tools to help understand the performance of the agents. Currently, we only support Leduc Hold'em and Dou Dizhu. The frontend is developed with [React](https://reactjs.org/). The backend is based on [Django](https://www.djangoproject.com/). Have fun!
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 = {Proceedings of the Twenty-Ninth International Joint Conference on
Artificial Intelligence, {IJCAI-20}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
To set up the frontend, you should make sure you have [Node.js](https://nodejs.org/) and NPM installed. Normally you just need to manually install Node.js, and the NPM package would be automatically installed together with Node.js for you. Please refer to its official website for installation of Node.js.
The frontend will be started in port 3000 in localhost by default. You can view it at [http://127.0.0.1:3000/](http://127.0.0.1:3000/). The backend will run by default in [http://127.0.0.1:8000/](http://127.0.0.1:8000/).
If you have any questions or feedback, feel free to drop an email to [Songyi Huang](mailto:songyih@sfu.ca) for the frontend or [Daochen Zha](http://dczha.com/) for backend.
We would like to thank JJ World Network Technology Co., LTD for the generous support, [Chieh-An Tsai](https://anntsai.myportfolio.com/) for user interface design, and [Lei Pan](mailto:lpa25@sfu.ca) for the help in visualizations.