LightZero is a lightweight, efficient, and easy-to-understand open-source algorithm toolkit that combines Monte Carlo Tree Search (MCTS) and Deep Reinforcement Learning (RL).
The LightZero documentation can be found here. It contains tutorials and the API reference.
For those interested in customizing environments and algorithms, we provide relevant guides:
- Customize Environments
- Customize Algorithms
- How to Set Configuration Files?
- Logging and Monitoring System
Should you have any questions, feel free to contact us for support.
# step 1: install
cd LightZero
pip install -r requirements-doc.txt
# step 2: compile docs
cd LightZero/docs/source
make live
# step 3: open http://127.0.0.1:8000 in your browser, and explore it!@article{niu2024lightzero,
title={LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios},
author={Niu, Yazhe and Pu, Yuan and Yang, Zhenjie and Li, Xueyan and Zhou, Tong and Ren, Jiyuan and Hu, Shuai and Li, Hongsheng and Liu, Yu},
journal={Advances in Neural Information Processing Systems},
volume={36},
year={2024}
}
@article{pu2024unizero,
title={UniZero: Generalized and Efficient Planning with Scalable Latent World Models},
author={Pu, Yuan and Niu, Yazhe and Ren, Jiyuan and Yang, Zhenjie and Li, Hongsheng and Liu, Yu},
journal={arXiv preprint arXiv:2406.10667},
year={2024}
}
@article{xuan2024rezero,
title={ReZero: Boosting MCTS-based Algorithms by Backward-view and Entire-buffer Reanalyze},
author={Xuan, Chunyu and Niu, Yazhe and Pu, Yuan and Hu, Shuai and Liu, Yu and Yang, Jing},
journal={arXiv preprint arXiv:2404.16364},
year={2024}
}If you have any questions about documentation, please add a new issue or contact opendilab@pjlab.org.cn
LightZero-Docs released under the Apache 2.0 license