ReAgent is an open source end-to-end platform for applied
reinforcement learning (RL) developed and used at Facebook. ReAgent is
built in Python and uses PyTorch for modeling and training and
TorchScript for model serving. The platform contains workflows to train
popular deep RL algorithms and includes data preprocessing, feature
transformation, distributed training, counterfactual policy evaluation,
and optimized serving. For more detailed information about ReAgent see
the white paper here.
The platform was once named "Horizon" but we have adopted the name
"ReAgent" recently to emphasize its broader scope in decision making and
reasoning.
ReAgent can be installed via. Docker or manually. Detailed instructions on how to install ReAgent can be found here.
Usage
Detailed instructions on how to use ReAgent Models can be found here.
The ReAgent Serving Platform (RASP) tutorial is available here.
License
ReAgent is released under a BSD license. Find out more about it here.
Citing
@article{gauci2018horizon,
title={Horizon: Facebook's Open Source Applied Reinforcement Learning
Platform},
author={Gauci, Jason and Conti, Edoardo and Liang, Yitao and Virochsiri,
Kittipat and Chen, Zhengxing and He, Yuchen and Kaden, Zachary and
Narayanan, Vivek and Ye, Xiaohui},
journal={arXiv preprint arXiv:1811.00260},
year={2018}
}