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Pull Request resolved: #451

This diff uses the logger in pytorch lightning to recreate the graphs that were traditionally reported through the dqn_reporter.  These graphs are then fed back into fblearner, eliiminating the need to report them manually.

Reviewed By: czxttkl

Differential Revision: D27694627

fbshipit-source-id: 9f5437ff38d61f316c09b03d6088ce36f4d6199c
db61132

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Applied Reinforcement Learning @ Facebook

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Overview

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.

Algorithms Supported

Installation

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 3-Clause 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} }

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A platform for Reasoning systems (Reinforcement Learning, Contextual Bandits, etc.)

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