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Kittipat Virochsiri
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Summary: We no longer use Thrift for configuration Reviewed By: czxttkl Differential Revision: D18242481 fbshipit-source-id: bfdfa1e7baa21be92bb51a7103d0f379dafee2b2
105 lines
3.6 KiB
ReStructuredText
105 lines
3.6 KiB
ReStructuredText
.. role:: raw-html-m2r(raw)
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:format: html
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.. image:: ../logo/reagent_banner.png
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:alt: Banner
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ReAgent: Applied Reinforcement Learning Platform
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. image:: https://circleci.com/gh/facebookresearch/ReAgent/tree/master.svg?style=svg
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:target: https://circleci.com/gh/facebookresearch/ReAgent/tree/master
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--------------------------------------------------------------------------------------------------------------------------------------------------------------------
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Overview
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~~~~~~~~
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ReAgent is an open source end-to-end platform for applied reinforcement learning (RL) developed and used at Facebook.
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ReAgent is built in Python and uses PyTorch for modeling and training and TorchScript for model serving. The platform contains
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workflows to train popular deep RL algorithms and includes data preprocessing, feature transformation, distributed training,
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counterfactual policy evaluation, and optimized serving. For more detailed information about ReAgent see the white
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paper here: `Platform <https://research.fb.com/publications/horizon-facebooks-open-source-applied-reinforcement-learning-platform/>`_.
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The source code is available here: `Source code <https://github.com/facebookresearch/ReAgent>`_.
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The platform was once named "Horizon" but we have adopted the name "ReAgent" recently to emphasize its broader scope in decision making and reasoning.
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Algorithms Supported
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~~~~~~~~~~~~~~~~~~~~
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* Discrete-Action `DQN <https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf>`_
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* Parametric-Action DQN
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* `Double DQN <https://arxiv.org/abs/1509.06461>`_\ , `Dueling DQN <https://arxiv.org/abs/1511.06581>`_\ , `Dueling Double DQN <https://arxiv.org/abs/1710.02298>`_
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* Distributional RL `C51 <https://arxiv.org/abs/1707.06887>`_\ , `QR-DQN <https://arxiv.org/abs/1710.10044>`_
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* `Twin Delayed DDPG <https://arxiv.org/abs/1802.09477>`_ (TD3)
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* `Soft Actor-Critic <https://arxiv.org/abs/1801.01290>`_ (SAC)
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Installation
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~~~~~~~~~~~~~~~~~~~
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ReAgent can be installed via. Docker or manually. Detailed instructions on how to install ReAgent can be found
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here: :ref:`installation`.
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Usage
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~~~~~~~~~~~~
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The ReAgent Serving Platform (RASP) tutorial covers serving and training models and is available here: :ref:`rasp_tutorial`.
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Detailed instructions on how to use ReAgent can be found here: :ref:`usage`.
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License
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~~~~~~~~~~~~~~
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ReAgent is released under a BSD license. Find out more about it here: :ref:`license`.
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Citing
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~~~~~~
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@article{gauci2018horizon,
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title={Horizon: Facebook's Open Source Applied Reinforcement Learning Platform},
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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},
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journal={arXiv preprint arXiv:1811.00260},
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year={2018}
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}
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Table of Contents
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~~~~~~~~~~~~~~~~~~~~~
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.. toctree::
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:caption: Getting Started
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Installation <installation>
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Tutorial <rasp_tutorial>
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Usage <usage>
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.. toctree::
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:caption: Advanced Topics
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Distributed Training <distributed>
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Continuous Integration <continuous_integration>
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.. toctree::
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:caption: Package Reference
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Evaluation <api/ml.rl.evaluation>
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Models <api/ml.rl.models>
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Prediction <api/ml.rl.prediction>
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Preprocessing <api/ml.rl.preprocessing>
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Readers <api/ml.rl.readers>
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Simulators <api/ml.rl.simulators>
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Training <api/ml.rl.training>
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Workflow <api/ml.rl.workflow>
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All Modules <api/modules>
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.. toctree::
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:caption: Others
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Github <https://github.com/facebookresearch/ReAgent>
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License <license>
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