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* wip * works with cartpole * lint * fix pg * comment * action dist rename * preprocessor * fix test * typo * fix the action[0] nonsense * revert * satisfy the lint * wip * works with cartpole * lint * fix pg * comment * action dist rename * preprocessor * fix test * typo * fix the action[0] nonsense * revert * satisfy the lint * Minor indentation changes. * fix merge * add humanoid * initial dqn refactor * remove tfutil * fix calls * fix tf errors 1 * closer * runs now * lint * tensorboard graph * fix linting * more 4 space * fix * fix linT * more lint * oops * es parity * remove example.py * fix training bug * add cartpole demo * try fixing cartpole * allow model options, configure cartpole * debug * simplify * no dueling * avoid out of file handles * Test dqn in jenkins. * Minor formatting. * fix issue * fix another * Fix problem in which we log to a directory that hasn't been created.
Implement object table notification subscriptions and switch to using Redis modules for object table. (#134)
Ray
===
.. image:: https://travis-ci.org/ray-project/ray.svg?branch=master
:target: https://travis-ci.org/ray-project/ray
.. image:: https://readthedocs.org/projects/ray/badge/?version=latest
:target: http://ray.readthedocs.io/en/latest/?badge=latest
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Ray is a flexible, high-performance distributed execution framework.
View the `documentation`_.
.. _`documentation`: http://ray.readthedocs.io/en/latest/index.html
Description
An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.
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