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* make information available for GAE * buggy version of GAE estimator * fix * add more logging and reweight losses * fix logging * fix loss * adapt advantage calculation * update gae * standardize returns * don't normalize td lambda ret * fix * don't standardize advantages * do standardization earlier * different standardization * initializer * drop into the debugger * fix tensorflow broadcasting bug * vf clipping * don't standardize tdlambdaret * different standardization * use huber loss for value function * refactor -- first half * it runs * fix * update * documentation * linting and tests * fix linting * naming * fix * linting * fix * remove prefix madness * fixes * fix * add value function example * fix linting * remove newline
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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