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* Code for Supporting Shared Models Running (with vnet modification) - needs to be tested for performance Summaries Small refactoring + generalized to more domains Small fix for jenkins Linting linting Addressing changes Addressing changes Update envs.py Addressing changes convnet Merge - new model final touches final linting Changing iterations back removed extra change changes for fast experimentation changes to enable a2c TEMP FOR DEBUGGING ContinuousActions - Still doesn't work InvertedPendulum trains with 8 workers - k=200 huber loss Maxes for InvertedPendulum-v1 - 16w,200steps temp: working with a2c Back to shared model more fixes small nit LSTM to shared models need to fix last_features tuning pong Best record for hitting 0 - with k=16,n=20 nit a2cremoval remove A2c reference and nits nit removed a2c vestiges removing a2c removing example.py Linting nit * Linting + Removing vestigal code * Final Touches * nits * rerun travis
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
|
Ray is a flexible, high-performance distributed execution framework.
Installation
------------
- Ray can be installed on Linux and Mac with ``pip install ray``.
- To build Ray from source, see the instructions for `Ubuntu`_ and `Mac`_.
.. _`Ubuntu`: http://ray.readthedocs.io/en/latest/install-on-ubuntu.html
.. _`Mac`: http://ray.readthedocs.io/en/latest/install-on-macosx.html
More Information
----------------
- `Documentation`_
- `Blog`_
- `HotOS paper`_
.. _`Documentation`: http://ray.readthedocs.io/en/latest/index.html
.. _`Blog`: https://ray-project.github.io/ray/
.. _`HotOS paper`: https://arxiv.org/abs/1703.03924
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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