Richard Liaw bc082e9a9e [rllib] Additional support for Shared Models in A3C (#866)
* 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
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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
S
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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