Richard Liaw b463d9e5c7 Initial A3C Example - PongDeterministic-v3 (#331)
* Initializing A3C code

* Modifications for Ray usage

* cleanup

* removing universe dependency

* fixes (not yet working

* hack

* documentation

* Cleanup

* Preliminary Portion

Make sure to change when merging

* RL part

* Cleaning up Driver and Worker code

* Updating driver code

* instructions...

* fixed

* Minor changes.

* Fixing cmake issues

* ray instruction

* updating port to new universe

* Fix for env.configure

* redundant commands

* Revert scipy.misc -> cv2 and raise exception for wrong gym version.
2017-03-11 00:57:53 -08:00
2016-11-22 17:04:24 -08:00
2016-07-28 13:11:13 -07:00
2017-02-28 18:57:51 -08:00
2016-11-01 23:19:06 -07:00
2016-07-08 12:39:11 -07:00
2016-11-22 17:04:24 -08:00

Ray

Build Status Documentation Status

Ray is an experimental distributed execution engine. It is under development and not ready to be used.

The goal of Ray is to make it easy to write machine learning applications that run on a cluster while providing the development and debugging experience of working on a single machine.

View the documentation.

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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