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Eric Liang 5f430da180
[rllib] Provide internal access to episode state in compute_actions() and allow returning extra batches (#2559)
The goal of this PR is to allow custom policies to perform model-based rollouts. In the multi-agent setting, this requires access to not only policies of other agents, but also their current observations.
Also, you might want to return the model-based trajectories as part of the rollout for efficiency.

  compute_actions() now takes a new keyword arg episodes
  pull out internal episode class into a top-level file
  add function to return extra trajectories from an episode that will be appended to the sample batch
  documentation
2018-08-16 14:37:21 -07:00
.github Add docs for contributors. (#1191) 2017-11-10 00:40:19 -08:00
.travis silence progress log from 'git clone' and 'pip install' (#2667) 2018-08-15 22:54:35 -07:00
cmake/Modules Upgrade arrow to include the plasma TensorFlow op (#2412) 2018-07-18 12:33:02 -07:00
doc [rllib] Provide internal access to episode state in compute_actions() and allow returning extra batches (#2559) 2018-08-16 14:37:21 -07:00
docker [tune] Fix Categorical Space + Add Keras Example (#2401) 2018-07-17 23:52:52 +02:00
examples [rllib] _init renamed to _build_layers in example 2018-07-12 19:21:58 +02:00
java Upgrade arrow to include tensorflow op fix (#2607) 2018-08-14 21:47:01 -07:00
python [rllib] Provide internal access to episode state in compute_actions() and allow returning extra batches (#2559) 2018-08-16 14:37:21 -07:00
site Add parameter server blog post. (#2398) 2018-07-16 21:51:39 -07:00
src [xray] Resubmit tasks that fail to be forwarded (#2645) 2018-08-16 00:12:56 -07:00
test Add ray.internal.free (#2542) 2018-08-14 22:01:23 -07:00
thirdparty/scripts silence progress log from 'git clone' and 'pip install' (#2667) 2018-08-15 22:54:35 -07:00
.clang-format Implement object table notification subscriptions and switch to using Redis modules for object table. (#134) 2016-12-18 18:19:02 -08:00
.gitignore [xray] Adds a driver table. (#2289) 2018-08-08 23:41:40 -07:00
.style.yapf YAPF, take 3 (#2098) 2018-05-19 16:07:28 -07:00
.travis.yml [xray] Make sure raylet does not crash if remote raylet dies (#2619) 2018-08-09 20:36:30 -07:00
build-docker.sh adding -x flag for better debugging during builds (#1079) 2017-10-04 13:56:14 -07:00
build.sh Support building Java and Python version at the same time. (#2640) 2018-08-14 11:33:51 -07:00
CMakeLists.txt Support building Java and Python version at the same time. (#2640) 2018-08-14 11:33:51 -07:00
CONTRIBUTING.rst Replace special single quote with regular single quote. (#1693) 2018-03-10 20:36:01 -08:00
LICENSE [rllib] Basic IMPALA implementation (using deepmind's reference vtrace.py) (#2504) 2018-08-01 20:53:53 -07:00
pylintrc adding pylint (#233) 2016-07-08 12:39:11 -07:00
README.rst Update Travis CI badge from travis-ci.org to travis-ci.com. (#2155) 2018-05-29 16:44:02 -07:00
scripts Improve yapf speed and document its usage (#2160) 2018-06-05 20:22:11 -07:00
setup_thirdparty.sh Support building Java and Python version at the same time. (#2640) 2018-08-14 11:33:51 -07:00

Ray
===

.. image:: https://travis-ci.com/ray-project/ray.svg?branch=master
    :target: https://travis-ci.com/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.


Ray is easy to install: ``pip install ray``

Example Use
-----------

+------------------------------------------------+----------------------------------------------------+
| **Basic Python**                               | **Distributed with Ray**                           |
+------------------------------------------------+----------------------------------------------------+
|.. code-block:: python                          |.. code-block:: python                              |
|                                                |                                                    |
|  # Execute f serially.                         |  # Execute f in parallel.                          |
|                                                |                                                    |
|                                                |  @ray.remote                                       |
|  def f():                                      |  def f():                                          |
|      time.sleep(1)                             |      time.sleep(1)                                 |
|      return 1                                  |      return 1                                      |
|                                                |                                                    |
|                                                |                                                    |
|                                                |  ray.init()                                        |
|  results = [f() for i in range(4)]             |  results = ray.get([f.remote() for i in range(4)]) |
+------------------------------------------------+----------------------------------------------------+


Ray comes with libraries that accelerate deep learning and reinforcement learning development:

- `Ray Tune`_: Hyperparameter Optimization Framework
- `Ray RLlib`_: Scalable Reinforcement Learning

.. _`Ray Tune`: http://ray.readthedocs.io/en/latest/tune.html
.. _`Ray RLlib`: http://ray.readthedocs.io/en/latest/rllib.html

Installation
------------

Ray can be installed on Linux and Mac with ``pip install ray``.

To build Ray from source or to install the nightly versions, see the `installation documentation`_.

.. _`installation documentation`: http://ray.readthedocs.io/en/latest/installation.html

More Information
----------------

- `Documentation`_
- `Tutorial`_
- `Blog`_
- `Ray paper`_
- `Ray HotOS paper`_

.. _`Documentation`: http://ray.readthedocs.io/en/latest/index.html
.. _`Tutorial`: https://github.com/ray-project/tutorial
.. _`Blog`: https://ray-project.github.io/
.. _`Ray paper`: https://arxiv.org/abs/1712.05889
.. _`Ray HotOS paper`: https://arxiv.org/abs/1703.03924

Getting Involved
----------------

- Ask questions on our mailing list `ray-dev@googlegroups.com`_.
- Please report bugs by submitting a `GitHub issue`_.
- Submit contributions using `pull requests`_.

.. _`ray-dev@googlegroups.com`: https://groups.google.com/forum/#!forum/ray-dev
.. _`GitHub issue`: https://github.com/ray-project/ray/issues
.. _`pull requests`: https://github.com/ray-project/ray/pulls