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[rllib] remove redundant docs (#1728)

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* fix apex

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* Mon Mar 12 21:45:50 PDT 2018

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* Update apex_optimizer.py

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This commit is contained in:
Eric Liang
2018-03-17 14:45:04 -07:00
committed by GitHub
parent 9b361115c3
commit e3685fca5e
3 changed files with 8 additions and 47 deletions
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doc/source/policy-optimizers.rst
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Policy Optimizers
=================
RLlib supports using its distributed policy optimizer implementations from external algorithms.
RLlib supports using its policy optimizer implementations from external algorithms.
Example of constructing and using a policy optimizer `(link to full example) <https://github.com/ericl/baselines/blob/rllib-example/baselines/deepq/run_simple_loop.py>`__:
doc/source/rllib.rst
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Ray RLlib: Scalable Reinforcement Learning
==========================================
Ray RLlib is an RL execution toolkit built on the Ray distributed execution framework. RLlib implements a collection of distributed *policy optimizers* that make it easy to use a variety of training strategies with existing RL algorithms written in frameworks such as PyTorch, TensorFlow, and Theano. This enables complex architectures for RL training (e.g., Ape-X, IMPALA), to be implemented once and reused many times across different RL algorithms and libraries.
Ray RLlib is an RL execution toolkit built on the Ray distributed execution framework. RLlib implements a collection of distributed *policy optimizers* that make it easy to use a variety of training strategies with existing RL algorithms written in frameworks such as PyTorch, TensorFlow, and Theano.
You can find the code for RLlib `here on GitHub <https://github.com/ray-project/ray/tree/master/python/ray/rllib>`__, and the paper `here <https://arxiv.org/abs/1712.09381>`__.
.. note::
To use RLlib's policy optimizers outside of RLlib, see the `RLlib policy optimizers documentation <policy-optimizers.html>`__.
RLlib's policy optimizers serve as the basis for RLlib's reference algorithms, which include:
- `Proximal Policy Optimization (PPO) <https://arxiv.org/abs/1707.06347>`__ which
@@ -29,6 +24,10 @@ RLlib's policy optimizers serve as the basis for RLlib's reference algorithms, w
These algorithms can be run on any `OpenAI Gym MDP <https://github.com/openai/gym>`__,
including custom ones written and registered by the user.
.. note::
To use RLlib's policy optimizers outside of RLlib, see the `policy optimizers documentation <policy-optimizers.html>`__.
Installation
------------