ray/doc/source/rllib/package_ref/policy.rst
Max Pumperla f9b71a8bf6
[docs] new structure (#21776)
This PR consolidates both #21667 and #21759 (look there for features), but improves on them in the following way:

- [x] we reverted renaming of existing projects `tune`, `rllib`, `train`, `cluster`, `serve`, `raysgd` and `data` so that links won't break. I think my consolidation efforts with the `ray-` prefix were a little overeager in that regard. It's better like this. Only the creation of `ray-core` was a necessity, and some files moved into the `rllib` folder, so that should be relatively benign.
- [x] Additionally, we added Algolia `docsearch`, screenshot below. This is _much_ better than our current search. Caveat: there's a sphinx dependency that needs to be replaced (`sphinx-tabs`) by another, newer one (`sphinx-panels`), as the former prevents loading of the `algolia.js` library. Will follow-up in the next PR (hoping this one doesn't get re-re-re-re-reverted).
2022-01-21 15:42:05 -08:00

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.. _policy-reference-docs:
Policies
========
The :py:class:`~ray.rllib.policy.policy.Policy` class contains functionality to compute
actions for decision making in an environment, as well as computing loss(es) and gradients,
updating a neural network model as well as postprocessing a collected environment trajectory.
One or more :py:class:`~ray.rllib.policy.policy.Policy` objects sit inside a
:py:class:`~ray.rllib.evaluation.RolloutWorker`'s :py:class:`~ray.rllib.policy.policy_map.PolicyMap` and
are - if more than one - are selected based on a multi-agent ``policy_mapping_fn``,
which maps agent IDs to a policy ID.
.. https://docs.google.com/drawings/d/1eFAVV1aU47xliR5XtGqzQcdvuYs2zlVj1Gb8Gg0gvnc/edit
.. figure:: ../images/policy_classes_overview.svg
:align: left
**RLlib's Policy class hierarchy:** Policies are deep-learning framework
specific as they hold functionality to handle a computation graph (e.g. a
TensorFlow 1.x graph in a session). You can define custom policy behavior
by sub-classing either of the available, built-in classes, depending on your
needs.
Policy API Reference
--------------------
.. toctree::
:maxdepth: 1
policy/policy.rst
policy/tf_policies.rst
policy/torch_policy.rst
policy/custom_policies.rst