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Signed-off-by: simonsays1980 <simon.zehnder@gmail.com>
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4 changes: 2 additions & 2 deletions doc/source/rllib/algorithm-config.rst
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.. _rllib-algo-configuration-docs:

AlgorithmConfig API
===================

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RLlib's :py:class:`~ray.rllib.algorithms.algorithm_config.AlgorithmConfig` API is
the auto-validated and type-safe gateway into configuring and building an RLlib
:py:class:`~ray.rllib.algorithms.algorithm.Algorithm`.
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6 changes: 2 additions & 4 deletions doc/source/rllib/checkpoints.rst
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.. _rllib-checkpoints-docs:

Checkpointing
=============

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RLlib offers a powerful checkpointing system for all its major classes, allowing you to save the
states of :py:class:`~ray.rllib.algorithms.algorithm.Algorithm` instances and their subcomponents
to local disk or cloud storage, and restore previously run experiment states and individual subcomponents.
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5 changes: 2 additions & 3 deletions doc/source/rllib/external-envs.rst
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.. _rllib-external-env-setups-doc:


External Environments and Applications
======================================

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In many situations, it doesn't make sense for an RL environment to be "stepped" by RLlib.
For example, if you train a policy inside a complex simulator that operates its own execution loop,
like a game engine or a robotics simulation. A natural and user friendly approach is to flip this setup around
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5 changes: 2 additions & 3 deletions doc/source/rllib/hierarchical-envs.rst
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.. _rllib-hierarchical-environments-doc:


Hierarchical Environments
=========================

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You can implement hierarchical training as a special case of multi-agent RL. For example, consider a two-level hierarchy of policies,
where a top-level policy issues high level tasks that are executed at a finer timescale by one or more low-level policies.
The following timeline shows one step of the top-level policy, which corresponds to four low-level actions:
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6 changes: 2 additions & 4 deletions doc/source/rllib/key-concepts.rst
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.. _rllib-key-concepts:

Key concepts
============

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To help you get a high-level understanding of how the library works, on this page, you learn about the
key concepts and general architecture of RLlib.

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4 changes: 2 additions & 2 deletions doc/source/rllib/multi-agent-envs.rst
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.. _rllib-multi-agent-environments-doc:

Multi-Agent Environments
========================

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In a multi-agent environment, multiple "agents" act simultaneously, in a turn-based
sequence, or through an arbitrary combination of both.

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5 changes: 2 additions & 3 deletions doc/source/rllib/new-api-stack-migration-guide.rst
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.. _rllib-new-api-stack-migration-guide:

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New API stack migration guide
=============================

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This page explains, step by step, how to convert and translate your existing old API stack
RLlib classes and code to RLlib's new API stack.

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5 changes: 2 additions & 3 deletions doc/source/rllib/package_ref/algorithm-config.rst
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.. _algorithm-config-reference-docs:


Algorithm Configuration API
===========================

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.. currentmodule:: ray.rllib.algorithms.algorithm_config

Constructor
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4 changes: 2 additions & 2 deletions doc/source/rllib/package_ref/algorithm.rst
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.. _algorithm-reference-docs:

Algorithms
==========

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The :py:class:`~ray.rllib.algorithms.algorithm.Algorithm` class is the highest-level API in RLlib responsible for **WHEN** and **WHAT** of RL algorithms.
Things like **WHEN** should we sample the algorithm, **WHEN** should we perform a neural network update, and so on.
The **HOW** will be delegated to components such as ``RolloutWorker``, etc..
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4 changes: 3 additions & 1 deletion doc/source/rllib/package_ref/callback.rst
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.. _rllib-callback-reference-docs:

Callback APIs
=============

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Callback APIs enable you to inject code into an experiment, an Algorithm,
and the subcomponents of an Algorithm.

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5 changes: 2 additions & 3 deletions doc/source/rllib/package_ref/distributions.rst
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.. _rllib-distributions-reference-docs:

Distribution API
================

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.. currentmodule:: ray.rllib.models.distributions

Base Distribution class
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5 changes: 2 additions & 3 deletions doc/source/rllib/package_ref/env.rst
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.. _env-reference-docs:

Environments
============

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RLlib mainly supports the `Farama gymnasium API <https://gymnasium.farama.org/>`__ for
single-agent environments, and RLlib's own :py:class:`~ray.rllib.env.multi_agent_env.MultiAgentEnv`
API for multi-agent setups.
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5 changes: 2 additions & 3 deletions doc/source/rllib/package_ref/env/env_runner.rst
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.. _env-runner-reference-docs:

EnvRunner API
=============

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rllib.env.env_runner.EnvRunner
------------------------------

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5 changes: 2 additions & 3 deletions doc/source/rllib/package_ref/env/multi_agent_env.rst
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.. _multi-agent-env-reference-docs:

MultiAgentEnv API
=================

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rllib.env.multi_agent_env.MultiAgentEnv
---------------------------------------

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4 changes: 2 additions & 2 deletions doc/source/rllib/package_ref/env/multi_agent_env_runner.rst
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.. _multi-agent-env-runner-reference-docs:

MultiAgentEnvRunner API
=======================

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rllib.env.multi_agent_env_runner.MultiAgentEnvRunner
----------------------------------------------------

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4 changes: 2 additions & 2 deletions doc/source/rllib/package_ref/env/multi_agent_episode.rst
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.. _multi-agent-episode-reference-docs:

MultiAgentEpisode API
=====================

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rllib.env.multi_agent_episode.MultiAgentEpisode
-----------------------------------------------

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4 changes: 2 additions & 2 deletions doc/source/rllib/package_ref/env/single_agent_env_runner.rst
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.. _single-agent-env-runner-reference-docs:

SingleAgentEnvRunner API
========================

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rllib.env.single_agent_env_runner.SingleAgentEnvRunner
------------------------------------------------------

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4 changes: 2 additions & 2 deletions doc/source/rllib/package_ref/env/single_agent_episode.rst
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.. _single-agent-episode-reference-docs:

SingleAgentEpisode API
======================

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rllib.env.single_agent_episode.SingleAgentEpisode
-------------------------------------------------

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4 changes: 2 additions & 2 deletions doc/source/rllib/package_ref/env/utils.rst
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.. _env-utils-reference-docs:

Env Utils
=========

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rllib.env.utils
---------------

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5 changes: 2 additions & 3 deletions doc/source/rllib/package_ref/index.rst
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.. _rllib-reference-docs:

Ray RLlib API
=============

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.. tip:: We'd love to hear your feedback on using RLlib - `sign up to our forum and start asking questions <https://discuss.ray.io>`_!

This section contains an overview of RLlib's package- and API reference.
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6 changes: 2 additions & 4 deletions doc/source/rllib/package_ref/learner.rst
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.. _learner-reference-docs:


LearnerGroup API
================

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Configuring a LearnerGroup and Learner actors
---------------------------------------------

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6 changes: 2 additions & 4 deletions doc/source/rllib/package_ref/offline.rst
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.. _new-api-offline-reference-docs:


Offline RL API
==============

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Configuring Offline RL
----------------------

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5 changes: 2 additions & 3 deletions doc/source/rllib/package_ref/replay-buffers.rst
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.. _replay-buffer-api-reference-docs:

Replay Buffer API
=================

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The following classes don't take into account the separation of experiences from different policies, multi-agent replay buffers will be explained further below.

Replay Buffer Base Classes
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4 changes: 1 addition & 3 deletions doc/source/rllib/package_ref/rl_modules.rst
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.. _rlmodule-reference-docs:

RLModule APIs
=============

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RLModule specifications and configurations
-------------------------------------------
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5 changes: 2 additions & 3 deletions doc/source/rllib/package_ref/utils.rst
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.. _utils-reference-docs:

RLlib Utilities
===============

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Here is a list of all the utilities available in RLlib.

Scheduler API
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