RLlib Table of Contents
=======================

Training APIs
-------------
*  `Command-line <rllib-training.html>`__
*  `Configuration <rllib-training.html#configuration>`__

   -  `Specifying Parameters <rllib-training.html#specifying-parameters>`__

   -  `Specifying Resources <rllib-training.html#specifying-resources>`__

   -  `Common Parameters <rllib-training.html#common-parameters>`__

   -  `Tuned Examples <rllib-training.html#tuned-examples>`__

*  `Python API <rllib-training.html#python-api>`__

   -  `Custom Training Workflows <rllib-training.html#custom-training-workflows>`__

   -  `Accessing Policy State <rllib-training.html#accessing-policy-state>`__

   -  `Accessing Model State <rllib-training.html#accessing-model-state>`__

   -  `Global Coordination <rllib-training.html#global-coordination>`__

   -  `Callbacks and Custom Metrics <rllib-training.html#callbacks-and-custom-metrics>`__

   -  `Rewriting Trajectories <rllib-training.html#rewriting-trajectories>`__

   -  `Curriculum Learning <rllib-training.html#curriculum-learning>`__

*  `Debugging <rllib-training.html#debugging>`__

   -  `Gym Monitor <rllib-training.html#gym-monitor>`__

   -  `Eager Mode <rllib-training.html#eager-mode>`__

   -  `Episode Traces <rllib-training.html#episode-traces>`__

   -  `Log Verbosity <rllib-training.html#log-verbosity>`__

   -  `Stack Traces <rllib-training.html#stack-traces>`__

*  `REST API <rllib-training.html#rest-api>`__

Environments
------------
* `RLlib Environments Overview <rllib-env.html>`__
* `Feature Compatibility Matrix <rllib-env.html#feature-compatibility-matrix>`__
* `OpenAI Gym <rllib-env.html#openai-gym>`__
* `Vectorized <rllib-env.html#vectorized>`__
* `Multi-Agent and Hierarchical <rllib-env.html#multi-agent-and-hierarchical>`__
* `Interfacing with External Agents <rllib-env.html#interfacing-with-external-agents>`__
* `Advanced Integrations <rllib-env.html#advanced-integrations>`__

Models, Preprocessors, and Action Distributions
-----------------------------------------------
* `RLlib Models, Preprocessors, and Action Distributions Overview <rllib-models.html>`__
* `TensorFlow Models <rllib-models.html#tensorflow-models>`__
* `PyTorch Models <rllib-models.html#pytorch-models>`__
* `Custom Preprocessors <rllib-models.html#custom-preprocessors>`__
* `Custom Action Distributions <rllib-models.html#custom-action-distributions>`__
* `Supervised Model Losses <rllib-models.html#supervised-model-losses>`__
* `Variable-length / Parametric Action Spaces <rllib-models.html#variable-length-parametric-action-spaces>`__
* `Autoregressive Action Distributions <rllib-models.html#autoregressive-action-distributions>`__

Algorithms
----------

*  High-throughput architectures

   -  `Distributed Prioritized Experience Replay (Ape-X) <rllib-algorithms.html#distributed-prioritized-experience-replay-ape-x>`__

   -  `Importance Weighted Actor-Learner Architecture (IMPALA) <rllib-algorithms.html#importance-weighted-actor-learner-architecture-impala>`__

   -  `Asynchronous Proximal Policy Optimization (APPO) <rllib-algorithms.html#asynchronous-proximal-policy-optimization-appo>`__

*  Gradient-based

   -  `Advantage Actor-Critic (A2C, A3C) <rllib-algorithms.html#advantage-actor-critic-a2c-a3c>`__

   -  `Deep Deterministic Policy Gradients (DDPG, TD3) <rllib-algorithms.html#deep-deterministic-policy-gradients-ddpg-td3>`__

   -  `Deep Q Networks (DQN, Rainbow, Parametric DQN) <rllib-algorithms.html#deep-q-networks-dqn-rainbow-parametric-dqn>`__

   -  `Policy Gradients <rllib-algorithms.html#policy-gradients>`__

   -  `Proximal Policy Optimization (PPO) <rllib-algorithms.html#proximal-policy-optimization-ppo>`__

   -  `Soft Actor Critic (SAC) <rllib-algorithms.html#soft-actor-critic-sac>`__

*  Derivative-free

   -  `Augmented Random Search (ARS) <rllib-algorithms.html#augmented-random-search-ars>`__

   -  `Evolution Strategies <rllib-algorithms.html#evolution-strategies>`__

*  Multi-agent specific

   -  `QMIX Monotonic Value Factorisation (QMIX, VDN, IQN) <rllib-algorithms.html#qmix-monotonic-value-factorisation-qmix-vdn-iqn>`__
   -  `Multi-Agent Deep Deterministic Policy Gradient (contrib/MADDPG) <rllib-algorithms.html#multi-agent-deep-deterministic-policy-gradient-contrib-maddpg>`__

*  Offline

   -  `Advantage Re-Weighted Imitation Learning (MARWIL) <rllib-algorithms.html#advantage-re-weighted-imitation-learning-marwil>`__

Offline Datasets
----------------
* `Working with Offline Datasets <rllib-offline.html>`__
* `Input Pipeline for Supervised Losses <rllib-offline.html#input-pipeline-for-supervised-losses>`__
* `Input API <rllib-offline.html#input-api>`__
* `Output API <rllib-offline.html#output-api>`__

Concepts and Custom Algorithms
------------------------------
*  `Policies <rllib-concepts.html>`__

   -  `Policies in Multi-Agent <rllib-concepts.html#policies-in-multi-agent>`__

   -  `Building Policies in TensorFlow <rllib-concepts.html#building-policies-in-tensorflow>`__

   -  `Building Policies in TensorFlow Eager <rllib-concepts.html#building-policies-in-tensorflow-eager>`__

   -  `Building Policies in PyTorch <rllib-concepts.html#building-policies-in-pytorch>`__

   -  `Extending Existing Policies <rllib-concepts.html#extending-existing-policies>`__

*  `Policy Evaluation <rllib-concepts.html#policy-evaluation>`__
*  `Policy Optimization <rllib-concepts.html#policy-optimization>`__
*  `Trainers <rllib-concepts.html#trainers>`__

Examples
--------

* `Tuned Examples <rllib-examples.html#tuned-examples>`__
* `Training Workflows <rllib-examples.html#training-workflows>`__
* `Custom Envs and Models <rllib-examples.html#custom-envs-and-models>`__
* `Serving and Offline <rllib-examples.html#serving-and-offline>`__
* `Multi-Agent and Hierarchical <rllib-examples.html#multi-agent-and-hierarchical>`__
* `Community Examples <rllib-examples.html#community-examples>`__

Development
-----------

* `Development Install <rllib-dev.html#development-install>`__
* `API Stability <rllib-dev.html#api-stability>`__
* `Features <rllib-dev.html#feature-development>`__
* `Benchmarks <rllib-dev.html#benchmarks>`__
* `Contributing Algorithms <rllib-dev.html#contributing-algorithms>`__

Package Reference
-----------------
* `ray.rllib.agents <rllib-package-ref.html#module-ray.rllib.agents>`__
* `ray.rllib.env <rllib-package-ref.html#module-ray.rllib.env>`__
* `ray.rllib.evaluation <rllib-package-ref.html#module-ray.rllib.evaluation>`__
* `ray.rllib.models <rllib-package-ref.html#module-ray.rllib.models>`__
* `ray.rllib.optimizers <rllib-package-ref.html#module-ray.rllib.optimizers>`__
* `ray.rllib.utils <rllib-package-ref.html#module-ray.rllib.utils>`__

Troubleshooting
---------------

If you encounter errors like
`blas_thread_init: pthread_create: Resource temporarily unavailable` when using many workers,
try setting ``OMP_NUM_THREADS=1``. Similarly, check configured system limits with
`ulimit -a` for other resource limit errors.

If you encounter out-of-memory errors, consider setting ``redis_max_memory`` and ``object_store_memory`` in ``ray.init()`` to reduce memory usage.

For debugging unexpected hangs or performance problems, you can run ``ray stack`` to dump
the stack traces of all Ray workers on the current node, and ``ray timeline`` to dump
a timeline visualization of tasks to a file.

TensorFlow 2.0
~~~~~~~~~~~~~~

RLlib currently runs in ``tf.compat.v1`` mode. This means eager execution is disabled by default, and RLlib imports TF with ``import tensorflow.compat.v1 as tf; tf.disable_v2_behaviour()``. Eager execution can be enabled manually by calling ``tf.enable_eager_execution()`` or setting the ``"eager": True`` trainer config.