mirror of
https://github.com/vale981/ray
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319c1340cb
* add marvil policy graph * fix typo * add offline optimizer and enable running marwil * fix loss function * add maintaining the moving average of advantage norm * use sync replay optimizer for unifying * remove offline optimizer and use sync replay optimizer * format by yapf * add imitation learning objective * fix according to eric's review * format by yapf * revise * add test data * marwil
128 lines
5.1 KiB
ReStructuredText
128 lines
5.1 KiB
ReStructuredText
RLlib: Scalable Reinforcement Learning
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======================================
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RLlib is an open-source library for reinforcement learning that offers both a collection of reference algorithms and scalable primitives for composing new ones.
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.. image:: rllib-stack.svg
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Learn more about RLlib's design by reading the `ICML paper <https://arxiv.org/abs/1712.09381>`__.
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Installation
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------------
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RLlib has extra dependencies on top of ``ray``. First, you'll need to install either `PyTorch <http://pytorch.org/>`__ or `TensorFlow <https://www.tensorflow.org>`__. Then, install the RLlib module:
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.. code-block:: bash
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pip install tensorflow # or tensorflow-gpu
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pip install ray[rllib] # also recommended: ray[debug]
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You might also want to clone the `Ray repo <https://github.com/ray-project/ray>`__ for convenient access to RLlib helper scripts:
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.. code-block:: bash
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git clone https://github.com/ray-project/ray
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cd ray/python/ray/rllib
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Training APIs
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-------------
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* `Command-line <rllib-training.html>`__
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* `Configuration <rllib-training.html#configuration>`__
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* `Python API <rllib-training.html#python-api>`__
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* `Debugging <rllib-training.html#debugging>`__
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* `REST API <rllib-training.html#rest-api>`__
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Environments
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------------
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* `RLlib Environments Overview <rllib-env.html>`__
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* `OpenAI Gym <rllib-env.html#openai-gym>`__
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* `Vectorized <rllib-env.html#vectorized>`__
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* `Multi-Agent and Hierarchical <rllib-env.html#multi-agent-and-hierarchical>`__
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* `Interfacing with External Agents <rllib-env.html#interfacing-with-external-agents>`__
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* `Batch Asynchronous <rllib-env.html#batch-asynchronous>`__
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Algorithms
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----------
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* High-throughput architectures
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- `Distributed Prioritized Experience Replay (Ape-X) <rllib-algorithms.html#distributed-prioritized-experience-replay-ape-x>`__
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- `Importance Weighted Actor-Learner Architecture (IMPALA) <rllib-algorithms.html#importance-weighted-actor-learner-architecture-impala>`__
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* Gradient-based
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- `Advantage Actor-Critic (A2C, A3C) <rllib-algorithms.html#advantage-actor-critic-a2c-a3c>`__
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- `Deep Deterministic Policy Gradients (DDPG, TD3) <rllib-algorithms.html#deep-deterministic-policy-gradients-ddpg-td3>`__
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- `Deep Q Networks (DQN, Rainbow, Parametric DQN) <rllib-algorithms.html#deep-q-networks-dqn-rainbow-parametric-dqn>`__
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- `Policy Gradients <rllib-algorithms.html#policy-gradients>`__
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- `Proximal Policy Optimization (PPO) <rllib-algorithms.html#proximal-policy-optimization-ppo>`__
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* Derivative-free
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- `Augmented Random Search (ARS) <rllib-algorithms.html#augmented-random-search-ars>`__
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- `Evolution Strategies <rllib-algorithms.html#evolution-strategies>`__
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* Multi-agent specific
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- `QMIX Monotonic Value Factorisation (QMIX, VDN, IQN) <rllib-algorithms.html#qmix-monotonic-value-factorisation-qmix-vdn-iqn>`__
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* Offline
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- `Advantage Re-Weighted Imitation Learning (MARWIL) <rllib-algorithms.html#advantage-re-weighted-imitation-learning-marwil>`__
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Models and Preprocessors
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------------------------
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* `RLlib Models and Preprocessors Overview <rllib-models.html>`__
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* `Built-in Models and Preprocessors <rllib-models.html#built-in-models-and-preprocessors>`__
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* `Custom Models (TensorFlow) <rllib-models.html#custom-models-tensorflow>`__
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* `Custom Models (PyTorch) <rllib-models.html#custom-models-pytorch>`__
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* `Custom Preprocessors <rllib-models.html#custom-preprocessors>`__
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* `Customizing Policy Graphs <rllib-models.html#customizing-policy-graphs>`__
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* `Variable-length / Parametric Action Spaces <rllib-models.html#variable-length-parametric-action-spaces>`__
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* `Model-Based Rollouts <rllib-models.html#model-based-rollouts>`__
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Offline Datasets
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----------------
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* `Working with Offline Datasets <rllib-offline.html>`__
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* `Input API <rllib-offline.html#input-api>`__
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* `Output API <rllib-offline.html#output-api>`__
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Development
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-----------
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* `Development Install <rllib-dev.html#development-install>`__
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* `Features <rllib-dev.html#feature-development>`__
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* `Benchmarks <rllib-dev.html#benchmarks>`__
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* `Contributing Algorithms <rllib-dev.html#contributing-algorithms>`__
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Concepts
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--------
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* `Policy Graphs <rllib-concepts.html>`__
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* `Policy Evaluation <rllib-concepts.html#policy-evaluation>`__
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* `Policy Optimization <rllib-concepts.html#policy-optimization>`__
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Package Reference
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-----------------
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* `ray.rllib.agents <rllib-package-ref.html#module-ray.rllib.agents>`__
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* `ray.rllib.env <rllib-package-ref.html#module-ray.rllib.env>`__
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* `ray.rllib.evaluation <rllib-package-ref.html#module-ray.rllib.evaluation>`__
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* `ray.rllib.models <rllib-package-ref.html#module-ray.rllib.models>`__
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* `ray.rllib.optimizers <rllib-package-ref.html#module-ray.rllib.optimizers>`__
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* `ray.rllib.utils <rllib-package-ref.html#module-ray.rllib.utils>`__
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Troubleshooting
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---------------
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If you encounter errors like
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`blas_thread_init: pthread_create: Resource temporarily unavailable` when using many workers,
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try setting ``OMP_NUM_THREADS=1``. Similarly, check configured system limits with
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`ulimit -a` for other resource limit errors.
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For debugging unexpected hangs or performance problems, you can run ``ray stack`` to dump
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the stack traces of all Ray workers on the current node. This requires py-spy to be installed.
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