From 98509447bfc1d66c30055cbe55143edf81ddce49 Mon Sep 17 00:00:00 2001 From: Sven Mika Date: Tue, 29 Oct 2024 23:40:02 +0100 Subject: [PATCH] [RLlib; docs] RLlib documentation do-over (new API stack): Main index page. (#48285) --- doc/source/_includes/rllib/new_api_stack.rst | 6 +- doc/source/rllib/doc_code/rllib_in_60s.py | 6 +- doc/source/rllib/feature_overview.rst | 85 ----- .../rllib/images/algos/cql-architecture.svg | 1 + .../images/rllib-new-api-stack-simple.svg | 1 + ...llib-stack.svg => rllib-old-api-stack.svg} | 0 doc/source/rllib/images/rllib-stack.png | Bin 793670 -> 0 bytes .../rllib/images/sigils/new-api-stack.svg | 1 - .../rllib/images/sigils/old-api-stack.svg | 1 - doc/source/rllib/index.rst | 345 +++++++++++------- doc/source/rllib/rllib-algorithms.rst | 102 ++++-- doc/source/rllib/rllib-examples.rst | 178 +++------ .../policy_inference_after_training.py | 7 +- 13 files changed, 342 insertions(+), 391 deletions(-) delete mode 100644 doc/source/rllib/feature_overview.rst create mode 100644 doc/source/rllib/images/algos/cql-architecture.svg create mode 100644 doc/source/rllib/images/rllib-new-api-stack-simple.svg rename doc/source/rllib/images/{rllib-stack.svg => rllib-old-api-stack.svg} (100%) delete mode 100644 doc/source/rllib/images/rllib-stack.png delete mode 100644 doc/source/rllib/images/sigils/new-api-stack.svg delete mode 100644 doc/source/rllib/images/sigils/old-api-stack.svg diff --git a/doc/source/_includes/rllib/new_api_stack.rst b/doc/source/_includes/rllib/new_api_stack.rst index 08cdf1b5523b..b9a94c23bf20 100644 --- a/doc/source/_includes/rllib/new_api_stack.rst +++ b/doc/source/_includes/rllib/new_api_stack.rst @@ -1,7 +1,7 @@ .. note:: Ray 2.10.0 introduces the alpha stage of RLlib's "new API stack". - The Ray Team plans to transition algorithms, example scripts, and documentation to the new code base - thereby incrementally replacing the "old API stack" (e.g., ModelV2, Policy, RolloutWorker) throughout the subsequent minor releases leading up to Ray 3.0. + The team is currently transitioning algorithms, example scripts, and + documentation to the new code base throughout the subsequent minor releases leading up to Ray 3.0. - :doc:`See here ` for more details on how to use the new API stack. \ No newline at end of file + :doc:`See here ` for more details on how to activate and use the new API stack. diff --git a/doc/source/rllib/doc_code/rllib_in_60s.py b/doc/source/rllib/doc_code/rllib_in_60s.py index f52e1ea08823..a17de677cee0 100644 --- a/doc/source/rllib/doc_code/rllib_in_60s.py +++ b/doc/source/rllib/doc_code/rllib_in_60s.py @@ -2,13 +2,13 @@ # __rllib-in-60s-begin__ from ray.rllib.algorithms.ppo import PPOConfig +from ray.rllib.core.rl_module.default_model_config import DefaultModelConfig config = ( # 1. Configure the algorithm, PPOConfig() - .environment("Taxi-v3") + .environment(env="Taxi-v3") .env_runners(num_env_runners=2) - .framework("torch") - .training(model={"fcnet_hiddens": [64, 64]}) + .rl_module(model_config=DefaultModelConfig(fcnet_hiddens=[64, 64])) .evaluation(evaluation_num_env_runners=1) ) diff --git a/doc/source/rllib/feature_overview.rst b/doc/source/rllib/feature_overview.rst deleted file mode 100644 index 968079a6522e..000000000000 --- a/doc/source/rllib/feature_overview.rst +++ /dev/null @@ -1,85 +0,0 @@ -.. List of most important features of RLlib, with sigil-like buttons for each of the features. - To be included into different rst files. - -.. container:: clear-both - - .. container:: buttons-float-left - - .. https://docs.google.com/drawings/d/1yEOfeHvuLi5EzZKtGFQMfQ2NINzi3bUBrU3Z7bCiuKs/edit - - .. image:: images/sigils/rllib-sigil-distributed-learning.svg - :width: 100 - :target: https://github.com/ray-project/ray/blob/master/rllib/utils/framework.py - - .. container:: - - **Highly distributed learning**: Our RLlib algorithms (such as our "PPO" or "IMPALA") - allow you to set the ``num_env_runners`` config parameter, such that your workloads can run - on 100s of CPUs/nodes thus parallelizing and speeding up learning. - - -.. container:: clear-both - - .. container:: buttons-float-left - - .. https://docs.google.com/drawings/d/1Lbi1Zf5SvczSliGEWuK4mjWeehPIArYY9XKys81EtHU/edit - - .. image:: images/sigils/rllib-sigil-multi-agent.svg - :width: 100 - :target: https://github.com/ray-project/ray/blob/master/rllib/examples/multi_agent/pettingzoo_independent_learning.py - - .. container:: - - | **Multi-agent RL** (MARL): Convert your (custom) ``gym.Envs`` into a multi-agent one - via a few simple steps and start training your agents in any of the following fashions: - | 1) Cooperative with `shared `_ or - `separate `_ - policies and/or value functions. - | 2) Adversarial scenarios using `self-play `_ - and `league-based training `_. - | 3) `Independent learning `_ - of neutral/co-existing agents. - - -.. container:: clear-both - - .. container:: buttons-float-left - - .. https://docs.google.com/drawings/d/1DY2IJUPo007mSRylz6IEs-dz_n1-rFh67RMi9PB2niY/edit - - .. image:: images/sigils/rllib-sigil-external-simulators.svg - :width: 100 - :target: https://github.com/ray-project/ray/tree/master/rllib/examples/envs/external_envs - - .. container:: - - **External simulators**: Don't have your simulation running as a gym.Env in python? - No problem! RLlib supports an external environment API and comes with a pluggable, - off-the-shelve - `client `_/ - `server `_ - setup that allows you to run 100s of independent simulators on the "outside" - (e.g. a Windows cloud) connecting to a central RLlib Policy-Server that learns - and serves actions. Alternatively, actions can be computed on the client side - to save on network traffic. - - -.. container:: clear-both - - .. container:: buttons-float-left - - .. https://docs.google.com/drawings/d/1VFuESSI5u9AK9zqe9zKSJIGX8taadijP7Qw1OLv2hSQ/edit - - .. image:: images/sigils/rllib-sigil-offline-rl.svg - :width: 100 - :target: https://github.com/ray-project/ray/blob/master/rllib/examples/offline_rl/offline_rl.py - - .. container:: - - **Offline RL and imitation learning/behavior cloning**: You don't have a simulator - for your particular problem, but tons of historic data recorded by a legacy (maybe - non-RL/ML) system? This branch of reinforcement learning is for you! - RLlib's comes with several `offline RL `_ - algorithms (*CQL*, *MARWIL*, and *DQfD*), allowing you to either purely - `behavior-clone `_ - your existing system or learn how to further improve over it. diff --git a/doc/source/rllib/images/algos/cql-architecture.svg b/doc/source/rllib/images/algos/cql-architecture.svg new file mode 100644 index 000000000000..77ddfdc3e20d --- /dev/null +++ b/doc/source/rllib/images/algos/cql-architecture.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/doc/source/rllib/images/rllib-new-api-stack-simple.svg b/doc/source/rllib/images/rllib-new-api-stack-simple.svg new file mode 100644 index 000000000000..fcd2173986da --- /dev/null +++ b/doc/source/rllib/images/rllib-new-api-stack-simple.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/doc/source/rllib/images/rllib-stack.svg b/doc/source/rllib/images/rllib-old-api-stack.svg similarity index 100% rename from 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b/doc/source/rllib/images/sigils/old-api-stack.svg deleted file mode 100644 index 5378fbce4e64..000000000000 --- a/doc/source/rllib/images/sigils/old-api-stack.svg +++ /dev/null @@ -1 +0,0 @@ - diff --git a/doc/source/rllib/index.rst b/doc/source/rllib/index.rst index 16bd8647a2c4..f23b6516cd51 100644 --- a/doc/source/rllib/index.rst +++ b/doc/source/rllib/index.rst @@ -1,11 +1,39 @@ .. include:: /_includes/rllib/we_are_hiring.rst -.. include:: /_includes/rllib/new_api_stack.rst +.. |single_agent| image:: /rllib/images/sigils/single-agent.svg + :class: inline-figure + :width: 72 + +.. |multi_agent| image:: /rllib/images/sigils/multi-agent.svg + :class: inline-figure + :width: 72 + +.. |discr_act| image:: /rllib/images/sigils/discr-actions.svg + :class: inline-figure + :width: 72 + +.. |cont_act| image:: /rllib/images/sigils/cont-actions.svg + :class: inline-figure + :width: 72 + +.. |multi_gpu| image:: /rllib/images/sigils/multi-gpu.svg + :class: inline-figure + :width: 72 + +.. |multi_node_multi_gpu| image:: /rllib/images/sigils/multi-node-multi-gpu.svg + :class: inline-figure + :alt: Only on the Anyscale Platform! + :width: 72 .. _rllib-index: -RLlib: Industry-Grade Reinforcement Learning -============================================ +RLlib: Industry-Grade, Scalable Reinforcement Learning +====================================================== + +.. include:: /_includes/rllib/new_api_stack.rst + +.. image:: images/rllib-logo.png + :align: center .. toctree:: :hidden: @@ -21,134 +49,226 @@ RLlib: Industry-Grade Reinforcement Learning package_ref/index -.. image:: images/rllib-logo.png - :align: center +**RLlib** is an open source library for reinforcement learning (RL), offering support for +production-level, highly scalable, and fault-tolerant RL workloads, while maintaining simple and unified +APIs for a large variety of industry applications. -**RLlib** is an open-source library for reinforcement learning (RL), -offering support for -production-level, highly distributed RL workloads while maintaining -unified and simple APIs for a large variety of industry applications. -Whether you would like to train your agents in a **multi-agent** setup, -purely from **offline** (historic) datasets, or using **externally -connected simulators**, RLlib offers a simple solution for each of your decision -making needs. - -If you either have your problem coded (in python) as an -`RL environment `_ -or own lots of pre-recorded, historic behavioral data to learn from, you will be -up and running in only a few days. - -RLlib is already used in production by industry leaders in many different verticals, -such as -`climate control `_, -`industrial control `_, -`manufacturing and logistics `_, -`finance `_, +Whether training policies in a **multi-agent** setup, from historic **offline** data, +or using **externally connected simulators**, RLlib offers simple solutions for each of +these autonomous decision making needs and enables you to start running your experiments within hours. + +RLlib is used in production by industry leaders in many different verticals, such as `gaming `_, -`automobile `_, `robotics `_, -`boat design `_, -and many others. +`finance `_, +`climate- and industrial control `_, +`manufacturing and logistics `_, +`automobile `_, +and +`boat design `_. RLlib in 60 seconds ------------------- .. figure:: images/rllib-index-header.svg -It only takes a few steps to get your first RLlib workload -up and running on your laptop. +It only takes a few steps to get your first RLlib workload up and running on your laptop. +Install RLlib and `PyTorch `__, as shown below: + +.. code-block:: bash -RLlib does not automatically install a deep-learning framework, but supports -**TensorFlow** (both 1.x with static-graph and 2.x with eager mode) as well as -**PyTorch**. -Depending on your needs, make sure to install either TensorFlow or -PyTorch (or both, as shown below): + pip install "ray[rllib]" torch -.. raw:: html +.. note:: -

- pip install "ray[rllib]" tensorflow torch -
+ For installation on computers running Apple Silicon (such as M1), + `follow instructions here. `_ .. note:: - For installation on computers running Apple Silicon (such as M1), please follow instructions - `here. `_ - To be able to run our Atari examples, you should also install - `pip install "gym[atari]" "gym[accept-rom-license]" atari_py`. + To be able to run our Atari or MuJoCo examples, you also need to run: + `pip install "gymnasium[atari,accept-rom-license,mujoco]"`. + +This is all! You can now start coding against RLlib. Here is an example for running the PPO Algorithm on the +`Taxi domain `__. +You first create a `config` for the algorithm, which defines the RL environment (taxi) and +any other needed settings and parameters. + +Next, `build` the algorithm and `train` it for a total of 5 iterations. +One training iteration includes parallel (distributed) sample collection by the :py:class:`~ray.rllib.env.env_runner.EnvRunner` actors, +followed by loss calculation on the collected data, and a model update step. -This is all you need to start coding against RLlib. -Here is an example of running a PPO Algorithm on the -`Taxi domain `_. -We first create a `config` for the algorithm, which sets the right environment, and -defines all training parameters we want. -Next, we `build` the algorithm and `train` it for a total of `5` iterations. -A training iteration includes parallel sample collection by the environment workers, as well as loss calculation on the collected batch and a model update. -As a last step, we `evaluate` the trained Algorithm: +At the end of your script, the trained Algorithm is evaluated: .. literalinclude:: doc_code/rllib_in_60s.py :language: python :start-after: __rllib-in-60s-begin__ :end-before: __rllib-in-60s-end__ -Note that you can use any Farama-Foundation Gymnasium environment as `env`. -In `rollouts` you can for instance specify the number of parallel workers to collect samples from the environment. -The `framework` config lets you choose between "tf2", "tf" and "torch" for execution. -You can also tweak RLlib's default `model` config,and set up a separate config for `evaluation`. +You can use any `Farama-Foundation Gymnasium `__ registered environment +with the `env` argument. -If you want to learn more about the RLlib training API, -`you can learn more about it here `_. -Also, see `here for a simple example on how to write an action inference loop after training. `_ +In `config.env_runners()` you can specify - amongst many other things - the number of parallel +:py:class:`~ray.rllib.env.env_runner.EnvRunner` actors to collect samples from the environment. + +You can also tweak the NN architecture used by by tweaking RLlib's `DefaultModelConfig`, as well as, set up a separate +config for the evaluation :py:class:`~ray.rllib.env.env_runner.EnvRunner` actors through the `config.evaluation()` method. + +`See here `_, if you want to learn more about the RLlib training APIs. +Also, `see here `__ +for a simple example on how to write an action inference loop after training. If you want to get a quick preview of which **algorithms** and **environments** RLlib supports, -click on the dropdowns below: +click the dropdowns below: .. dropdown:: **RLlib Algorithms** :animate: fade-in-slide-down - * High-throughput architectures - - - |pytorch| |tensorflow| :ref:`Importance Weighted Actor-Learner Architecture (IMPALA) ` - - - |pytorch| |tensorflow| :ref:`Asynchronous Proximal Policy Optimization (APPO) ` - - * Gradient-based + +-------------------------------------------------------------------------+----------------+---------------+-------------+------------+-------------+------------------------+ + | **On-Policy** | + +-------------------------------------------------------------------------+----------------+---------------+-------------+------------+-------------+------------------------+ + | :ref:`PPO (Proximal Policy Optimization) ` | |single_agent| | |multi_agent| | |discr_act| | |cont_act| | |multi_gpu| | |multi_node_multi_gpu| | + +-------------------------------------------------------------------------+----------------+---------------+-------------+------------+-------------+------------------------+ + | **Off-Policy** | + +-------------------------------------------------------------------------+----------------+---------------+-------------+------------+-------------+------------------------+ + | :ref:`SAC (Soft Actor Critic) ` | |single_agent| | |multi_agent| | | |cont_act| | |multi_gpu| | |multi_node_multi_gpu| | + +-------------------------------------------------------------------------+----------------+---------------+-------------+------------+-------------+------------------------+ + | :ref:`DQN/Rainbow (Deep Q Networks) ` | |single_agent| | |multi_agent| | |discr_act| | | |multi_gpu| | |multi_node_multi_gpu| | + +-------------------------------------------------------------------------+----------------+---------------+-------------+------------+-------------+------------------------+ + | **High-throughput Architectures** | + +-------------------------------------------------------------------------+----------------+---------------+-------------+------------+-------------+------------------------+ + | :ref:`IMPALA (Importance Weighted Actor-Learner Architecture) ` | |single_agent| | |multi_agent| | |discr_act| | | |multi_gpu| | |multi_node_multi_gpu| | + +-------------------------------------------------------------------------+----------------+---------------+-------------+------------+-------------+------------------------+ + | :ref:`APPO (Asynchronous Proximal Policy Optimization) ` | |single_agent| | |multi_agent| | |discr_act| | | |multi_gpu| | |multi_node_multi_gpu| | + +-------------------------------------------------------------------------+----------------+---------------+-------------+------------+-------------+------------------------+ + | **Model-based RL** | + +-------------------------------------------------------------------------+----------------+---------------+-------------+------------+-------------+------------------------+ + | :ref:`DreamerV3 ` | |single_agent| | | |discr_act| | |cont_act| | |multi_gpu| | |multi_node_multi_gpu| | + +-------------------------------------------------------------------------+----------------+---------------+-------------+------------+-------------+------------------------+ + | **Offline RL and Imitation Learning** | + +-------------------------------------------------------------------------+----------------+---------------+-------------+------------+-------------+------------------------+ + | :ref:`BC (Behavior Cloning) ` | |single_agent| | | |discr_act| | |cont_act| | | | + +-------------------------------------------------------------------------+----------------+---------------+-------------+------------+-------------+------------------------+ + | :ref:`CQL (Conservative Q-Learning) ` | |single_agent| | | | |cont_act| | | | + +-------------------------------------------------------------------------+----------------+---------------+-------------+------------+-------------+------------------------+ + | :ref:`MARWIL (Advantage Re-Weighted Imitation Learning) ` | |single_agent| | | |discr_act| | |cont_act| | | | + +-------------------------------------------------------------------------+----------------+---------------+-------------+------------+-------------+------------------------+ - - |pytorch| |tensorflow| :ref:`Deep Q Networks (DQN, Rainbow, Parametric DQN) ` - - |pytorch| |tensorflow| :ref:`Proximal Policy Optimization (PPO) ` +.. dropdown:: **RLlib Environments** + :animate: fade-in-slide-down - - |pytorch| |tensorflow| :ref:`Soft Actor Critic (SAC) ` + +-------------------------------------------------------------------------------------------| + | **Farama-Foundation Environments** | + +-------------------------------------------------------------------------------------------| + | `gymnasium `__ |single_agent| | + | | + | .. code-block:: bash | + | | + | pip install "gymnasium[atari,accept-rom-license,mujoco]"`` | + | | + | .. code-block:: python | + | | + | config.environment("CartPole-v1") # Classic Control | + | config.environment("ale_py:ALE/Pong-v5") # Atari | + | config.environment("Hopper-v5") # MuJoCo | + +-------------------------------------------------------------------------------------------| + | `PettingZoo `__ |multi_agent| | + | | + | .. code-block:: bash | + | | + | pip install "pettingzoo[all]" | + | | + | .. code-block:: python | + | | + | from ray.tune.registry import register_env | + | from ray.rllib.env.wrappers.pettingzoo_env import PettingZooEnv | + | from pettingzoo.sisl import waterworld_v4 | + | register_env("env", lambda _: PettingZooEnv(waterworld_v4.env())) | + | config.environment("env") | + +-------------------------------------------------------------------------------------------+ + | **RLlib Multi-Agent** | + +-------------------------------------------------------------------------------------------+ + | `RLlib's MultiAgentEnv API `__ |multi_agent| | + | | + | .. code-block:: python | + | | + | config.environment("CartPole-v1") # Classic Control | + | config.environment("ale_py:ALE/Pong-v5") # Atari | + +-------------------------------------------------------------------------------------------+ + + +Why chose RLlib? +---------------- - * Model-based / Meta-learning / Offline +.. dropdown:: **Scalable and Fault-Tolerant** + :animate: fade-in-slide-down - - |pytorch| :ref:`DreamerV3 ` + RLlib workloads scale along various axes: - * Offline + - The number of :py:class:`~ray.rllib.env.env_runner.EnvRunner` actors to use. + This is configurable through ``config.env_runners(num_env_runners=...)`` and + allows you to scale the speed of your (simulator) data collection step. + This `EnvRunner` axis is fully **fault tolerant**, meaning you can train against + custom environments that are unstable or frequently stall execution and even place all + your `EnvRunner` actors on spot machines. - - |pytorch| |tensorflow| :ref:`Advantage Re-Weighted Imitation Learning (MARWIL) ` + - The number of :py:class:`~ray.rllib.core.learner.Learner` actors to use for **multi-GPU training**. + This is configurable through ``config.learners(num_learners=...)`` and you normally + set this to the number of GPUs available (make sure you then also set + ``config.learners(num_gpus_per_learner=1)``) or - if you do not have GPUs - you can + use this setting for **DDP-style learning on CPUs** instead. +.. dropdown:: **Multi-Agent (MARL)** + :animate: fade-in-slide-down -.. dropdown:: **RLlib Environments** + RLlib natively supports multi-agent reinforcement learning (MARL), thereby allowing you to run + in any complex configuration. + + - **Independent** multi-agent learning (the default): Every agent collects data for updating its own + policy network, interpreting other agents as part of the environment. + - **Collaborative** training: Train a team of agents that either all share the same policy (shared parameters) + or in which some agents have their own policy network(s). You can also share value functions between all + members of the team or some of them, as you see fit, thus allowing for global vs local objectives to be + optimized. + - **Adversarial** training: Have agents play against other agents in competitive environments. Use self-play, + or league based self-play to train your agents to learn how to play throughout various stages of + ever increasing difficulty. + - **Any combination of the above!** Yes, you can train teams of arbitrary sizes of agents playing against + other teams where the agents in each team might have individual sub-objectives and there are groups + of neutral agents not participating in any competition. + +.. dropdown:: **Offline RL** :animate: fade-in-slide-down - * `RLlib Environments Overview `__ - * `Farama-Foundation gymnasium `__ - * `Vectorized `__ - * `Multi-Agent and Hierarchical `__ - * `External Agents and Applications `__ + **Ray.Data** has been integrated into RLlib, enabling **large-scale data ingestion** for offline RL and behavior + cloning (BC) workloads. - - `External Application Clients `__ + See here for a basic `tuned example for the behavior cloning algo `__ + and here for how to `pre-train a policy with BC, then finetuning it with online PPO `__. - * `Advanced Integrations `__ -Feature Overview ----------------- +Learn More +---------- .. grid:: 1 2 3 3 :gutter: 1 :class-container: container pb-4 + .. grid-item-card:: + + **RLlib Environments** + ^^^ + Get started with environments supported by RLlib, such as Farama foundation's Gymnasium, Petting Zoo, + and many custom formats for vectorized and multi-agent environments. + +++ + .. button-ref:: rllib-environments-doc + :color: primary + :outline: + :expand: + + Environments + .. grid-item-card:: **RLlib Key Concepts** @@ -177,59 +297,24 @@ Feature Overview Algorithms - .. grid-item-card:: - - **RLlib Environments** - ^^^ - Get started with environments supported by RLlib, such as Farama foundation's Gymnasium, Petting Zoo, - and many custom formats for vectorized and multi-agent environments. - +++ - .. button-ref:: rllib-environments-doc - :color: primary - :outline: - :expand: - - Environments - - -The following is a summary of RLlib's most striking features. -Click on the images below to see an example script for each of the listed features: - -.. include:: feature_overview.rst - Customizing RLlib ----------------- -RLlib provides simple APIs to customize all aspects of your training- and experimental workflows. +RLlib provides powerful, yet easy to use APIs for customizing all aspects of your experimental- and +production training-workflows. For example, you may code your own `environments `__ -in python using Farama-Foundation's gymnasium or DeepMind's OpenSpiel, provide custom -`TensorFlow/Keras- `__ or , -`Torch models `_, write your own -`policy- and loss definitions `__, or define -custom `exploratory behavior `_. +in python using the `Farama Foundation's gymnasium `__ or DeepMind's OpenSpiel, +provide custom `PyTorch models `_, +write your own `optimizer setups and loss definitions `__, +or define custom `exploratory behavior `_. -Via mapping one or more agents in your environments to (one or more) policies, multi-agent -RL (MARL) becomes an easy-to-use low-level primitive for our users. -.. figure:: images/rllib-stack.svg +.. figure:: images/rllib-new-api-stack-simple.svg :align: left - :width: 650 - - **RLlib's API stack:** Built on top of Ray, RLlib offers off-the-shelf, highly distributed - algorithms, policies, loss functions, and default models (including the option to - auto-wrap a neural network with an LSTM or an attention net). Furthermore, our library - comes with a built-in Server/Client setup, allowing you to connect - hundreds of external simulators (clients) via the network to an RLlib server process, - which provides learning functionality and serves action queries. User customizations - are realized via sub-classing the existing abstractions and - by overriding certain - methods in those sub-classes - define custom behavior. + :width: 850 - -.. |tensorflow| image:: images/tensorflow.png - :class: inline-figure - :width: 16 - -.. |pytorch| image:: images/pytorch.png - :class: inline-figure - :width: 16 + **RLlib's API stack:** Built on top of Ray, RLlib offers off-the-shelf, distributed and fault-tolerant + algorithms and loss functions, PyTorch default models, multi-GPU training, and multi-agent support. + User customizations are realized by subclassing the existing abstractions and - by overriding certain + methods in those subclasses - define custom behavior. diff --git a/doc/source/rllib/rllib-algorithms.rst b/doc/source/rllib/rllib-algorithms.rst index 0a776648322d..3214562d697c 100644 --- a/doc/source/rllib/rllib-algorithms.rst +++ b/doc/source/rllib/rllib-algorithms.rst @@ -63,11 +63,12 @@ Proximal Policy Optimization (PPO) .. figure:: images/algos/ppo-architecture.svg :width: 750 - **PPO architecture:** In a training iteration, PPO performs three major steps: sampling a set of episodes or episode fragments (1), - converting these into a train batch and updating the model(s) using a clipped objective and multiple SGD passes over this batch (2), - and synching the weights from the Learners back to the EnvRunners (3). + **PPO architecture:** In a training iteration, PPO performs three major steps: + 1. Sampling a set of episodes or episode fragments + 1. Converting these into a train batch and updating the model using a clipped objective and multiple SGD passes over this batch + 1. Synching the weights from the Learners back to the EnvRunners PPO scales out on both axes, supporting multiple EnvRunners for sample collection and multiple GPU- or CPU-based Learners - for updating the model(s). + for updating the model. **Tuned examples:** @@ -95,12 +96,12 @@ Deep Q Networks (DQN, Rainbow, Parametric DQN) .. figure:: images/algos/dqn-architecture.svg :width: 650 - **DQN architecture:** DQN uses a replay buffer to temporarily store episode samples collected from the environment(s). + **DQN architecture:** DQN uses a replay buffer to temporarily store episode samples that RLlib collects from the environment. Throughout different training iterations, these episodes and episode fragments are re-sampled from the buffer and re-used for updating the model, before eventually being discarded when the buffer has reached capacity and new samples keep coming in (FIFO). This reuse of training data makes DQN very sample-efficient and off-policy. DQN scales out on both axes, supporting multiple EnvRunners for sample collection and multiple GPU- or CPU-based Learners - for updating the model(s). + for updating the model. All of the DQN improvements evaluated in `Rainbow `__ are available, though not all are enabled by default. @@ -140,12 +141,12 @@ Soft Actor Critic (SAC) .. figure:: images/algos/sac-architecture.svg :width: 750 - **SAC architecture:** SAC uses a replay buffer to temporarily store episode samples collected from the environment(s). + **SAC architecture:** SAC uses a replay buffer to temporarily store episode samples that RLlib collects from the environment. Throughout different training iterations, these episodes and episode fragments are re-sampled from the buffer and re-used for updating the model, before eventually being discarded when the buffer has reached capacity and new samples keep coming in (FIFO). This reuse of training data makes DQN very sample-efficient and off-policy. SAC scales out on both axes, supporting multiple EnvRunners for sample collection and multiple GPU- or CPU-based Learners - for updating the model(s). + for updating the model. **Tuned examples:** @@ -171,14 +172,14 @@ Importance Weighted Actor-Learner Architecture (IMPALA) .. figure:: images/algos/impala-architecture.svg :width: 750 - **IMPALA architecture:** In a training iteration, IMPALA requests samples from all EnvRunners asynchronously and the collected episode - samples are returned to the main algo process as ray references (rather than actual objects available on the local algo process). - These episode references are then passed to the Learner(s) for asynchronous updates of the model(s). - To account for the fact that this asynchronous design leads to some degree of off-policiness - on the EnvRunners (models are not always synched back to EnvRunners right after a new version of the weights is available), IMPALA uses - a procedure called v-trace, `described in the paper `__. + **IMPALA architecture:** In a training iteration, IMPALA requests samples from all EnvRunners asynchronously and the collected episodes + are returned to the main algorithm process as Ray references rather than actual objects available on the local process. + IMPALA then passes these episode references to the Learners for asynchronous updates of the model. + RLlib doesn't always synch back the weights to the EnvRunners right after a new model version is available. + To account for the EnvRunners being off-policy, IMPALA uses a procedure called v-trace, + `described in the paper `__. IMPALA scales out on both axes, supporting multiple EnvRunners for sample collection and multiple GPU- or CPU-based Learners - for updating the model(s). + for updating the model. Tuned examples: @@ -217,13 +218,13 @@ Asynchronous Proximal Policy Optimization (APPO) **APPO architecture:** APPO is an asynchronous variant of :ref:`Proximal Policy Optimization (PPO) ` based on the IMPALA architecture, but using a surrogate policy loss with clipping, allowing for multiple SGD passes per collected train batch. In a training iteration, APPO requests samples from all EnvRunners asynchronously and the collected episode - samples are returned to the main algo process as ray references (rather than actual objects available on the local algo process). - These episode references are then passed to the Learner(s) for asynchronous updates of the model(s). - To account for the fact that this asynchronous design leads to some degree of off-policiness - on the EnvRunners (models are not always synched back to EnvRunners right after a new version of the weights is available), APPO uses - a procedure called v-trace, `described in the IMPALA paper here `__. + samples are returned to the main algorithm process as Ray references rather than actual objects available on the local process. + APPO then passes these episode references to the Learners for asynchronous updates of the model. + RLlib doesn't always synch back the weights to the EnvRunners right after a new model version is available. + To account for the EnvRunners being off-policy, APPO uses a procedure called v-trace, + `described in the IMPALA paper `__. APPO scales out on both axes, supporting multiple EnvRunners for sample collection and multiple GPU- or CPU-based Learners - for updating the model(s). + for updating the model. **Tuned examples:** @@ -250,13 +251,13 @@ DreamerV3 :width: 850 **DreamerV3 architecture:** DreamerV3 trains a recurrent WORLD_MODEL in supervised fashion - using real environment interactions (sampled from a replay buffer). The WORLD_MODEL's objective + using real environment interactions sampled from a replay buffer. The world model's objective is to correctly predict the transition dynamics of the RL environment: next observation, reward, and a boolean continuation flag. - The ACTOR- and CRITIC-networks are subsequently trained on synthesized trajectories only, - which are "dreamed" by the WORLD_MODEL. + DreamerV3 trains the actor- and critic-networks on synthesized trajectories only, + which are "dreamed" by the world model. DreamerV3 scales out on both axes, supporting multiple EnvRunners for sample collection and - multiple GPU- or CPU-based Learners for updating the model(s). + multiple GPU- or CPU-based Learners for updating the model. It can also be used in different environment types, including those with image- or vector based observations, continuous- or discrete actions, as well as sparse or dense reward functions. @@ -310,11 +311,11 @@ Behavior Cloning (BC) :width: 750 **BC architecture:** RLlib's behavioral cloning (BC) uses Ray Data to tap into its parallel data - processing capabilities. In one training iteration, episodes are parallelly read in from - offline (ex. JSON) files by the n DataWorkers. These episodes are then preprocessed into train - batches and sent as data iterators directly to the n Learners, which perform the forward- and backward passes - as well as the optimizer step. - RLlib's (BC) implementation is directly derived from the `MARWIL`_ implementation, + processing capabilities. In one training iteration, BC reads episodes in parallel from + offline files, for example `parquet `__, by the n DataWorkers. + Connector pipelines then preprocess these episodes into train batches and send these as + data iterators directly to the n Learners for updating the model. + RLlib's (BC) implementation is directly derived from its `MARWIL`_ implementation, with the only difference being the ``beta`` parameter (set to 0.0). This makes BC try to match the behavior policy, which generated the offline data, disregarding any resulting rewards. @@ -328,6 +329,31 @@ Behavior Cloning (BC) :members: training +.. _cql: + +Conservative Q-Learning (CQL) +----------------------------- +`[paper] `__ +`[implementation] `__ + +.. figure:: images/algos/cql-architecture.svg + :width: 750 + + **CQL architecture:** CQL (Conservative Q-Learning) is an offline RL algorithm that mitigates the overestimation of Q-values + outside the dataset distribution through a conservative critic estimate. It adds a simple Q regularizer loss to the standard + Bellman update loss, ensuring that the critic doesn't output overly optimistic Q-values. + The `SACLearner` adds this conservative correction term to the TD-based Q-learning loss. + + +**Tuned examples:** +`Pendulum-v1 `__ + +**CQL-specific configs** and `common configs `__): + +.. autoclass:: ray.rllib.algorithms.cql.cql.CQLConfig + :members: training + + .. _marwil: Monotonic Advantage Re-Weighted Imitation Learning (MARWIL) @@ -340,15 +366,14 @@ Monotonic Advantage Re-Weighted Imitation Learning (MARWIL) **MARWIL architecture:** MARWIL is a hybrid imitation learning and policy gradient algorithm suitable for training on batched historical data. When the ``beta`` hyperparameter is set to zero, the MARWIL objective reduces to plain - imitation learning (see `BC`_). MARWIL uses Ray Data to tap into its parallel data - processing capabilities. In one training iteration, episodes are parallelly read in from - offline (ex. JSON) files by the n DataWorkers. These episodes are then preprocessed into train - batches and sent as data iterators directly to the n Learners, which perform the forward- and backward passes - as well as the optimizer step. + imitation learning (see `BC`_). MARWIL uses Ray.Data to tap into its parallel data + processing capabilities. In one training iteration, MARWIL reads episodes in parallel from offline files, + for example `parquet `__, by the n DataWorkers. Connector pipelines preprocess these + episodes into train batches and send these as data iterators directly to the n Learners for updating the model. **Tuned examples:** -`CartPole-v1 `__ +`CartPole-v1 `__ **MARWIL-specific configs** (see also `common configs `__): @@ -370,9 +395,10 @@ Curiosity-driven Exploration by Self-supervised Prediction .. figure:: images/algos/curiosity-architecture.svg :width: 850 - **ICM (intrinsic curiosity model) architecture:** The main idea behind ICM is to train a world-model + **Intrinsic Curiosity Model (ICM) architecture:** The main idea behind ICM is to train a world-model (in parallel to the "main" policy) to predict the environment's dynamics. The loss of - the world model is the intrinsic reward that will be added to the env's reward. This makes sure + the world model is the intrinsic reward that the `ICMLearner` adds to the env's + (extrinsic) reward. This makes sure that when in regions of the environment that are relatively unknown (world model performs badly in predicting what happens next), the artificial intrinsic reward is large and the agent is motivated to go and explore these unknown regions. diff --git a/doc/source/rllib/rllib-examples.rst b/doc/source/rllib/rllib-examples.rst index 616290b6bdd8..148f9a7ee851 100644 --- a/doc/source/rllib/rllib-examples.rst +++ b/doc/source/rllib/rllib-examples.rst @@ -2,14 +2,6 @@ .. include:: /_includes/rllib/new_api_stack.rst -.. |new_stack| image:: /rllib/images/sigils/new-api-stack.svg - :class: inline-figure - :width: 64 - -.. |old_stack| image:: /rllib/images/sigils/old-api-stack.svg - :class: inline-figure - :width: 64 - Examples ======== @@ -56,39 +48,39 @@ Most of the scripts share a common subset of generally applicable command line a for example `--num-env-runners`, `--no-tune`, or `--wandb-key`. -All sub-folders -+++++++++++++++ +All example sub-folders ++++++++++++++++++++++++ Algorithms ---------- -- |new_stack| `How to write a custom Algorith.training_step() method combining on- and off-policy training `__: +- `How to write a custom Algorith.training_step() method combining on- and off-policy training `__: Example of how to override the :py:meth:`~ray.rllib.algorithms.algorithm.Algorithm.training_step` method to train two different policies in parallel (also using multi-agent API). Checkpoints ----------- -- |new_stack| `How to extract a checkpoint from n Tune trials using one or more custom criteria. `__: +- `How to extract a checkpoint from n Tune trials using one or more custom criteria. `__: Example of how to find a :ref:`checkpoint ` after a `Tuner.fit()` with some custom defined criteria. Connectors ---------- .. note:: - RLlib's Connector API has been re-written from scratch for the new API stack (|new_stack|). + RLlib's Connector API has been re-written from scratch for the new API stack. Connector-pieces and -pipelines are now referred to as :py:class:`~ray.rllib.connectors.connector_v2.ConnectorV2` - (as opposed to ``Connector``, which only continue to work on the old API stack |old_stack|). + (as opposed to ``Connector``, which only continue to work on the old API stack). -- |new_stack| `How to frame-stack Atari image observations `__: +- `How to frame-stack Atari image observations `__: An example using Atari framestacking in a very efficient manner, not in the environment itself (as a `gym.Wrapper`), but by stacking the observations on-the-fly using `EnvToModule` and `LearnerConnector` pipelines. This method of framestacking is more efficient as it avoids having to send large observation tensors through the network (ray). -- |new_stack| `How to mean/std-filter observations `__: +- `How to mean/std-filter observations `__: An example of a :py:class:`~ray.rllib.connectors.connector_v2.ConnectorV2` that filters all observations from the environment using a plain mean/std-filter (shift by the mean and divide by std-dev). This example demonstrates how a stateful :py:class:`~ray.rllib.connectors.connector_v2.ConnectorV2` class has its states @@ -96,174 +88,104 @@ Connectors :py:class:`~ray.rllib.env.env_runner.EnvRunner` instances a) merged into one common state and then b) broadcast again back to the remote :py:class:`~ray.rllib.env.env_runner.EnvRunner` workers. -- |new_stack| `How to include previous-actions and/or previous rewards in RLModule inputs `__: +- `How to include previous-actions and/or previous rewards in RLModule inputs `__: An example of a :py:class:`~ray.rllib.connectors.connector_v2.ConnectorV2` that adds the n previous actions and/or the m previous rewards to the RLModule's input dict (to perform its forward passes, both for inference and training). -- |new_stack| `How to train with nested action spaces `__: +- `How to train with nested action spaces `__: Learning in arbitrarily nested action spaces, using an env in which the action space equals the observation space (both are complex, nested Dicts) and the policy has to pick actions that closely match (or are identical) to the previously seen observations. -- |new_stack| `How to train with nested observation spaces `__: +- `How to train with nested observation spaces `__: Learning in arbitrarily nested observation spaces (using a CartPole-v1 variant with a nested Dict observation space). Curriculum Learning ------------------- -- |new_stack| `How to set up curriculum learning with the custom callbacks API `__: +- `How to set up curriculum learning with the custom callbacks API `__: Example of how to make the environment go through different levels of difficulty (from easy to harder to solve) and thus help the learning algorithm to cope with an otherwise unsolvable task. Also see the :doc:`curriculum learning how-to ` from the documentation. -Debugging ---------- -- |old_stack| `How to train an RLlib algorithm using a deterministic/reproducible setup `__: - Example showing how you can train an RLlib algorithm in a deterministic, reproducible fashion using seeding. - Environments ------------ -- |new_stack| `How to register a custom gymnasium environment `__: +- `How to register a custom gymnasium environment `__: Example showing how to write your own RL environment using ``gymnasium`` and register it to run train your algorithm against this env with RLlib. -- |new_stack| `How to set up rendering (and recording) of the environment trajectories during training with WandB `__: +- `How to set up rendering and recording of the environment trajectories during training with W&B `__: Example showing how you can render and record episode trajectories of your gymnasium envs and log the videos to WandB. -- |old_stack| `How to run a Unity3D multi-agent environment locally `__: - Example of how to setup an RLlib Algorithm against a locally running Unity3D editor instance to - learn any Unity3D game (including support for multi-agent). - Use this example to try things out and watch the game and the learning progress live in the editor. - Providing a compiled game, this example could also run in distributed fashion with `num_env_runners > 0`. - For a more heavy-weight, distributed, cloud-based example, see ``Unity3D client/server`` below. - -- |old_stack| `How to run with a Unity3D client/server setup `__: - Example of how to setup n distributed Unity3D (compiled) games in the cloud that function as data collecting - clients against a central RLlib Policy server learning how to play the game. - The n distributed clients could themselves be servers for external/human players and allow for control - being fully in the hands of the Unity entities instead of RLlib. - Note: Uses Unity's MLAgents SDK (>=1.0) and supports all provided MLAgents example games and multi-agent setups. - -- |old_stack| `How to run with a CartPole client/server setup `__: - Example of online serving of predictions for a simple CartPole policy. - - Evaluation ---------- -- |new_stack| `How to run evaluation with a custom evaluation function `__: +- `How to run evaluation with a custom evaluation function `__: Example of how to write a custom evaluation function that's called instead of the default behavior, which is running with the evaluation worker set through n episodes. -- |new_stack| `How to run evaluation in parallel to training `__: +- `How to run evaluation in parallel to training `__: Example showing how the evaluation workers and the "normal" rollout workers can run (to some extend) in parallel to speed up training. GPU (for Training and Sampling) ------------------------------- -- |new_stack| `How to use fractional GPUs for training an RLModule `__: +- `How to use fractional GPUs for training an RLModule `__: If your model is small and easily fits on a single GPU and you want to therefore train other models alongside it to save time and cost, this script shows you how to set up your RLlib config with a fractional number of GPUs on the learner (model training) side. -Hierarchical Training ---------------------- - -- |old_stack| `How to setup hierarchical training `__: - Example of hierarchical training using the multi-agent API. - Inference (of Models/Policies) ------------------------------ -- |old_stack| `How to do inference with an already trained policy `__: +- `How to do inference with an already trained policy `__: Example of how to perform inference (compute actions) on an already trained policy. -- |old_stack| `How to do inference with an already trained (LSTM) policy `__: +- `How to do inference with an already trained (LSTM) policy `__: Example of how to perform inference (compute actions) on an already trained (LSTM) policy. -- |old_stack| `How to do inference with an already trained (attention) policy `__: - Example of how to perform inference (compute actions) on an already trained (attention) policy. - -Metrics -------- - -- |old_stack| `How to write your own custom metrics and callbacks in RLlib `__: - Example of how to output custom training metrics to TensorBoard. - Multi-Agent RL -------------- -- |new_stack| `How to set up independent multi-agent training `__: +- `How to set up independent multi-agent training `__: Set up RLlib to run any algorithm in (independent) multi-agent mode against a multi-agent environment. -- |new_stack| `How to set up shared-parameter multi-agent training `__: +- `How to set up shared-parameter multi-agent training `__: Set up RLlib to run any algorithm in (shared-parameter) multi-agent mode against a multi-agent environment. -- |new_stack| `How to compare a heuristic policy vs a trained one on rock-paper-scissors `__ and `Rock-paper-scissors learned vs learned `__: +- `How to compare a heuristic policy versus a trained one on rock-paper-scissors `__ and `Rock-paper-scissors learned vs learned `__: Two examples of different heuristic and learned policies competing against each other in the rock-paper-scissors environment. -- |new_stack| `How to use agent grouping in a multi-agent environment (two-step game) `__: +- `How to use agent grouping in a multi-agent environment (two-step game) `__: Example on how to use agent grouping in a multi-agent environment (the two-step game from the `QMIX paper `__). -- |new_stack| `How to set up multi-agent training vs a PettingZoo environment `__: +- `How to set up multi-agent training versus a PettingZoo environment `__: Example on how to use RLlib to learn in `PettingZoo `__ multi-agent environments. -- |new_stack| `How to hand-code a (heuristic) policy `__: +- `How to hand-code a (heuristic) policy `__: Example of running a custom hand-coded policy alongside trainable policies. -- |new_stack| `How to train a single policy (weight sharing) controlling more than one agents `__: +- `How to train a single policy (weight sharing) controlling more than one agent `__: Example of how to define weight-sharing layers between two different policies. -- |old_stack| `Hwo to write and set up a model with centralized critic `__: - Example of customizing PPO to leverage a centralized value function. -- |old_stack| `How to write and set up a model with centralized critic in the env `__: - A simpler method of implementing a centralized critic by augmenting agent observations with global information. -- |old_stack| `How to combine multiple algorithms into onw using the multi-agent API `__: - Example of alternating training between DQN and PPO. - -Offline RL ----------- - -- |old_stack| `How to run an offline RL experiment with CQL `__: - Example showing how to run an offline RL training job using a historic-data JSON file. - -- |old_stack| `How to save experiences from an environment for offline RL `__: - Example of how to externally generate experience batches in RLlib-compatible format. - - Ray Serve and RLlib ------------------- -- |new_stack| `How to use a trained RLlib algorithm with Ray Serve `__ +- `How to use a trained RLlib algorithm with Ray Serve `__ This script offers a simple workflow for 1) training a policy with RLlib first, 2) creating a new policy 3) restoring its weights from the trained one and serving the new policy with Ray Serve. Ray Tune and RLlib ------------------ -- |new_stack| `How to define a custom progress reporter and use it with Ray Tune and RLlib `__: +- `How to define a custom progress reporter and use it with Ray Tune and RLlib `__: Example of how to write your own progress reporter (for a multi-agent experiment) and use it with Ray Tune and RLlib. -- |new_stack| `How to define and plug in your custom logger into Ray Tune and RLlib `__: +- `How to define and plug in your custom logger into Ray Tune and RLlib `__: How to setup a custom Logger object in RLlib and use it with Ray Tune. -- |new_stack| `How to Custom tune experiment `__: +- `How to custom tune an experiment `__: How to run a custom Ray Tune experiment with RLlib with custom training- and evaluation phases. RLModules --------- -- |new_stack| `How to configure an autoregressive action distribution `__: +- `How to configure an autoregressive action distribution `__: Learning with an auto-regressive action distribution (for example, two action components, where distribution of the second component depends on the first's actually sampled value). -- |old_stack| `How to train with parametric actions `__: - Example of how to handle variable-length or parametric action spaces. -- |old_stack| `How to using the "Repeated" space of RLlib for variable lengths observations `__: - How to use RLlib's `Repeated` space to handle variable length observations. -- |old_stack| `How to write a custom Keras model `__: - Example of defining and registering a custom model with a supervised loss. -- |old_stack| `How to train with batch normalization `__: - Shows how to define a custom Model API in RLlib, such that it can be used inside certain algorithms. -- |old_stack| `How to write a "trajectory ciew API" utilizing model `__: - An example on how a model can use the trajectory view API to specify its own input. -- |old_stack| `How to wrap MobileNetV2 into your RLlib model `__: - Implementations of `tf.keras.applications.mobilenet_v2.MobileNetV2` and `torch.hub (mobilenet_v2)`-wrapping example models. -- |old_stack| `How to setup a Differentiable Neural Computer `__: - Example of DeepMind's Differentiable Neural Computer for partially observable environments. - Tuned Examples ++++++++++++++ @@ -288,51 +210,51 @@ release tests as well. Community Examples ++++++++++++++++++ -- |old_stack| `Arena AI `__: +- `Arena AI `__: A General Evaluation Platform and Building Toolkit for Single/Multi-Agent Intelligence with RLlib-generated baselines. -- |old_stack| `CARLA `__: +- `CARLA `__: Example of training autonomous vehicles with RLlib and `CARLA `__ simulator. -- |old_stack| `The Emergence of Adversarial Communication in Multi-Agent Reinforcement Learning `__: +- `The Emergence of Adversarial Communication in Multi-Agent Reinforcement Learning `__: Using Graph Neural Networks and RLlib to train multiple cooperative and adversarial agents to solve the "cover the area"-problem, thereby learning how to best communicate (or - in the adversarial case - how to disturb communication) (`code `__). -- |old_stack| `Flatland `__: +- `Flatland `__: A dense traffic simulating environment with RLlib-generated baselines. -- |old_stack| `GFootball `__: +- `GFootball `__: Example of setting up a multi-agent version of `GFootball `__ with RLlib. -- |old_stack| `mobile-env `__: +- `mobile-env `__: An open, minimalist Gymnasium environment for autonomous coordination in wireless mobile networks. Includes an example notebook using Ray RLlib for multi-agent RL with mobile-env. -- |old_stack| `Neural MMO `__: +- `Neural MMO `__: A multiagent AI research environment inspired by Massively Multiplayer Online (MMO) role playing games – self-contained worlds featuring thousands of agents per persistent macrocosm, diverse skilling systems, local and global economies, complex emergent social structures, and ad-hoc high-stakes single and team based conflict. -- |old_stack| `NeuroCuts `__: +- `NeuroCuts `__: Example of building packet classification trees using RLlib / multi-agent in a bandit-like setting. -- |old_stack| `NeuroVectorizer `__: +- `NeuroVectorizer `__: Example of learning optimal LLVM vectorization compiler pragmas for loops in C and C++ codes using RLlib. -- |old_stack| `Roboschool / SageMaker `__: +- `Roboschool / SageMaker `__: Example of training robotic control policies in SageMaker with RLlib. -- |old_stack| `Sequential Social Dilemma Games `__: +- `Sequential Social Dilemma Games `__: Example of using the multi-agent API to model several `social dilemma games `__. -- |old_stack| `Simple custom environment for single RL with Ray and RLlib `__: +- `Simple custom environment for single RL with Ray and RLlib `__: Create a custom environment and train a single agent RL using Ray 2.0 with Tune. -- |old_stack| `StarCraft2 `__: +- `StarCraft2 `__: Example of training in StarCraft2 maps with RLlib / multi-agent. -- |old_stack| `Traffic Flow `__: +- `Traffic Flow `__: Example of optimizing mixed-autonomy traffic simulations with RLlib / multi-agent. Blog Posts ++++++++++ -- |old_stack| `Attention Nets and More with RLlib’s Trajectory View API `__: +- `Attention Nets and More with RLlib’s Trajectory View API `__: Blog describing RLlib's new "trajectory view API" and how it enables implementations of GTrXL (attention net) architectures. -- |old_stack| `Reinforcement Learning with RLlib in the Unity Game Engine `__: +- `Reinforcement Learning with RLlib in the Unity Game Engine `__: How-To guide about connecting RLlib with the Unity3D game engine for running visual- and physics-based RL experiments. -- |old_stack| `Lessons from Implementing 12 Deep RL Algorithms in TF and PyTorch `__: +- `Lessons from Implementing 12 Deep RL Algorithms in TF and PyTorch `__: Discussion on how the Ray Team ported 12 of RLlib's algorithms from TensorFlow to PyTorch and the lessons learned. -- |old_stack| `Scaling Multi-Agent Reinforcement Learning `__: +- `Scaling Multi-Agent Reinforcement Learning `__: Blog post of a brief tutorial on multi-agent RL and its design in RLlib. -- |old_stack| `Functional RL with Keras and TensorFlow Eager `__: +- `Functional RL with Keras and TensorFlow Eager `__: Exploration of a functional paradigm for implementing reinforcement learning (RL) algorithms. diff --git a/rllib/examples/inference/policy_inference_after_training.py b/rllib/examples/inference/policy_inference_after_training.py index e33976c22e80..b462263a5dab 100644 --- a/rllib/examples/inference/policy_inference_after_training.py +++ b/rllib/examples/inference/policy_inference_after_training.py @@ -136,7 +136,10 @@ best_result = results.get_best_result( metric=f"{ENV_RUNNER_RESULTS}/{EPISODE_RETURN_MEAN}", mode="max" ) - # Create new Algorithm and restore its state from the last checkpoint. + # Create new RLModule and restore its state from the last algo checkpoint. + # Note that the checkpoint for the RLModule can be found deeper inside the algo + # checkpoint's sub-directories ([algo dir] -> "learner/" -> "module_state/" -> + # "[module ID]): rl_module = RLModule.from_checkpoint( os.path.join( best_result.checkpoint.path, @@ -147,7 +150,7 @@ ) ) - # Create the env to do inference in. + # Create an env to do inference in. env = gym.make(args.env) obs, info = env.reset()