doc/source/rllib/key-concepts.rst
.. include:: /_includes/rllib/we_are_hiring.rst
.. _rllib-key-concepts:
.. include:: /_includes/rllib/new_api_stack.rst
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.
.. figure:: images/rllib_key_concepts.svg :width: 750 :align: left
**RLlib overview:** The central component of RLlib is the :py:class:`~ray.rllib.algorithms.algorithm.Algorithm`
class, acting as a runtime for executing your RL experiments.
Your gateway into using an :ref:`Algorithm <rllib-key-concepts-algorithms>` is the
:py:class:`~ray.rllib.algorithms.algorithm_config.AlgorithmConfig` (cyan) class, allowing
you to manage available configuration settings, for example learning rate or model architecture.
Most :py:class:`~ray.rllib.algorithms.algorithm.Algorithm` objects have
:py:class:`~ray.rllib.env.env_runner.EnvRunner` actors (blue) to collect training samples
from the :ref:`RL environment <rllib-key-concepts-environments>` and
:py:class:`~ray.rllib.core.learner.learner.Learner` actors (yellow)
to compute gradients and update your :ref:`models <rllib-key-concepts-rl-modules>`.
The algorithm synchronizes model weights after an update.
.. _rllib-key-concepts-algorithms:
.. todo (sven): Change the following link to the actual algorithm and algorithm-config page, once done. Right now, it's pointing to the algos-overview page, instead!
.. tip::
The following is a quick overview of RLlib AlgorithmConfigs and Algorithms.
See here for a :ref:detailed description of the Algorithm class <rllib-algorithms-doc>.
The RLlib :py:class:~ray.rllib.algorithms.algorithm.Algorithm class serves as a runtime for your RL experiments,
bringing together all components required for learning an optimal solution to your :ref:RL environment <rllib-key-concepts-environments>.
It exposes powerful Python APIs for controlling your experiment runs.
The gateways into using the various RLlib :py:class:~ray.rllib.algorithms.algorithm.Algorithm types are the respective
:py:class:~ray.rllib.algorithms.algorithm_config.AlgorithmConfig classes, allowing you to configure
available settings in a checked and type-safe manner.
For example, to configure a :py:class:~ray.rllib.algorithms.ppo.ppo.PPO ("Proximal Policy Optimization") algorithm instance,
you use the :py:class:~ray.rllib.algorithms.ppo.ppo.PPOConfig class.
During its construction, the :py:class:~ray.rllib.algorithms.algorithm.Algorithm first sets up its
:py:class:~ray.rllib.env.env_runner_group.EnvRunnerGroup, containing n
:py:class:~ray.rllib.env.env_runner.EnvRunner actors <actors.html>, and
its :py:class:~ray.rllib.core.learner.learner_group.LearnerGroup, containing
m :py:class:~ray.rllib.core.learner.learner.Learner actors <actors.html>.
This way, you can scale up sample collection and training, respectively, from a single core to many thousands of cores in a cluster.
.. todo: Separate out our scaling guide into its own page in new PR
See this :ref:scaling guide <rllib-scaling-guide> for more details here.
You have two ways to interact with and run an :py:class:~ray.rllib.algorithms.algorithm.Algorithm:
~ray.rllib.algorithms.algorithm.Algorithm class is a subclass of the :ref:Tune Trainable API <tune-60-seconds>,
you can use Ray Tune <https://docs.ray.io/en/latest/tune/index.html>__ to more easily manage your experiment and tune hyperparameters.The following examples demonstrate this on RLlib's :py:class:~ray.rllib.algorithms.ppo.PPO ("Proximal Policy Optimization") algorithm:
.. tab-set::
.. tab-item:: Manage Algorithm instance directly
.. testcode::
from ray.rllib.algorithms.ppo import PPOConfig
# Configure.
config = (
PPOConfig()
.environment("CartPole-v1")
.training(
train_batch_size_per_learner=2000,
lr=0.0004,
)
)
# Build the Algorithm.
algo = config.build()
# Train for one iteration, which is 2000 timesteps (1 train batch).
print(algo.train())
.. testcode::
:hide:
algo.stop()
.. tab-item:: Run Algorithm through Ray Tune
.. testcode::
from ray import tune
from ray.rllib.algorithms.ppo import PPOConfig
# Configure.
config = (
PPOConfig()
.environment("CartPole-v1")
.training(
train_batch_size_per_learner=2000,
lr=0.0004,
)
)
# Train through Ray Tune.
results = tune.Tuner(
"PPO",
param_space=config,
# Train for 4000 timesteps (2 iterations).
run_config=tune.RunConfig(stop={"num_env_steps_sampled_lifetime": 4000}),
).fit()
.. _rllib-key-concepts-environments:
.. tip::
The following is a quick overview of RL environments.
See :ref:here for a detailed description of how to use RL environments in RLlib <rllib-environments-doc>.
A reinforcement learning (RL) environment is a structured space, like a simulator or a controlled section of the real world, in which one or more agents interact and learn to achieve specific goals. The environment defines an observation space, which is the structure and shape of observable tensors at each timestep, an action space, which defines the available actions for the agents at each time step, a reward function, and the rules that govern environment transitions when applying actions.
.. figure:: images/envs/env_loop_concept.svg :width: 900 :align: left
A simple **RL environment** where an agent starts with an initial observation returned by the ``reset()`` method.
The agent, possibly controlled by a neural network policy, sends actions, like ``right`` or ``jump``,
to the environment's ``step()`` method, which returns a reward. Here, the reward values are +5 for reaching the goal
and 0 otherwise. The environment also returns a boolean flag indicating whether the episode is complete.
Environments may vary in complexity, from simple tasks, like navigating a grid world, to highly intricate systems, like autonomous driving simulators, robotic control environments, or multi-agent games.
RLlib interacts with the environment by playing through many :ref:episodes <rllib-key-concepts-episodes> during a
training iteration to collect data, such as made observations, taken actions, received rewards and done flags
(see preceding figure). It then converts this episode data into a train batch for model updating. The goal of these
model updates is to change the agents' behaviors such that it leads to a maximum sum of received rewards over the agents'
lifetimes.
.. _rllib-key-concepts-rl-modules:
.. tip::
The following is a quick overview of RLlib RLModules.
See :ref:here for a detailed description of the RLModule class <rlmodule-guide>.
RLModules <rl-modules.html>__ are deep-learning framework-specific neural network wrappers.
RLlib's :ref:EnvRunners <rllib-key-concepts-env-runners> use them for computing actions when stepping through the
:ref:RL environment <rllib-key-concepts-environments> and RLlib's :ref:Learners <rllib-key-concepts-learners> use
:py:class:~ray.rllib.core.rl_module.rl_module.RLModule instances for computing losses and gradients before updating them.
.. figure:: images/rl_modules/rl_module_overview.svg :width: 750 :align: left
**RLModule overview**: *(left)* A minimal :py:class:`~ray.rllib.core.rl_module.rl_module.RLModule` contains a neural network
and defines its forward exploration-, inference- and training logic.
*(right)* In more complex setups, a :py:class:`~ray.rllib.core.rl_module.multi_rl_module.MultiRLModule` contains
many submodules, each itself an :py:class:`~ray.rllib.core.rl_module.rl_module.RLModule` instance and
identified by a ``ModuleID``, allowing you to implement arbitrarily complex multi-model and multi-agent algorithms.
In a nutshell, an :py:class:~ray.rllib.core.rl_module.rl_module.RLModule carries the neural
network models and defines how to use them during the three phases of its RL lifecycle:
Exploration, for collecting training data, inference when computing actions for evaluation or in production,
and training for computing the loss function inputs.
You can choose to use :ref:RLlib's built-in default models and configure these <rllib-default-rl-modules-docs> as needed,
for example for changing the number of layers or the activation functions, or
:ref:write your own custom models in PyTorch <rllib-implementing-custom-rl-modules>,
allowing you to implement any architecture and computation logic.
.. figure:: images/rl_modules/rl_module_in_env_runner.svg :width: 450 :align: left
**An RLModule inside an EnvRunner actor**: The :py:class:`~ray.rllib.env.env_runner.EnvRunner` operates on its own copy of an
inference-only version of the :py:class:`~ray.rllib.core.rl_module.rl_module.RLModule`, using it only to compute actions.
Each :py:class:~ray.rllib.env.env_runner.EnvRunner actor, managed by the :py:class:~ray.rllib.env.env_runner_group.EnvRunnerGroup of the Algorithm,
has a copy of the user's :py:class:~ray.rllib.core.rl_module.rl_module.RLModule.
Also, each :py:class:~ray.rllib.core.learner.learner.Learner actor, managed by the
:py:class:~ray.rllib.core.learner.learner_group.LearnerGroup of the Algorithm has an :py:class:~ray.rllib.core.rl_module.rl_module.RLModule copy.
The :py:class:~ray.rllib.env.env_runner.EnvRunner copy is normally in its inference_only version, meaning that components
not required for bare action computation, for example a value function estimate, are missing to save memory.
.. figure:: images/rl_modules/rl_module_in_learner.svg :width: 400 :align: left
**An RLModule inside a Learner actor**: The :py:class:`~ray.rllib.core.learner.learner.Learner` operates on its own copy of
an :py:class:`~ray.rllib.core.rl_module.rl_module.RLModule`, computing the loss function inputs, the loss itself,
and the model's gradients, then updating the :py:class:`~ray.rllib.core.rl_module.rl_module.RLModule`
through the :py:class:`~ray.rllib.core.learner.learner.Learner`'s optimizers.
.. _rllib-key-concepts-episodes:
.. tip::
The following is a quick overview of Episode.
See :ref:here for a detailed description of the Episode classes <single-agent-episode-docs>.
RLlib sends around all training data the form of :ref:Episodes <single-agent-episode-docs>.
The :py:class:~ray.rllib.env.single_agent_episode.SingleAgentEpisode class describes
single-agent trajectories. The :py:class:~ray.rllib.env.multi_agent_episode.MultiAgentEpisode class contains several
such single-agent episodes and describes the stepping times- and patterns of the individual agents with respect to each other.
Both Episode classes store the entire trajectory data generated while stepping through an :ref:RL environment <rllib-key-concepts-environments>.
This data includes the observations, info dicts, actions, rewards, termination signals, and any
model computations along the way, like recurrent states, action logits, or action log probabilities.
.. tip::
See here for RLlib's standardized column names <https://github.com/ray-project/ray/blob/master/rllib/core/columns.py>__.
Note that episodes conveniently don't have to store any ``next obs`` information as it always overlaps
with the information under ``obs``. This design saves almost 50% of memory, because
observations are often the largest piece in a trajectory. The same is true for ``state_in`` and ``state_out``
information for stateful networks. RLlib only keeps the ``state_out`` key in the episodes.
Typically, RLlib generates episode chunks of size config.rollout_fragment_length through the :ref:EnvRunner <rllib-key-concepts-env-runners>
actors in the Algorithm's :ref:EnvRunnerGroup <rllib-key-concepts-env-runners>, and sends as many episode chunks to each
:ref:Learner <rllib-key-concepts-learners> actor as required to build one training batch of exactly size
config.train_batch_size_per_learner.
A typical :py:class:~ray.rllib.env.single_agent_episode.SingleAgentEpisode object roughly looks as follows:
.. code-block:: python
# A SingleAgentEpisode of length 20 has roughly the following schematic structure.
# Note that after these 20 steps, you have 20 actions and rewards, but 21 observations and info dicts
# due to the initial "reset" observation/infos.
episode = {
'obs': np.ndarray((21, 4), dtype=float32), # 21 due to additional reset obs
'infos': [{}, {}, {}, {}, .., {}, {}], # infos are always lists of dicts
'actions': np.ndarray((20,), dtype=int64), # Discrete(4) action space
'rewards': np.ndarray((20,), dtype=float32),
'extra_model_outputs': {
'action_dist_inputs': np.ndarray((20, 4), dtype=float32), # Discrete(4) action space
},
'is_terminated': False, # <- single bool
'is_truncated': True, # <- single bool
}
For complex observations, for example gym.spaces.Dict, the episode holds all observations in a struct entirely analogous
to the observation space, with NumPy arrays at the leafs of that dict. For example:
.. code-block:: python
episode_w_complex_observations = {
'obs': {
"camera": np.ndarray((21, 64, 64, 3), dtype=float32), # RGB images
"sensors": {
"front": np.ndarray((21, 15), dtype=float32), # 1D tensors
"rear": np.ndarray((21, 5), dtype=float32), # another batch of 1D tensors
},
},
...
Because RLlib keeps all values in NumPy arrays, this allows for efficient encoding and transmission across the network.
In multi-agent mode <rllib-concepts.html#policies-in-multi-agent>__, the :py:class:~ray.rllib.env.env_runner_group.EnvRunnerGroup
produces :py:class:~ray.rllib.env.multi_agent_episode.MultiAgentEpisode instances.
.. note::
The Ray team is working on a detailed description of the
:py:class:~ray.rllib.env.multi_agent_episode.MultiAgentEpisode class.
.. _rllib-key-concepts-env-runners:
Given the :ref:RL environment <rllib-key-concepts-environments> and an :ref:RLModule <rllib-key-concepts-rl-modules>,
an :py:class:~ray.rllib.env.env_runner.EnvRunner produces lists of :ref:Episodes <rllib-key-concepts-episodes>.
It does so by executing a classic environment interaction loop. Efficient sample collection can be burdensome to get right, especially when leveraging environment vectorization, stateful recurrent neural networks, or when operating in a multi-agent setting.
RLlib provides two built-in :py:class:~ray.rllib.env.env_runner.EnvRunner classes,
:py:class:~ray.rllib.env.single_agent_env_runner.SingleAgentEnvRunner and
:py:class:~ray.rllib.env.multi_agent_env_runner.MultiAgentEnvRunner that
automatically handle these complexities. RLlib picks the correct type based on your
configuration, in particular the config.environment() and config.multi_agent()
settings.
.. tip::
Call the :py:meth:~ray.rllib.algorithms.algorithm_config.AlgorithmConfig.is_multi_agent
method to find out, whether your config is multi-agent or not.
RLlib bundles several :py:class:~ray.rllib.env.env_runner.EnvRunner actors through the
:py:class:~ray.rllib.env.env_runner_group.EnvRunnerGroup API.
You can also use an :py:class:~ray.rllib.env.env_runner.EnvRunner standalone to produce lists of Episodes by calling its
:py:meth:~ray.rllib.env.env_runner.EnvRunner.sample method.
Here is an example of creating a set of remote :py:class:~ray.rllib.env.env_runner.EnvRunner actors
and using them to gather experiences in parallel:
.. testcode::
import tree # pip install dm_tree
import ray
from ray.rllib.algorithms.ppo import PPOConfig
from ray.rllib.env.single_agent_env_runner import SingleAgentEnvRunner
# Configure the EnvRunners.
config = (
PPOConfig()
.environment("Acrobot-v1")
.env_runners(num_env_runners=2, num_envs_per_env_runner=1)
)
# Create the EnvRunner actors.
env_runners = [
ray.remote(SingleAgentEnvRunner).remote(config=config)
for _ in range(config.num_env_runners)
]
# Gather lists of `SingleAgentEpisode`s (each EnvRunner actor returns one
# such list with exactly two episodes in it).
episodes = ray.get([
er.sample.remote(num_episodes=3)
for er in env_runners
])
# Two remote EnvRunners used.
assert len(episodes) == 2
# Each EnvRunner returns three episodes
assert all(len(eps_list) == 3 for eps_list in episodes)
# Report the returns of all episodes collected
for episode in tree.flatten(episodes):
print("R=", episode.get_return())
.. testcode:: :hide:
for er in env_runners:
er.stop.remote()
.. _rllib-key-concepts-learners:
.. tip::
The following is a quick overview of RLlib Learners.
See :ref:here for a detailed description of the Learner class <learner-guide>.
Given the :ref:RLModule <rllib-key-concepts-rl-modules> and one or more optimizers and loss functions,
a :py:class:~ray.rllib.core.learner.learner.Learner computes losses and gradients, then updates the :py:class:~ray.rllib.core.rl_module.rl_module.RLModule.
The input data for such an update step comes in as a list of :ref:episodes <rllib-key-concepts-episodes>,
which either the Learner's own connector pipeline or an external one converts into the final train batch.
.. note::
:py:class:~ray.rllib.connectors.connector_v2.ConnectorV2 documentation is work in progress.
The Ray team links to the correct documentation page here, once it has completed this work.
:py:class:~ray.rllib.core.learner.learner.Learner instances are algorithm-specific, mostly due to the various
loss functions used by different RL algorithms.
RLlib always bundles several :py:class:~ray.rllib.core.learner.learner.Learner actors through
the :py:class:~ray.rllib.core.learner.learner_group.LearnerGroup API, automatically applying
distributed data parallelism (DDP) on the training data.
You can also use a :py:class:~ray.rllib.core.learner.learner.Learner standalone to update your RLModule
with a list of Episodes.
Here is an example of creating a remote :py:class:~ray.rllib.core.learner.learner.Learner
actor and calling its :py:meth:~ray.rllib.core.learner.learner.Learner.update method.
.. testcode::
import gymnasium as gym
import ray
from ray.rllib.algorithms.ppo import PPOConfig
from ray.rllib.core.rl_module.default_model_config import DefaultModelConfig
# Configure the Learner.
config = (
PPOConfig()
.environment("Acrobot-v1")
.training(lr=0.0001)
.rl_module(model_config=DefaultModelConfig(fcnet_hiddens=[64, 32]))
)
# Get the Learner class.
ppo_learner_class = config.get_default_learner_class()
# Create the Learner actor.
learner_actor = ray.remote(ppo_learner_class).remote(
config=config,
module_spec=config.get_multi_rl_module_spec(env=gym.make("Acrobot-v1")),
)
# Build the Learner.
ray.get(learner_actor.build.remote())
# Perform an update from the list of episodes we got from the `EnvRunners` above.
learner_results = ray.get(learner_actor.update.remote(
episodes=tree.flatten(episodes)
))
print(learner_results["default_policy"]["policy_loss"])