RLlib: Industry-Grade, Scalable Reinforcement Learning#

Note

Ray 2.10.0 introduces the alpha stage of RLlib’s “new API stack”. 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.

See here for more details on how to activate and use the new API stack.

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.

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.

Industry leaders use RLlib in production in many different verticals, such as gaming, robotics, finance, climate- and industrial control, manufacturing and logistics, automobile, and boat design.

RLlib in 60 seconds#

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:

pip install "ray[rllib]" torch

Note

For installation on computers running Apple Silicon, such as M1, follow instructions here.

Note

To be able to run the Atari or MuJoCo examples, you also need to do: 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 and any other needed settings and parameters.

Next, build the algorithm and train it for a total of five iterations. One training iteration includes parallel, distributed sample collection by the EnvRunner actors, followed by loss calculation on the collected data, and a model update step.

At the end of your script, RLlib evaluates the trained Algorithm:

from ray.rllib.algorithms.ppo import PPOConfig
from ray.rllib.connectors.env_to_module import FlattenObservations

# 1. Configure the algorithm,
config = (
    PPOConfig()
    .environment("Taxi-v3")
    .env_runners(
        num_env_runners=2,
        # Observations are discrete (ints) -> We need to flatten (one-hot) them.
        env_to_module_connector=lambda env: FlattenObservations(),
    )
    .evaluation(evaluation_num_env_runners=1)
)
# 2. build the algorithm ..
algo = config.build()
# 3. .. train it ..
for _ in range(5):
    print(algo.train())
# 4. .. and evaluate it.
algo.evaluate()

You can use any Farama-Foundation Gymnasium registered environment with the env argument.

In config.env_runners() you can specify - amongst many other things - the number of parallel EnvRunner actors to collect samples from the environment.

You can also tweak the NN architecture used by tweaking RLlib’s DefaultModelConfig, as well as, set up a separate config for the evaluation 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 the dropdowns below:

Why chose RLlib?#

Learn More#

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.

Environments

RLlib Key Concepts

Learn more about the core concepts of RLlib, such as environments, algorithms and policies.

Key Concepts

RLlib Algorithms

See the many available RL algorithms of RLlib for model-free and model-based RL, on-policy and off-policy training, multi-agent RL, and more.

Algorithms

Customizing RLlib#

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 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.

../_images/rllib-new-api-stack-simple.svg — **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. Users customize their experiments by subclassing the existing abstractions.#

Citing RLlib#

If RLlib helps with your academic research, the Ray RLlib team encourages you to cite these papers:

@inproceedings{liang2021rllib,
    title={{RLlib} Flow: Distributed Reinforcement Learning is a Dataflow Problem},
    author={
        Wu, Zhanghao and
        Liang, Eric and
        Luo, Michael and
        Mika, Sven and
        Gonzalez, Joseph E. and
        Stoica, Ion
    },
    booktitle={Conference on Neural Information Processing Systems ({NeurIPS})},
    year={2021},
    url={https://proceedings.neurips.cc/paper/2021/file/2bce32ed409f5ebcee2a7b417ad9beed-Paper.pdf}
}

@inproceedings{liang2018rllib,
    title={{RLlib}: Abstractions for Distributed Reinforcement Learning},
    author={
        Eric Liang and
        Richard Liaw and
        Robert Nishihara and
        Philipp Moritz and
        Roy Fox and
        Ken Goldberg and
        Joseph E. Gonzalez and
        Michael I. Jordan and
        Ion Stoica,
    },
    booktitle = {International Conference on Machine Learning ({ICML})},
    year={2018},
    url={https://arxiv.org/pdf/1712.09381}
}

On-Policy
PPO (Proximal Policy Optimization)
Off-Policy
SAC (Soft Actor Critic)
DQN/Rainbow (Deep Q Networks)
High-throughput Architectures
APPO (Asynchronous Proximal Policy Optimization)
IMPALA (Importance Weighted Actor-Learner Architecture)
Model-based RL
DreamerV3
Offline RL and Imitation Learning
BC (Behavior Cloning)
CQL (Conservative Q-Learning)
MARWIL (Advantage Re-Weighted Imitation Learning)