What is Ray RLlib?
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.
RLlib is used by industry leaders in many different verticals, such as climate control, industrial control, manufacturing and logistics, finance, gaming, automobile, robotics, boat design, and many others.
Benefits
Easy Pythonic API
Get up and running quickly with Ray RLlib’s easy-to-use Pythonic APIs. RLlib provides simple configurations and classes to customize all aspects of your training- and experimental workflows.
Complex, Multi-Agent Use Cases
With RLlib, get support for self play and dynamically add and remove policies as needed. Agents have access to all other agents' information for training shared NN components, but can also function completely independently based on your needs and configurations.
Modular Algorithms
Ray RLlib offers modular algorithms, for model-free and model-based RL, on- and off-policy training, multi-agent RL, offline RL, and more.
Advanced Architectures & Environments
Get started with environments supported by RLlib, such as Farama foundation’s Gymnasium, PettingZoo, and many custom APIs for vectorized and multi-agent environments.
A Library that Scales with Your Needs
RLlib is the most scalable reinforcement learning platform. Scale by adding environment workers, or by training your model on more compute power.
Easy Pythonic API
Get up and running quickly with Ray RLlib’s easy-to-use Pythonic APIs. RLlib provides simple configurations and classes to customize all aspects of your training- and experimental workflows.
Complex, Multi-Agent Use Cases
With RLlib, get support for self play and dynamically add and remove policies as needed. Agents have access to all other agents' information for training shared NN components, but can also function completely independently based on your needs and configurations.
Modular Algorithms
Ray RLlib offers modular algorithms, for model-free and model-based RL, on- and off-policy training, multi-agent RL, offline RL, and more.
Advanced Architectures & Environments
Get started with environments supported by RLlib, such as Farama foundation’s Gymnasium, PettingZoo, and many custom APIs for vectorized and multi-agent environments.
A Library that Scales with Your Needs
RLlib is the most scalable reinforcement learning platform. Scale by adding environment workers, or by training your model on more compute power.
Feature Comparison
Custom Models (PyTorch)
Stable Baseline3
Vector Environments for Multiprocessing
Stable Baseline3
Scalable Environment Runners
Stable Baseline3
Multi-Node/Multi-GPU Training
Stable Baseline3
Offline RL and Behavior Cloning
Stable Baseline3
Multi-Agent Support
Including Independent, Collaborative, and Adversarial
Stable Baseline3
Multi-Model Support
Including Curiosity, Shared Value Functions, and more
Stable Baseline3
Model-Based Reinforcement Learning
Stable Baseline3
Stable Baseline3 | | | |
|---|---|---|---|
Custom Models (PyTorch) | Stable Baseline3 | | |
Vector Environments for Multiprocessing | Stable Baseline3Limited | | |
Scalable Environment Runners | Stable Baseline3Limited | | |
Multi-Node/Multi-GPU Training | Stable Baseline3Limited | – | |
Offline RL and Behavior Cloning | Stable Baseline3– | | |
Multi-Agent SupportIncluding Independent, Collaborative, and Adversarial | Stable Baseline3– | | |
Multi-Model SupportIncluding Curiosity, Shared Value Functions, and more | Stable Baseline3– | | |
Model-Based Reinforcement Learning | Stable Baseline3– | – | |
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