Technology Landscape
Reinforcement Learning is supported by a wide ecosystem of tools and libraries across academia and industry.
library
Stable-Baselines3
A popular library for RL algorithms implemented in PyTorch. Simple, modular, and used for fast experimentation.
library
Ray RLlib
Scalable RL framework by Anyscale built on Ray. Supports multi-agent training, distributed compute, and custom environments.
library
CleanRL
High-quality, single-file implementations of RL algorithms. Great for learning and reproducible experiments.
library
Acme
A framework from DeepMind for building and training RL agents with research-level flexibility.