Modular Reinforcement Learning (RL) library (implemented in PyTorch, JAX, and NVIDIA Warp) with support for Gymnasium/Gym, NVIDIA Isaac Lab, MuJoCo Playground and other environments

🦁 A research-friendly codebase for fast experimentation of multi-agent reinforcement learning in JAX

🕹️ A diverse suite of scalable reinforcement learning environments in JAX

Multi-Agent Reinforcement Learning with JAX

The purpose of this repo is to make it easy to get started with JAX, Flax, and Haiku. It contains my "Machine Learning with JAX" series of tutorials (YouTube videos and Jupyter Notebooks) as well as the content I found useful while learning about the JAX ecosystem.

JAX (Flax) implementation of algorithms for Deep Reinforcement Learning with continuous action spaces.

Physics Informed Machine Learning Tutorials (Pytorch and Jax)

Pax is a Jax-based machine learning framework for training large scale models. Pax allows for advanced and fully configurable experimentation and parallelization, and has demonstrated industry leading model flop utilization rates.

A High Level API for Deep Learning in JAX

Official JAX implementation of Learning to (Learn at Test Time): RNNs with Expressive Hidden States

🏛️A research-friendly codebase for fast experimentation of single-agent reinforcement learning in JAX • End-to-End JAX RL

Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R

Rax is a Learning-to-Rank library written in JAX.

JAX-accelerated Meta-Reinforcement Learning Environments Inspired by XLand and MiniGrid 🏎️

Oryx is a library for probabilistic programming and deep learning built on top of Jax.

Creative Machine Learning course and notebook tutorials in JAX, PyTorch and Numpy

EvoRL is a fully GPU-accelerated framework for Evolutionary Reinforcement Learning, implemented with JAX. It supports Reinforcement Learning (RL), Evolutionary Computation (EC), Evolution-guided Reinforcement Learning (ERL), AutoRL, and seamless integration with GPU-optimized simulation environments.

FedJAX is a JAX-based open source library for Federated Learning simulations that emphasizes ease-of-use in research.

Pretrained deep learning models for Jax/Flax: StyleGAN2, GPT2, VGG, ResNet, etc.

Hardware-Accelerated Reinforcement Learning Algorithms in pure Jax!

Slide decks, coding exercises, and quick references for learning the JAX AI Stack

Online Goal-Conditioned Reinforcement Learning in JAX. ICLR 2025 Spotlight.

Reinforcement learning on general 2D physics environments in JAX. ICLR 2025 Oral.

Crash course to master gradient-based machine learning. Also secretly a JAX course in disguise!

Named Tensors for Legible Deep Learning in JAX

A Pytree Module system for Deep Learning in JAX

Implementation of Model-Agnostic Meta-Learning (MAML) in Jax

Concise deep learning for JAX

A pure-functional implementation of a machine learning transformer model in Python/JAX

Notebooks for the "Deep Learning with JAX" book