Distributed GPU-Accelerated Framework for Evolutionary Computation. Comprehensive Library of Evolutionary Algorithms & Benchmark Problems.

GPU-accelerated NeuroEvolution of Augmenting Topologies (NEAT)

The Simple Simulator: Simulation Made Simple

Walkthroughs for DSL, AirSim, the Vector Institute, and more

MetaDE is a GPU-accelerated evolutionary framework that optimizes Differential Evolution (DE) strategies via meta-level evolution. Supporting both JAX and PyTorch, it dynamically adapts mutation and crossover strategies for efficient large-scale black-box optimization.

a modular reinforcement learning library with JAX agents

GPU-accelerated Evolutionary Multiobjective Optimization Using Tensorized RVEA.

💡 Grasp - Pick-and-place with a robotic hand 👨🏻‍💻

An implementation of Short Horizon Actor Critic writen in Jax. Core algorithm written in the style of Brax, with several bits taken from Xu's original paper.

Reinforcement learning in pure JAX.

Simple implementations of Cartesian Genetic Programming (CGP) and Linear Genetic Programming (LGP) in JAX

Relentlessly learning, persistently failing, but never surrendering.

[ICML 2024] Official environments and JAX-implementations for "Subequivariant Reinforcement Learning in 3D Multi-Entity Physical Environments"

Procedural Environment Generation for Accelerated Multi-Agent Reinforcement Learning

Learning to Generalize towards Unseen Domains via a Content-Aware Style Invariant Framework for Disease Detection from Chest X-rays