Welcome to the PyTorch implementation of the Augmented Random Search (ARS) algorithm for Brax!
Brax is a differentiable physics engine that simulates environments made up of rigid bodies, joints, and actuators; which optimized for use on acceleration hardware, allowing for both efficient single-device simulation and scalable, massively parallel simulation on multiple devices.
This repository contains the PyTorch implementation of the ARS algorithm, which can be used to train policies for controlling the behavior of objects in a Brax simulated environment. The ARS algorithm is a model-free, gradient-free optimization method that is effective at learning control policies in high-dimensional continuous action spaces.
I hope you find this implementation useful for learning control policies in Brax environments! If you have any questions or suggestions, please don't hesitate to open an issue or submit a pull request.