Metadata-Version: 2.1
Name: brax
Version: 0.0.2
Summary: A differentiable physics engine written in JAX.
Home-page: http://github.com/google/brax
Author: Brax Authors
Author-email: no-reply@google.com
License: Apache 2.0
Description: # `BRAX`
        
        Brax is a differentiable physics engine that simulates environments made up
        of rigid bodies, joints, and actuators. It's also a suite of learning algorithms
        to train agents to operate in these environments (PPO, SAC, evolutionary
        strategy, and direct trajectory optimization are implemented).
        
        Brax is written in [JAX](https://github.com/google/jax) and is designed for
        use on acceleration hardware. It is both efficient for single-core training, and
        scalable to massively parallel simulation, without the need for pesky
        datacenters.
        
        <img src="https://github.com/google/brax/raw/main/docs/img/ant.gif" width="150" height="107"/><img src="https://github.com/google/brax/raw/main/docs/img/fetch.gif" width="150" height="107"/><img src="https://github.com/google/brax/raw/main/docs/img/grasp.gif" width="150" height="107"/><img src="https://github.com/google/brax/raw/main/docs/img/halfcheetah.gif" width="150" height="107"/><img src="https://github.com/google/brax/raw/main/docs/img/humanoid.gif" width="150" height="107"/>
        
        *Some policies trained via Brax. Brax simulates these environments at millions of physics steps per second on TPU.*
        
        ## Colab Notebooks
        
        Explore Brax easily and quickly through a series of colab notebooks:
        
        * [Brax Basics](https://colab.research.google.com/github/google/brax/blob/main/notebooks/basics.ipynb) introduces the Brax API, and shows how to simulate basic physics primitives.
        * [Brax Training](https://colab.research.google.com/github/google/brax/blob/main/notebooks/training.ipynb) introduces Brax environments and training algorithms, and lets you train your own policies directly within the colab.
        
        ## Using Brax locally
        
        To install Brax from source, clone this repo, `cd` to it, and then:
        
        ```
        python3 -m venv env
        source env/bin/activate
        pip install --upgrade pip
        pip install -e .
        ```
        
        To train a model:
        
        ```
        learn
        ```
        
        Training on NVidia GPU is supported, but you must first install [CUDA, CuDNN,
        and JAX with GPU support](https://github.com/google/jax#installation).
        
        ## Citing Brax
        
        If you would like to reference Brax in a publication, please use:
        
        ```
        @software{brax2021github,
          author = {C. Daniel Freeman and Erik Frey and Anton Raichuk and Sertan Girgin and Igor Mordatch and Olivier Bachem},
          title = {Brax - A Differentiable Physics Engine for Large Scale Rigid Body Simulation},
          url = {http://github.com/google/brax},
          version = {0.1.0},
          year = {2021},
        }
        ```
        
Keywords: JAX reinforcement learning rigidbody physics
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Description-Content-Type: text/markdown
