Introduction to Brax
Brax is a cutting-edge physics engine designed for high-speed simulations and is gaining recognition in fields such as robotics, human perception, materials science, and reinforcement learning. It is ideal for tasks that demand intensive computational simulations. The engine is developed in JAX, making it highly efficient when running on accelerated hardware. A remarkable advantage of using Brax is its scalability, allowing simulations to perform seamlessly across multiple devices, thereby eliminating the need for extensive data center resources.
Key Features of Brax
Brax is capable of simulating environments at astounding speeds, clocking millions of physics steps per second when operated on TPU (Tensor Processing Unit). It comes equipped with a suite of learning algorithms that enable the training of models within minutes. The baseline learning algorithms include:
- PPO (Proximal Policy Optimization)
- SAC (Soft Actor-Critic)
- ARS (Augmented Random Search)
- Evolutionary Strategies
Additionally, Brax also supports learning algorithms that take full advantage of its differentiability, like analytic policy gradients.
One API, Four Pipelines
A standout feature of Brax is its offering of four distinct physics pipelines. These pipelines use a single API, making it easy to swap between them, an advantage for transfer learning and experiments aiming to bridge simulation with real-world dynamics. The pipelines include:
- MuJoCo XLA - MJX: A JAX-based reimplementation of the MuJoCo physics engine.
- Generalized: This computes motion using generalized coordinates similar to MuJoCo and TDS (Tiny Differentiable Simulator).
- Positional: Uses Position Based Dynamics, which is effective for handling joint and collision constraints.
- Spring: A swift and cost-efficient method suitable for rapid experimentation, commonly found in video games.
Quickstart with Colab
Brax provides numerous Colab notebooks for users to easily explore and experiment with the engine. These include:
- Brax Basics: A primer on the Brax API and basic physics simulations.
- Brax Training: An introduction to training algorithms where users can train policies and manage models within Colab.
- Brax Training with MuJoCo XLA - MJX: Demonstrates training using the MJX physics simulator.
- Brax Training with PyTorch on GPU: Illustrates how Brax integrates with other machine learning frameworks, such as PyTorch, for fast training on GPU.
Installing and Using Brax Locally
It is straightforward to install Brax via PyPI, Conda, or Mamba. The installation steps are as follows:
To install from PyPI:
python3 -m venv env
source env/bin/activate
pip install --upgrade pip
pip install brax
For installation via Conda or Mamba:
conda install -c conda-forge brax # replace 'conda' with 'mamba' for mamba
Installing from source involves cloning the repository and setting up the environment:
git clone [Brax GitHub Repository]
cd brax
python3 -m venv env
source env/bin/activate
pip install --upgrade pip
pip install -e .
To utilize Brax's full training capabilities on NVIDIA GPUs, ensure CUDA and CuDNN are properly installed along with JAX GPU support.
Further Learning
For those interested in a deeper understanding of Brax's design and its capabilities, the paper titled "Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulation" presented at NeurIPS 2021 is an excellent resource.
Acknowledgements and Collaboration
Brax's development has been a collaborative effort with contributions from many experts and open-source enthusiasts. Their collective efforts have been instrumental in refining Brax's functionalities and making it a versatile tool for modern physics simulations.