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brax-0.9.0
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مشاهده بیشتر
تبلیغات ما
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مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
A differentiable physics engine written in JAX.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | brax-0.9.0 |
| نام | brax |
| نسخه کتابخانه | 0.9.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Brax Authors |
| ایمیل نویسنده | no-reply@google.com |
| آدرس صفحه اصلی | http://github.com/google/brax |
| آدرس اینترنتی | https://pypi.org/project/brax/ |
| مجوز | Apache 2.0 |
<img src="https://github.com/google/brax/raw/main/docs/img/brax_logo.gif" width="336" height="80" alt="BRAX"/>
Brax is a fast and fully differentiable physics engine used for research and
development of robotics, human perception, materials science, reinforcement
learning, and other simulation-heavy applications.
Brax is written in [JAX](https://github.com/google/jax) and is designed for use
on acceleration hardware. It is both efficient for single-device simulation, and
scalable to massively parallel simulation on multiple devices, without the need
for pesky datacenters.
<img src="https://github.com/google/brax/raw/main/docs/img/humanoid_v2.gif" width="160" height="160"/><img src="https://github.com/google/brax/raw/main/docs/img/a1.gif" width="160" height="160"/><img src="https://github.com/google/brax/raw/main/docs/img/ant_v2.gif" width="160" height="160"/><img src="https://github.com/google/brax/raw/main/docs/img/ur5e.gif" width="160" height="160"/>
Brax simulates environments at millions of physics steps per second on TPU, and includes a suite of learning algorithms that train agents in seconds
to minutes:
* Baseline learning algorithms such as
[PPO](https://github.com/google/brax/blob/main/brax/training/agents/ppo),
[SAC](https://github.com/google/brax/blob/main/brax/training/agents/sac),
[ARS](https://github.com/google/brax/blob/main/brax/training/agents/ars), and
[evolutionary strategies](https://github.com/google/brax/blob/main/brax/training/agents/es).
* Learning algorithms that leverage the differentiability of the simulator, such as [analytic policy gradients](https://github.com/google/brax/blob/main/brax/training/agents/apg).
## One API, Three Pipelines
Brax offers three distinct physics pipelines that are easy to swap:
* [Generalized](https://github.com/google/brax/blob/main/brax/v2/generalized/)
calculates motion in [generalized coordinates](https://en.wikipedia.org/wiki/Generalized_coordinates) using the same accurate robot
dynamics algorithms as [MuJoCo](https://mujoco.org/) and [TDS](https://github.com/erwincoumans/tiny-differentiable-simulator).
* [Positional](https://github.com/google/brax/blob/main/brax/v2/positional/)
uses [Position Based Dynamics](https://matthias-research.github.io/pages/publications/posBasedDyn.pdf),
a fast but stable method of resolving joint and collision constraints.
* [Spring](https://github.com/google/brax/blob/main/brax/v2/spring/) provides
fast and cheap simulation for rapid experimentation, using simple impulse-based
methods often found in video games.
These pipelines share the same API and can run side-by-side within the same
simulation. This makes Brax well suited for experiments in transfer learning
and closing the gap between simulation and the real world.
## Quickstart: Colab in the Cloud
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 the Brax v2 API, and shows how to train a policy with the
generalized backend.
## Using Brax Locally
To install Brax from pypi, install it with:
```
python3 -m venv env
source env/bin/activate
pip install --upgrade pip
pip install brax
```
You may also install from [Conda](https://docs.conda.io/en/latest/) or [Mamba](https://github.com/mamba-org/mamba):
```
conda install -c conda-forge brax # s/conda/mamba for mamba
```
Alternatively, 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).
## Learn More
For a deep dive into Brax's design and performance characteristics, please see
our paper, [Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulation
](https://arxiv.org/abs/2106.13281), which appeared in the [Datasets and Benchmarks Track](https://neurips.cc/Conferences/2021/CallForDatasetsBenchmarks) at [NeurIPS 2021](https://nips.cc/Conferences/2021).
## 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 = {0.9.0},
year = {2021},
}
```
## Acknowledgements
Brax has come a long way since its original publication. We offer gratitude and
effusive praise to the following people:
* Manu Orsini and Nikola Momchev who provided a major refactor of Brax's
training algorithms to make them more accessible and reusable.
* Erwin Coumans who has graciously offered advice and mentorship, and many
useful references from [Tiny Differentiable Simulator](https://github.com/erwincoumans/tiny-differentiable-simulator).
* Baruch Tabanpour, a colleague who helped launch brax v2 and overhauled the contact library.
* [Shixiang Shane Gu](https://sites.google.com/corp/view/gugurus) and [Hiroki Furuta](https://frt03.github.io/), who contributed BIG-Gym and Braxlines, and a scene composer to Brax.
* Our awesome [open source collaborators and contributors](https://github.com/google/brax/graphs/contributors). Thank you!
نیازمندی
| مقدار | نام |
|---|---|
| - | absl-py |
| - | dm-env |
| - | etils |
| - | flask |
| - | flask-cors |
| - | flax |
| - | grpcio |
| - | gym |
| >=0.4.6 | jax |
| >=0.4.6 | jaxlib |
| - | jaxopt |
| - | jinja2 |
| - | mujoco |
| - | numpy |
| - | optax |
| - | Pillow |
| - | pytinyrenderer |
| - | scipy |
| - | tensorboardX |
| ==3.9.35 | trimesh |
| - | typing-extensions |
| - | dataclasses |
| - | pytest |
| - | transforms3d |
نحوه نصب
نصب پکیج whl brax-0.9.0:
pip install brax-0.9.0.whl
نصب پکیج tar.gz brax-0.9.0:
pip install brax-0.9.0.tar.gz