starimpact / mujoco_py

this can work in ubuntu16.04, python3.5.2, cpu mode only. with mjpro150 libraries.

mujoco-py

MuJoCo is a physics engine for detailed, efficient rigid body simulations with contacts. mujoco-py allows using MuJoCo from Python 3.

Synopsis

Requirements

The following platforms are currently supported:

• Linux with Python 3.5.2. See the Dockerfile for the canonical list of system dependencies. Support for Python 3.6 is planned.
• OS X with Python 3.5.2. Support for Python 3.6 is planned.
• Windows (experimental) with Python 3.5.2. See the Appveyor file for the canonical list of dependencies.

Python 2 has been desupported since 1.50.1.0. Python 2 users can stay on the 0.5 branch. The latest release there is 0.5.7 which can be installed with pip install mujoco-py==0.5.7.

Install MuJoCo

1. Obtain a 30-day free trial on the MuJoCo website or free license if you are a student. The license key will arrive in an email with your username and password.
2. Download the MuJoCo version 1.50 binaries for Linux, OSX, or Windows.
3. Unzip the downloaded mjpro150 directory into ~/.mujoco/mjpro150, and place your license key (the mjkey.txt file from your email) at ~/.mujoco/mjkey.txt.

Install and use mujoco-py

To include mujoco-py in your own package, add it to your requirements like so:

mujoco-py<1.50.2,>=1.50.1

To play with mujoco-py interactively, follow these steps:

$ pip3 install -U 'mujoco-py<1.50.2,>=1.50.1'
$ python3
import mujoco_py
from os.path import dirname
model = mujoco_py.load_model_from_path(dirname(dirname(mujoco_py.__file__))  +"/xmls/claw.xml")
sim = mujoco_py.MjSim(model)

print(sim.data.qpos)
# [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]

sim.step()
print(sim.data.qpos)
# [  2.09217903e-06  -1.82329050e-12  -1.16711384e-07  -4.69613872e-11
#   -1.43931860e-05   4.73350204e-10  -3.23749942e-05  -1.19854057e-13
#   -2.39251380e-08  -4.46750545e-07   1.78771599e-09  -1.04232280e-08]

See the full documentation for advanced usage.

Missing GLFW

A common error when installing is:

raise ImportError("Failed to load GLFW3 shared library.")

Which happens when the glfw python package fails to find a GLFW dynamic library.

MuJoCo ships with its own copy of this library, which can be used during installation.

Add the path to the mujoco bin directory to your dynamic loader:

LD_LIBRARY_PATH=$HOME/.mujoco/mjpro150/bin pip install mujoco-py

This is particularly useful on Ubuntu 14.04, which does not have a GLFW package.

Usage Examples

A number of examples demonstrating some advanced features of mujoco-py can be found in examples/. These include:

• body_interaction.py: shows interactions between colliding bodies
• disco_fetch.py: shows how TextureModder can be used to randomize object textures
• internal_functions.py: shows how to call raw mujoco functions like mjv_room2model
• markers_demo.py: shows how to add visualization-only geoms to the viewer
• serialize_model.py: shows how to save and restore a model
• setting_state.py: shows how to reset the simulation to a given state
• tosser.py: shows a simple actuated object sorting robot application

See the full documentation for advanced usage.

Development

To run the provided unit and integrations tests:

make test

To test GPU-backed rendering, run:

make test_gpu

This is somewhat dependent on internal OpenAI infrastructure at the moment, but it should run if you change the Makefile parameters for your own setup.

Changelog

• 03/08/2018: We removed MjSimPool, because most of benefit one can get with multiple processes having single simulation.

Credits

mujoco-py is maintained by the OpenAI Robotics team. Contributors include:

• Alex Ray
• Bob McGrew
• Jonas Schneider
• Jonathan Ho
• Peter Welinder
• Wojciech Zaremba

Details in Intallation of Mujoco

by starimpact

1. ubuntu建议是16.04版本，或macos，其它系统我没试过。
2. 从mujoco官网下载mjpro150，放到当前用户目录下：~/.mujoco，没有这个目录就自己建一个。解压mjpro150。
3. 从mujoco官网申请一个30天的试用key。放到~/.mujoco下面。注：国内邮箱收不到验证邮箱，至少我的126是没收到。
4. 进入mjpro150中的sample文件夹，make一下，如果成功，说明没什么大问题了。可以到bin目录下运行：LD_LIBRARY_PATH=./ ./simulate，如果有窗口显示说明就正常了。注意将.mujoco下的mjkey.txt 复制一份到bin下。
5. 从starimpact的github下载https://github.com/starimpact/mujoco_py.git。从这里下载是因为这是一个坑已经填好的版本。
6. 安装一个virutalenv的工具，后面的所有python相关操作都是在虚拟环境中进行，防止把系统的搞坏了。这玩意也挺好用。
7. apt-get install一个python3.5或python3.6的版本，然后用virtualenv --system-site-packages -p python3.5 [your virtual directory]安装虚拟环境。
8. 用source [your virtual directory]/bin/activate 进入虚拟环境。
9. 建一个~/.pip/pip.conf文件夹，写入：

   [global]
   index-url = https://pypi.tuna.tsinghua.edu.cn/simple
   

   这是用pip清华源，速度会快很多，比官方源快几十倍的样子。
10. 进入clone下来的mujoco_py文件夹，运行pip install -e . [注意后面有一个点]。
11. 一般情况下装到mujoco包时会等个十来分钟，如果超过这个时间，说明下载的速度出现了问题。这时候要用nload工具查看当前的下载速度，如果一直是在几kb，那么基本上永远都装不好了。这时候最好停掉，再重新安装，多试几次，直到看到下载速度在100kb左右时才有希望。
12. 上面一步完成后，进入python环境，然后运行导入命令import mujoco_py，这时个的mujoco会进行一些编译的工作。如果编译失败，说明缺少一些库文件，只需要按照提示安装好就行了。
13. mujoco_py导入成功后，那么后面问题就少了。进mujoco_py文件夹，会看到一个do.sh的文件，运行它，就可以看到效果。
14. 说一说do.sh里的变量的作用是什么：
    • LD_LIBRARY_PATH=/home/mingzhang/.mujoco/mjpro150/bin —> 将之前安装好的mjpro150的环境加到库搜索环境变量里。
    • MUJOCO_PY_FORCE_CPU=1 —> 指定mujoco只在cpu上运行。这一步很重要。
15. 完成。

注意：以上就是安装使用mujoco的关键步骤。中间会遇到的其它问题都是安装依赖库的问题，请自行google解决。不能google，自行翻墙，哈哈……