Status: Maintenance (expect bug fixes and minor updates)

MuJoCo is a physics engine for detailed, efficient rigid body simulations with contacts. mujoco-py allows using MuJoCo from Python 3. This library has been updated to be compatible with MuJoCo version 2.0 released on 10/1/2018.

It has been used in many well-known research projects, such as:

• gym RL platform created by OpenAI;
• ppdm a In-hand manipulations algrithom created by Google Brain;
• DeepMind's dm_control and rodent control;
• Mujoco Haptix , a glove-based system only runs on Windows, mujoco150 up to now.

• download mjc200 from https://www.roboti.us/download.html
• Everyone key: https://www.roboti.us/file/mjkey.txt
• clone this repo
• Activate CMD and set env with os:

activate XXX
cd mujoco-py
pip install -e .
python
>>> import os
>>> os.environ["MUJOCO_PY_MUJOCO_PATH"] = r"D:\zzm_codes\codes2023\mujoco200_win64" # abs dir
>>> os.environ["MUJOCO_PY_MJKEY_PATH"] = os.environ["MUJOCO_PY_MUJOCO_PATH"] + "/mjkey.txt"
>>> mjcpy_bin = os.environ["MUJOCO_PY_MUJOCO_PATH"]+"\\bin"
>>> os.environ["PATH"] += ";" + mjcpy_bin
>>> os.add_dll_directory(mjcpy_bin) # for mujoco_py in py38
>>> import gym # 0.21.0
>>> env=gym.make('HandManipulatePenTouchSensorsDense-v0')
>>> exit()

Following the issues#504, this repo adapt the latest version of mujoco python wrapper to the windows environment correctly. The following demo is the gym env env = gym.make('HandManipulatePenTouchSensorsDense-v0').

One should firstly apply a valid mujoco license in the license page and received a file called mjkey.txt.Then you could download this C/C++ API in the index page. After all these works, you could navigate to mujoco\mujoco200\bin directory with the mjkey.txt copied in, and run the commands like：

1. simulate 仿真xml定义的场景 Usage: use glfw to view the results:

simulate ../model/humanoid.xml
simulate ../model/hello.xml

2. compile 模型转换 Usage: compile infile outfile

infile can be in mjcf, urdf, mjb format
outfile can be in mjcf, mjb, txt format

This is the base usage of Mujoco. For dive-into demo, please check the \sample directory.

Here is my specific settings table.

After the two above-mentioned Settings, you can create a anaconda environment with these configurations:

• Windows 10
• Anaconda Python 3.7.6
• Mujoco200

And what will be created by this repo is:

• mujoco-py 2.0.2.9

conda create -n gym37 python=3.7
git clone https://gitee.com/TneitaP/mujoco-py.git
cd mujoco-py
activate gym37
pip install -e .
python
>> import mujoco_py as mjc # for generation
>> exit()
pip install gym

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

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