MuJoCo (Multi-Joint dynamics with Contact) is a proprietary physics engine for detailed, efficient rigid body simulations with contacts. MuJoCo can be used to create environments with continuous control tasks such as walking or running. Thus, many policy gradient methods (TRPO, PPO) have been tested on various MuJoCo environments.
We list various reinforcement learning algorithms that were tested with MuJoCo. These results are from RL Database. If this page was helpful, please consider giving a star!
There are many papers that have experimented with the MuJoCo continuous control environment, but most papers decided not include exact scores and instead used performance curves. If you know other papers that report results on the MuJoCo environment, please email me!
To install the MuJoCo environment, you need the OpenAI Gym toolkit. Read this page to learn how to install OpenAI Gym.
You also need to purchase MuJoCo license. MuJoCo offers a 30-day trial license for everyone, and a free license for students using MuJoCo for personal projects only. Visit their license page for more information.
Install MuJoCo binary
- Download the MuJoCo version 1.50 binaries for Linux, OSX, or Windows.
- Unzip the downloaded
~/.mujoco/mjpro150, and place your license key (the
mjkey.txtfile from your email) at
Install MuJoCo package
If you did a full install of OpenAI Gym, the MuJoCo package should already be installed. Otherwise, you can install the MuJoCo environments with a single
pip3 install gym[mujoco]
You can try rendering the
Humanoid-v2 environment to make sure the MuJoCo environment was correctly installed.
import gym env = gym.make('Humanoid-v2') env.reset() env.render()