mj_envs is a collection of environments/tasks simulated with the Mujoco physics engine and wrapped in the OpenAI gym API.

mj_envs uses git submodules to resolve dependencies. Please follow steps exactly as below to install correctly.

1. Clone this repo with pre-populated submodule dependencies

$ git clone --recursive https://github.com/vikashplus/mj_envs.git

2. Install package using pip

$ pip install -e .

OR Add repo to pythonpath by updating ~/.bashrc or ~/.bash_profile

export PYTHONPATH="<path/to/mj_envs>:$PYTHONPATH"

3. You can visualize the environments with random controls using the below command

$ python utils/visualize_env.py --env_name hammer-v0

NOTE: If the visualization results in a GLFW error, this is because mujoco-py does not see some graphics drivers correctly. This can usually be fixed by explicitly loading the correct drivers before running the python script. See this page for details.

mj_envs contains a variety of environements, which are organized as modules. Each module is a collection of loosely related environements. Following modules are provided at the moment with plans to improve the diversity of the collection.

HMS contains a collection of environements centered around dexterous manipulation with anthroporphic 24 degrees of freedom Adroit Hand. These environments were designed for the publication: Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations, RSS2018.

Hand-Manipulation-Suite Tasks (video)