learn_in_dmc.md

Install Gymnasium MuJoCo/DMC environments

First, you should install the proper environments:

• MuJoCo (Gymnasium): you need to install extra packages: use the pip install -e .[mujoco] command to have available all the MuJoCo environments provided by Gymnasium (https://gymnasium.farama.org/environments/mujoco/).
• DMC: you have to install extra packages with the following command: pip install -e .[dmc]. (https://github.com/deepmind/dm_control).

Install OpenGL rendering backands packages

MuJoCo/DMC supports three different OpenGL rendering backends: EGL (headless), GLFW (windowed), and OSMesa (headless). For each of them, you need to install some packages:

• GLFW: sudo apt-get install libglfw3 libglew2.2
• EGL: sudo apt-get install libglew2.2
• OSMesa: sudo apt-get install libgl1-mesa-glx libosmesa6 In order to use one of these rendering backends, you need to set the MUJOCO_GL environment variable to "glfw", "egl", "osmesa", respectively.

Note

The libglew2.2 could have a different name, based on your OS (e.g., libglew2.2 is for Ubuntu 22.04.2 LTS).

It could be necessary to install also the PyOpenGL-accelerate package with the pip install PyOpenGL-accelerate command and the mesalib package with the conda install conda-forge::mesalib command.

For more information: https://github.com/deepmind/dm_control and https://mujoco.readthedocs.io/en/stable/programming/index.html#using-opengl.

MuJoCo Gymnasium

In order to train your agents on the MuJoCo environments provided by Gymnasium, it is sufficient to select the MuJoCo environment (env=mujoco) and set the env.id to the name of the environment you want to use. For instance, "Walker2d-v4" if you want to train your agent in the walker walk environment.

python sheeprl.py exp=dreamer_v3 env=mujoco env.id=Walker2d-v4 algo.cnn_keys.encoder=[rgb]

DeepMind Control

In order to train your agents on the DeepMind control suite, you have to select the DMC environment (env=dmc) and set the domain and the task of the environment you want to use. A list of the available environments can be found here. For instance, if you want to train your agent on the walker walk environment, you need to set the env.wrapper.domain_name to "walker" and the env.wrapper.task_name to "walk".

python sheeprl.py exp=dreamer_v3 env=dmc env.wrapper.domain_name=walker env.wrapper.task_name=walk algo.cnn_keys.encoder=[rgb]

Note

By default the env.sync_env parameter is set to True. We recommend not changing this value for the MuJoCo environments to work properly.