This package contains:

• A set of Python Reinforcement Learning environments powered by the MuJoCo physics engine. See the suite subdirectory.

• Libraries that provide Python bindings to the MuJoCo physics engine.

If you use this package, please cite our accompanying accompanying tech report.

Follow these steps to install dm_control:

1. Download MuJoCo Pro 1.50 from the download page on the MuJoCo website. MuJoCo Pro must be installed before dm_control, since dm_control's install script generates Python ctypes bindings based on MuJoCo's header files. By default, dm_control assumes that the MuJoCo Zip archive is extracted as ~/.mujoco/mjpro150.

2. Install the dm_control Python package by running pip install git+git://github.com/deepmind/dm_control.git (PyPI package coming soon) or by cloning the repository and running pip install /path/to/dm_control/ At installation time, dm_control looks for the MuJoCo headers from Step 1 in ~/.mujoco/mjpro150/include, however this path can be configured with the headers-dir command line argument.

3. Install a license key for MuJoCo, required by dm_control at runtime. See the MuJoCo license key page for further details. By default, dm_control looks for the MuJoCo license key file at ~/.mujoco/mjkey.txt.

4. If the license key (e.g. mjkey.txt) or the shared library provided by MuJoCo Pro (e.g. libmujoco150.so or libmujoco150.dylib) are installed at non-default paths, specify their locations using the MJKEY_PATH and MJLIB_PATH environment variables respectively.

1. The above instructions using pip should work, provided that you use a Python interpreter that is installed by Homebrew (rather than the system-default one).

2. To get OpenGL working, install the glfw package from Homebrew by running brew install glfw.

3. Before running, the DYLD_LIBRARY_PATH environment variable needs to be updated with the path to the GLFW library. This can be done by running export DYLD_LIBRARY_PATH=$(brew --prefix)/lib:$DYLD_LIBRARY_PATH.

from dm_control import suite

# Load one task:
env = suite.load(domain_name="cartpole", task_name="swingup")

# Iterate over a task set:
for domain_name, task_name in suite.BENCHMARKING:
  env = suite.load(domain_name, task_name)

# Step through an episode and print out reward, discount and observation.
action_spec = env.action_spec()
time_step = env.reset()
while not time_step.last():
  action = np.random.uniform(action_spec.minimum,
                             action_spec.maximum,
                             size=action_spec.shape)
  time_step = env.step(action)
  print(time_step.reward, time_step.discount, time_step.observation)

See our tech report for further details.

Below is a video montage of solved Control Suite tasks, with reward visualisation enabled.