Skip to content

Latest commit

 

History

History
92 lines (66 loc) · 3.59 KB

README.md

File metadata and controls

92 lines (66 loc) · 3.59 KB
Raw

dm_control: The DeepMind Control Suite and Package

all domains

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.

Installation and requirements

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.

Additional instructions for Linux

Install GLFW and GLEW through your Linux distribution's package manager. For example, on Debian and Ubuntu, this can be done by running sudo apt-get install libglfw3 libglew2.0.

Additional instructions for Homebrew users on macOS

  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.

Control Suite quickstart

from dm_control import suite
import numpy as np

# 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.

Illustration video

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

Video montage