OpenAI Gym wrapper for the DeepMind Control Suite.

A lightweight wrapper around the DeepMind Control Suite that provides the standard OpenAI Gym interface. The wrapper allows to specify the following:

• Reliable random seed initialization that will ensure deterministic behaviour.
• Setting from_pixels=True converts proprioceptive observations into image-based. In additional, you can choose the image dimensions, by setting height and width.
• Action space normalization bound each action's coordinate into the [-1, 1] range.
• Setting frame_skip argument lets to perform action repeat.

Instalation

pip install git+git://github.com/denisyarats/dmc2gym.git

Usage

import dmc2gym

env = dmc2gym.make(domain_name='point_mass', task_name='easy', seed=1)

done = False
obs = env.reset()
while not done:
  action = env.action_space.sample()
  obs, reward, done, info = env.step(action)