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 settingheight
andwidth
. - Action space normalization bound each action's coordinate into the
[-1, 1]
range. - Setting
frame_skip
argument lets to perform action repeat.
pip install git+git://github.com/kmdanielduan/dmc2gym.git
import gym
from dm_control import suite
from dmc2gym import register_suite, dmc_task2str
register_suite(suite, tag='easy') # register all tasks with tag 'easy' in the suite to gym registry
env_id = dmc_task2str('point_mass', 'easy') # convert to "point_mass-easy-v0"
env = gym.make(env_id, task_kwargs=dict(random=42)) # make environments directly
done = False
obs = env.reset()
while not done:
action = env.action_space.sample()
obs, reward, done, info = env.step(action)