A lightweight wrapper around the DeepMind Control Suite that provides the standard OpenAI Gym interface. This updated version is compatible with gym>0.23.0 and has been modified to address incompatibility issues with newer gym versions. The wrapper allows you to:
- Ensure deterministic behavior with reliable random seed initialization.
- Convert proprioceptive observations into image-based observations by setting
from_pixels=True
. Additionally, you can choose the image dimensions by settingheight
andwidth
. - Normalize the action space to bound each action's coordinate within the
[-1, 1]
range. - Perform action repeat by setting the
frame_skip
argument.
pip install git+git://github.com/ItsBean/dmc2gym.git
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)
This updated introduction reflects the changes to the repository, including compatibility with the new gym version and the new GitHub repository owner.