gym-dmc 0.3.0rc1

gym-dmc is a gym wrapper around DeepMind Control Suite domains.

Link to other OpenAI Gym Plugins:

• gym-sawyer

• gym-toy-nav

Update Log

• 2024-03-25: Return np.Array from env.render() function

• 2022-01-13: Add space_dtype for overriding the dtype for the state and action spaces. Default to None, need to set to float/np.float32 for pytorch_SAC implementation.

• 2022-01-11: Added a env._get_obs() method to allow one to obtain the observation after resetting the environment. Version: ``v0.2.1``

Installation

The dm_control dependency relies on lower versions of setuptools and wheel. Downgrade to fix the installation error.

pip install setuptools==65.5.0
pip install wheel==0.38.4
pip install gym-dmc

How To Use

Usage pattern:

import gym

env = gym.make("dmc:Pendulum-swingup-v1")

For the full list of environments, you can print:

from dm_control.suite import ALL_TASKS

print(*ALL_TASKS, sep="\n")

# Out[2]: ('acrobot', 'swingup')
#         ('acrobot', 'swingup_sparse')
...

We register all of these environments using the following pattern:

acrobot task “swingup_sparse” becomes dmc:Acrobot-swingup_sparse-v1

You can see the usage pattern in https://github.com/geyang/gym_dmc/blob/master/specs/test_gym_dmc.py:

env = gym.make('dmc:Walker-walk-v1', frame_skip=4, space_dtype=np.float32)
assert env.action_space.dtype is np.float32
assert env.observation_space.dtype is np.float32

env = gym.make('dmc:Walker-walk-v1', frame_skip=4)
assert env._max_episode_steps == 250
assert env.reset().shape == (24,)

env = gym.make('dmc:Walker-walk-v1', from_pixels=True, frame_skip=4)
assert env._max_episode_steps == 250

env = gym.make('dmc:Cartpole-balance-v1', from_pixels=True, frame_skip=8)
assert env._max_episode_steps == 125
assert env.reset().shape == (3, 84, 84)

env = gym.make('dmc:Cartpole-balance-v1', from_pixels=True, frame_skip=8, channels_first=False)
assert env._max_episode_steps == 125
assert env.reset().shape == (84, 84, 3)

env = gym.make('dmc:Cartpole-balance-v1', from_pixels=True, frame_skip=8, channels_first=False, gray_scale=True)
assert env._max_episode_steps == 125
assert env.reset().shape == (84, 84, 1)

Note, the ``max_episode_steps`` is calculated based on the ``frame_skip``. All DeepMind control domains terminate after 1000 simulation steps. So for frame_skip=4, the max_episode_steps should be 250.

Built with :heart: by Ge Yang