Link to other OpenAI Gym Plugins:
gym-sawyer
gym-toy-nav
- 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
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 ./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 ❤️ by Ge Yang