Awesome
dm_control2gym
dm_control2gym is a small wrapper to make DeepMind Control Suite environments available for OpenAI Gym.
Installation
$ git clone https://github.com/martinseilair/dm_control2gym/
$ cd dm_control2gym
$ pip install .
Tested with Python 3.5.2 and Ubuntu 16.04.
Quick start
import gym
import dm_control2gym
# make the dm_control environment
env = dm_control2gym.make(domain_name="cartpole", task_name="balance")
# use same syntax as in gym
env.reset()
for t in range(1000):
observation, reward, done, info = env.step(env.action_space.sample()) # take a random action
env.render()
Short documentation
Spaces and Specs
The dm_control specs are converted to spaces. If there is only one entity in the observation dict, the original shape is used for the corresponding space. Otherwise, the observations are vectorized and concatenated. Note, that the pixel observation is processed separately through the render routine.
The difference between the Discrete
and the corresponding ArraySpec
with type np.int
, is that the domain ArraySpec
is arbitrary and of that the domain of Discrete
always starts at 0. Therefore, the domain is shifted to obtain a valid Discrete
space.
Rendering
Three rendering modes are available by default:
human
: Render scene and show itrgb_array
: Render scene and return it as rgb arrayhuman_rgb_array
: Render scene, show and return it
You can create your own rendering modes before making the environment by:
dm_control2gym.create_render_mode(name, show=True, return_pixel=False, height=240, width=320, camera_id=-1, overlays=(),
depth=False, scene_option=None)
name
: name of rendering modeshow
: rendered image is shownreturn_pixel
: return the rendered image
It is possible to render in different render modes subsequently. Output of several render modes can be visualized at the same time.
Procedurally generated environments
swimmer
:swimmer_n
k
: number of links
stacker
:stack_k
k
: number of boxes (max. 4)
lqr
:lqr_n_m
n
: number of massesm
: number of actuated masses
cartpole
:k_poles
k
: number of polesswing_up
: balance or swing_up task (default=TRUE)sparse
: use sparse reward variant (default=FALSE)
Example
env = dm_control2gym.make(domain_name="cartpole", task_name="k_poles",task_kwargs={'k':10})
What's new
2018-01-25: Optimized registering process (thanks to rejuvyesh), added access to procedurally generated environments, added render mode functionality