* Support kwargs in gym.make
We avoided adding kwargs for a long time in order to encourage people
to statically register their environment definitions. However, over
time we've found a few important use-cases for kwargs, such as:
- Runtime-specific objects, such as which GPU to run the environment
on
- Parametrized environments, which can have an infinite number of meaningful
variants
The latter breaks the invariant that the environment ID alone
determines the semantics of the environment, but it's an advanced
use-case and such users should be able to manage this on their own.
* Export the register method so it's easier for external users to register environments
* Improve kwargs test
Binding `__contains__` to the Space class' contains method this way prevents overriding it in subclasses, which leads to `NotImplementedException`s when trying to do things like `2 in gym.spaces.Discrete(2)`.
- enable atari env to use full action set of 18
- modify rendering.py to prevent some bad stack traces upon closing
- enable dict space to be more concisely specified as spaces.Dict(a=Box(…),…)
* Fixed rendering to properly display modifications to length etc
* 'self.length is half the pole's length'
* Explicitly base viewer size on Acrobot link size
* Made correct polygon rendering work for different link lengths
* remove vertical camera motion on env.reset()
* In MuJoCo envs, don't reset viewers on environment reset. If the user moves around the camera in the viewer, allow them to keep the camera wherever they put it.
* Docstring update.
* Updated order Calculation
Updated order of `x` and `theta` calculations so that they are no longer one timestamp behind `x_dot` and `theta_dot`.
https://github.com/openai/gym/issues/1018
* Added semi-implicit euler option
* Got implicit and standard euler mixed up
* switched default option
resolves -> WARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.
to my knowledge, does not change any behavior for this environment
* pass device_id=-1 when rendering mujoco in rgb_array mode
* trying ubuntu18.04 build
* use travis build matrix
* use travis build matrix
* use travis build matrix
* use fix 18.04 installation steps
* use fix 18.04 installation steps
* try py3 instead of py35
* trying the 14.04 build; cleaning up the 16.04 package list
* fixes to dockerfile.14.04
* fixes to dockerfile.14.04
* fixes to dockerfile.14.04
* fixes to dockerfile.14.04
* fixes to dockerfile.14.04
* fixing system package list for 14.04
* 16.04 build working
* clean up test.dockerfile.16.04
* adding python-numpy system package
* add patchelf
* remove python-numpy from 16.04 dockerfile
* 14.04 does not use mujoco (patchelf problem)
* cleaning up 18.04 dockerfile
* added comment to 14.04 dockerfile
* fix syntax in 14.04 dockerfile
* being fancy and switching from apt-get to apt command in 18.04 dockerfile
* update README.rst to use apt (instead of apt-get) on ubuntu 18.04
* Add depth rendering from camera
Allow the mujoco renderer to return the depth image using the `depth_array` mode.
It follows the `rgb_array` extraction method.
* Depth data is a 2d array.
* Add depth_array to render modes.
