https://github.com/geyang/gym-distracting-control

this is a packaged version of the distracting control suite from Stone et al.
https://github.com/geyang/gym-distracting-control

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this is a packaged version of the distracting control suite from Stone et al.

• Host: GitHub
• URL: https://github.com/geyang/gym-distracting-control
• Owner: geyang
• Created: 2021-05-06T19:45:23.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2022-06-20T09:01:59.000Z (over 2 years ago)
• Last Synced: 2024-04-14T15:11:50.955Z (9 months ago)
• Language: Python
• Size: 1.68 MB
• Stars: 5
• Watchers: 2
• Forks: 4
• Open Issues: 2
• Metadata Files:
  • Readme: README

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README

        
The Distracting Control Suite

=============================

This is a packaged version of the ``distracting_control`` suite from

*Stone et al*. We provide OpenAI gym bindings, to make the original code-block

base easier to use.

Getting Started

---------------

.. code-block:: bash

   pip install distracting_control

Then in your python script:

.. code-block:: python

   import gym

   env = gyn.make('gdc:Hopper-hop-easy-v1', from_pixel=True)

   obs = env.reset()

   doc.figure(obs, "figures/hopper_readme.png?raw=true")

Detailed API

------------

Take a look at the test file in the `https://github.com/geyang/distracting_control/blob/master/specs `__ folder, and

the source code-block. DeepMind control has a lot of low level binding burried

in the source code-block.

.. code-block:: python

   def test_max_episode_steps():

       env = gym.make('distracting_control:Walker-walk-easy-v1')

       assert env._max_episode_steps == 250

   def test_flat_obs():

       env = gym.make('distracting_control:Walker-walk-easy-v1', frame_skip=4)

       env.env.env.env.observation_spec()

       assert env.reset().shape == (24,)

   def test_frame_skip():

       env = gym.make('distracting_control:Walker-walk-easy-v1', from_pixels=True, frame_skip=8)

       assert env._max_episode_steps == 125

   def test_channel_first():

       env = gym.make('distracting_control:Walker-walk-easy-v1', from_pixels=True, channels_first=True)

       assert env.reset().shape == (3, 84, 84)

   def test_channel_last():

       env = gym.make('distracting_control:Walker-walk-easy-v1', from_pixels=True, frame_skip=8, channels_first=False)

       assert env._max_episode_steps == 125

       assert env.reset().shape == (84, 84, 3)

Important Changes from *Stone et al*

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

1. [planned] remove tensorflow dependency

2. [planned] increase ground floor transparency in ``Hopper``

Original README

---------------

``distracting_control`` extends ``dm_control`` with static or dynamic

visual distractions in the form of changing colors, backgrounds, and

camera poses. Details and experimental results can be found in our

`paper `__.

Requirements and Installation

-----------------------------

-  Clone this repository

-  ``sh run.sh``

-  Follow the instructions and install

   `dm_control `__.

   Make sure you setup your MuJoCo keys correctly.

-  Download the `DAVIS 2017

   dataset `__. Make

   sure to select the 2017 TrainVal - Images and Annotations (480p). The

   training images will be used as distracting backgrounds.

Instructions

------------

-  You can run the ``distracting_control_demo`` to generate sample

   images of the different tasks at different difficulties:

   ::

      python distracting_control_demo --davis_path=$HOME/DAVIS/JPEGImages/480p/

      --output_dir=/tmp/distrtacting_control_demo

-  As seen from the demo to generate an instance of the environment you

   simply need to import the suite and use ``suite.load`` while

   specifying the ``dm_control`` domain and task, then choosing a

   difficulty and providing the dataset_path.

-  Note the environment follows the dm_control environment APIs.

Paper

-----

If you use this code-block, please cite the accompanying

`paper `__ as:

::

   @article{stone2021distracting,

         title={The Distracting Control Suite -- A Challenging Benchmark for Reinforcement Learning from Pixels}, 

         author={Austin Stone and Oscar Ramirez and Kurt Konolige and Rico Jonschkowski},

         year={2021},

         journal={arXiv preprint arXiv:2101.02722},

   }

Disclaimer

----------

This is not an official Google product.