distracting_control's Introduction

The Distracting Control Suite

This is a copy of the original distracting_control repository, with the only changes being the reorganization of the package to be able to install and easily use in other codes. Additionally, dependency on TensorFlow has been completely removed. Simply clone this repo and run `pip install -e .`.

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, 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},
}