The Distracting Control Suite

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},
}

Disclaimer

This is not an official Google product.