# Acknowledgments

### People

We would like to thank members of the [Stanford PAIR Group](http://pair.stanford.edu/) for their support and feedback on this project. These people in particular have made the following contributions at different stages of this project:

- [Rohun Kulkarni](https://www.linkedin.com/in/rohunkulkarni/) (assistance with collecting real robot datasets and running real robot experiments)
- [Albert Tung](https://www.linkedin.com/in/albert-tung3/) (assistance with collecting simulation datasets using the [RoboTurk](https://roboturk.stanford.edu/) system)
- [Fei Xia](http://fxia.me/) ([egl_probe](https://github.com/StanfordVL/egl_probe) library, which helped us run experiments on lab clusters)
- [Jim Fan](https://twitter.com/drjimfan?lang=en) (providing support for running experiments on lab clusters)

### Codebases

- Our Config class (see `config/config.py`) was adapted from [addict](https://github.com/mewwts/addict).
- The [BCQ](https://github.com/sfujim/BCQ),  [CQL](https://github.com/aviralkumar2907/CQL), and [TD3-BC](https://github.com/sfujim/TD3_BC) author-provided implementations were used as a reference for our implementations.
- The `TanhWrappedDistribution` class in `models/distributions.py` was adapted from [rlkit](TanhWrappedDistribution).
- Support for training distributional critics (see `BCQ_Distributional` in `algos/bcq.py`) was adapted from [Acme](https://github.com/deepmind/acme). It also served as a useful reference for implementing Gaussian Mixture Model (GMM) policies.

We wholeheartedly welcome the community to contribute to our project through issues and pull requests. New contributors will be added to the list above.