An RL library in PyTorch embedded within the PyTorch Lightning framework. Aiming to provide a comprehensive platform for the development and testing of RL algorithms.
Branches should be using one of these groups to start with: wip - Works in progress; stuff I know won't be finished soon (like a release) feat - Feature I'm adding or expanding bug - Bug fix or experiment junk - Throwaway branch created to experiment
Groups should be split using "-". For example: junk-id-test
Skip the ID for now, since we don't have unique id generation in trello. Please add a label with the branch name to the card.
Commit your work as often as possible. Push the changes in batches. Each commit should have one line for each feature/change added.
Example of commit:
File x.py added
Gradient clipping fixed
Feature Y implemented
When finished with your work, create a pull request between the relevant branches. This would be discussed in our next meeting. Please add a label to trello to mark cards in need of review.
Any script you add in the tests directory with the name test_….py like the script already there: test_MLP_output_shape.py will run automatically when you merge to master. Or when you create a pull requests.
So just come up with a test, add it to the tests folder and voila.
When you are developing on the command line and you want to run the tests locally, go to the tests directory and run pytest on the command line. This will run all the tests in the directory.
You would need to install the pytest module from pip first of course.
To cite this repository in publications:
@misc{squiRL,
author = {Khalil, Ahmed and Anca, Mihai and Thomas, Jonathan},
title = {squiRL},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/squiReinforcementLearning/squiRL}},
}