First off, thanks for your interest to cotribute to SCOPE-RL!
We are doing our best to make this project even better. However, we recognize that there is ample room for improvement. We need your help to make this project even better. Let's make the best Off-Policy Evaluation software for Reinforcement Learning together!
We prepare some conventions as follows:
Code is formatted with black, and coding style is checked with flake8.
After installing black, you can perform code formatting by the following command:
# perform formatting recursively for the files under the current dir
$ black .After installing flake8, you can check the coding style by the following command:
# perform checking of the coding style
$ flake8 .We are currently working on implementing unit testing using pytest as the testing framework. We greatly appreciate any helps for adding the test codes. If you are interested in working on the test codes, please contact: hk844@cornell.edu
# perform all the tests under the tests directory
$ pytest .SCOPE-RL uses Github Actions to perform continuous integration.