Open-source reinforcement learning environment for autonomous racing — featured as a conference paper at ICCV 2021 and as the official challenge tracks at both SL4AD@ICML2022 and AI4AD@IJCAI2022. These are the L2R core libraries.
This is a long-overdue overhaul of the repository to prepare l2r for Python packaging and publishing on PyPi. The main features include:
Modifying the structure of the repository to be more package friendly
Refactor of the package to be more testable and easier to understand
Added a handful of unit tests
Added a handful of integration tests with the simulator
Added CI workflows for unit tests and lint checks
Added multiple examples on how to use the environment
Added makefile to make contributing easier
The refactor isn't complete and coverage is not satisfactory, but these changes make the repository more understandable, extendable, and more friendly to other contributors.
This is a long-overdue overhaul of the repository to prepare
l2r
for Python packaging and publishing on PyPi. The main features include:The refactor isn't complete and coverage is not satisfactory, but these changes make the repository more understandable, extendable, and more friendly to other contributors.