l2r
Enrichment pendingOpen-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. Thes
GraphCanon updated today · GitHub synced today
Trust & integrity
Full report- Maintenance
- Dormant (933d since push)
- As of today · Source: github_public_v1
- Provenance
- Not a fork · Organization account
- As of today · Source: github_public_v1
- Security (OSV)
- 118 low (118 low)
- As of today · Source: osv@v1
Public GitHub metadata and optional OSV dependency scans. Signals, not a guarantee. Trust methodology.
Overview
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.
Capability facts
- Deploy
- Self-host
Source: dockerfile:docker-compose.yml · Jul 11, 2026
- Docker
- Dockerfile present
Source: dockerfile:docker-compose.yml · Jul 11, 2026
- Languages
- python
Source: github.language+pyproject.toml · Jul 11, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Jul 11, 2026)
**Python:** We use Learn-to-Race with Python 3.8+.Source link
Tags
README
Requirements
Python: We use Learn-to-Race with Python 3.8+.
Graphics Hardware: An Nvidia graphics card & associated drives is required. An Nvidia 970 GTX graphics card is minimally sufficient to simply run the simulator, but a better card is recommended.
Docker: Commonly, the racing simulator runs in a Docker container.
Container GPU Access: If running the simulator in a container, the container needs access to the GPU, so nvidia-container-runtime is also required.
Installation
Due to the container GPU access requirement, this installation assumes a Linux operating system. If you do not have a Linux OS, we recommend running Learn-to-Race on a public cloud instance that has a sufficient GPU.
- Request access to the Racing simulator: https://www.aicrowd.com/challenges/learn-to-race-autonomous-racing-virtual-challenge
We recommmend running the simulator as a Python subprocess which simply requires that you specify the path of the simulator in the env_kwargs.controller_kwargs.sim_path of your configuration file. Alternatively, you can run the simulator as a Docker container by setting env_kwargs.controller_kwargs.start_container to True. If you prefer the latter, you can load the docker image as follows:
$ docker load < arrival-sim-image.tar.gz
- Download the source code from this repository and install the package requirements. We recommend using a virtual environment:
$ conda create -n l2r python=3.6
$ conda activate # activate the environment
(l2r) $ pip3 install git+https://github.com/learn-to-race/l2r.git@aicrowd-environment