{"data":{"slug":"learn-to-race-l2r","name":"l2r","tagline":"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. Thes","github_url":"https://github.com/learn-to-race/l2r","owner":"learn-to-race","repo":"l2r","owner_avatar_url":"https://avatars.githubusercontent.com/u/87724264?v=4","primary_language":"Python","stars":177,"forks":16,"topics":["ai","arrival-simulator","artificial-intelligence","autonomous-driving","autonomous-racing","computer-vision","constrained-mdps","deep-learning","deep-reinforcement-learning","l2r","learn-to-race","machine-learning","reinforcement-learning","research","robotics","safe-reinforcement-learning","simulator"],"archived":false,"github_pushed_at":"2023-12-20T18:08:08+00:00","maintenance_label":"Dormant","url":"https://www.graphcanon.com/tools/learn-to-race-l2r","markdown_url":"https://www.graphcanon.com/tools/learn-to-race-l2r.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/learn-to-race-l2r","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=learn-to-race-l2r","description":"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.","homepage_url":"https://learn-to-race.org","license":"GPL-2.0","open_issues":10,"watchers":9,"ai_summary":null,"readme_excerpt":"## Requirements\n\n**Python:** We use Learn-to-Race with Python 3.8+.\n\n**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.\n\n**Docker:** Commonly, the racing simulator runs in a [Docker](https://www.docker.com/get-started) container.\n\n**Container GPU Access:** If running the simulator in a container, the container needs access to the GPU, so [nvidia-container-runtime](https://github.com/NVIDIA/nvidia-container-runtime) is also required.\n\n---\n\n## Installation\n\nDue 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.\n\n1. Request access to the Racing simulator: https://www.aicrowd.com/challenges/learn-to-race-autonomous-racing-virtual-challenge\n\nWe 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:\n\n```bash\n$ docker load < arrival-sim-image.tar.gz\n```\n\n2. Download the source code from this repository and install the package requirements. We recommend using a virtual environment:\n\n```bash\n$ conda create -n l2r python=3.6\n$ conda activate                  # activate the environment\n(l2r) $ pip3 install git+https://github.com/learn-to-race/l2r.git@aicrowd-environment\n```","github_created_at":"2021-07-20T20:15:09+00:00","created_at":"2026-07-11T12:34:06.483419+00:00","updated_at":"2026-07-11T12:34:14.413314+00:00","categories":[{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"},{"slug":"ai-agents","name":"AI Agents","url":"https://www.graphcanon.com/categories/ai-agents","markdown_url":"https://www.graphcanon.com/categories/ai-agents.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/ai-agents"},{"slug":"inference-serving","name":"Inference & Serving","url":"https://www.graphcanon.com/categories/inference-serving","markdown_url":"https://www.graphcanon.com/categories/inference-serving.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/inference-serving"}],"tags":[{"slug":"autonomous-racing","name":"autonomous-racing"},{"slug":"arrival-simulator","name":"arrival-simulator"},{"slug":"deep-learning","name":"deep-learning"},{"slug":"constrained-mdps","name":"constrained-mdps"},{"slug":"ai","name":"ai"},{"slug":"artificial-intelligence","name":"artificial-intelligence"},{"slug":"autonomous-driving","name":"autonomous-driving"},{"slug":"computer-vision","name":"computer-vision"}],"trust":{"provenance":{"is_fork":false,"github_id":387904005,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-07-11T12:34:07.185Z","maintenance":{"label":"Dormant","score":18,"methodology":"github_public_v1","releases_90d":0,"days_since_push":933,"last_release_at":null},"security_summary":{"status":"findings","scanner":"osv@v1","low_count":118,"high_count":0,"last_scan_at":"2026-07-11T12:34:08.111Z","medium_count":0,"scan_profile":"deps","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-07-11T12:34:07.632Z"},"deploy":{"source":"dockerfile:docker-compose.yml","self_host":true,"observed_at":"2026-07-11T12:34:07.632Z","managed_saas":false},"languages":{"value":["python"],"source":"github.language+pyproject.toml","observed_at":"2026-07-11T12:34:07.632Z"},"has_docker":{"value":true,"source":"dockerfile:docker-compose.yml","observed_at":"2026-07-11T12:34:07.632Z"},"license_spdx":{"value":"GPL-2.0","source":"github.license","observed_at":"2026-07-11T12:34:07.632Z"}}}}