{"data":{"slug":"learn-to-race-l2r","name":"l2r","tagline":"Open-source reinforcement learning environment for autonomous racing","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":178,"forks":17,"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":"An open-source toolset for developing AI systems in the context of autonomous racing via reinforcement learning techniques.","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":"2026-08-01T06:00:30.754054+00:00","categories":[{"slug":"computer-vision","name":"Computer Vision","url":"https://www.graphcanon.com/categories/computer-vision","markdown_url":"https://www.graphcanon.com/categories/computer-vision.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/computer-vision"},{"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"}],"tags":[{"slug":"ai","name":"ai"},{"slug":"arrival-simulator","name":"arrival-simulator"},{"slug":"autonomous-driving","name":"autonomous-driving"},{"slug":"deep-learning","name":"deep-learning"},{"slug":"reinforcement-learning","name":"reinforcement-learning"}],"trust":{"provenance":{"is_fork":false,"github_id":387904005,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-01T06:00:29.939Z","maintenance":{"label":"Dormant","score":18,"methodology":"github_public_v1","releases_90d":0,"days_since_push":954,"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":"2026-08-01T06:00:30.434Z"},"deploy":{"source":"dockerfile:docker-compose.yml","self_host":true,"observed_at":"2026-08-01T06:00:30.434Z","managed_saas":false},"languages":{"value":["python"],"source":"github.language+pyproject.toml","observed_at":"2026-08-01T06:00:30.434Z"},"has_docker":{"value":true,"source":"dockerfile:docker-compose.yml","observed_at":"2026-08-01T06:00:30.434Z"},"license_spdx":{"value":"GPL-2.0","source":"github.license","observed_at":"2026-08-01T06:00:30.434Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":{"notes":["Requires Python 3.8 or higher","Nvidia graphics card and associated drives are necessary, with minimum recommendation of an Nvidia 970 GTX for simulator operation","Installation assumes a Linux operating system. For non-Linux environments, a public cloud instance with GPU is suggested"],"min_ram_gb":null,"requires_docker":true},"constraints":{"min_ram_gb":null,"requires_docker":true},"when_to_use":["When your project includes developing AI systems for autonomous vehicle simulation with a focus on racing environments","For researchers needing a validated environment showcased at major AI conferences like ICCV and IJCAI","If you require an implementation that supports constrained MDPs and safe reinforcement learning techniques"],"when_not_to_use":["When your project does not involve autonomous driving or is not specifically focused on the simulation of racing scenarios","For those who cannot meet the hardware requirements, such as lacking a suitable Nvidia GPU for running the simulator","If you are working in an OS environment that is not Linux, given that proper support and installation steps target Linux systems"],"source":"enrich:decision_facts","observed_at":"2026-07-16T22:26:55.575Z"},"constraint_facets":{"min_ram_gb":null,"requires_docker":true},"decision_summary":[{"label":"Requirements","value":"Requires Docker; Requires Python 3.8 or higher; Nvidia graphics card and associated drives are necessary, with minimum recommendation of an Nvidia 970 GTX for simulator operation; Installation assumes a Linux operating system. For non-Linux environments, a public cloud instance with GPU is suggested"},{"label":"Adopt for","value":"L2R is an open-source platform for reinforcement learning in autonomous racing simulators."},{"label":"License detail","value":"GPL-2.0 license ensures freedom to run, study, change and redistribute the software under specific conditions."}]}}