--- title: "holodeck vs l2r" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/byu-pccl-holodeck-vs-learn-to-race-l2r" tools: ["byu-pccl-holodeck", "learn-to-race-l2r"] --- # holodeck vs l2r *GraphCanon updated Aug 1, 2026* ## Verdict Pick holodeck if holodeck is a high-fidelity simulator for reinforcement learning and robotics research in Python using Unreal Engine, suited for creating detailed simulated environments; pick l2r if l2R is an open-source platform for reinforcement learning in autonomous racing simulators. [holodeck](https://holodeck.cs.byu.edu) reports 597 GitHub stars, 42 forks, and 52 open issues, last pushed Feb 19, 2022. [l2r](https://learn-to-race.org) has 178 stars, 17 forks, and 10 open issues, last pushed Dec 20, 2023. Figures are from public GitHub metadata via [holodeck's repository](https://github.com/BYU-PCCL/holodeck) and [l2r's repository](https://github.com/learn-to-race/l2r). | | [holodeck](/tools/byu-pccl-holodeck.md) | [l2r](/tools/learn-to-race-l2r.md) | | --- | --- | --- | | Tagline | High Fidelity Simulator for Reinforcement Learning and Robotics Research | Open-source reinforcement learning environment for autonomous racing | | Stars | 597 | 178 | | Forks | 42 | 17 | | Open issues | 52 | 10 | | Language | Python | Python | | Adopt for | Holodeck is a high-fidelity simulator for reinforcement learning and robotics research in Python using Unreal Engine, suited for creating detailed simulated environments. | L2R is an open-source platform for reinforcement learning in autonomous racing simulators. | | Persona | - | - | | Runtime | - | - | | License | MIT | GPL-2.0 license ensures freedom to run, study, change and redistribute the software under specific conditions. | | Categories | Computer Vision, Model Training | Computer Vision, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [holodeck](/tools/byu-pccl-holodeck.md) | [l2r](/tools/learn-to-race-l2r.md) | | --- | --- | --- | | Days since push | 1623d | 954d | | Open issues (now) | 52 | 10 | | Full report | [trust report](/tools/byu-pccl-holodeck/trust.md) | [trust report](/tools/learn-to-race-l2r/trust.md) | ## Shared compatibility - **Python**: [holodeck](/tools/byu-pccl-holodeck.md) - Python runtime; [l2r](/tools/learn-to-race-l2r.md) - Python runtime ## Decision facts: holodeck - **Adopt for:** Holodeck is a high-fidelity simulator for reinforcement learning and robotics research in Python using Unreal Engine, suited for creating detailed simulated environments. ## Decision facts: l2r - **Requirements:** 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 - **Adopt for:** L2R is an open-source platform for reinforcement learning in autonomous racing simulators. - **License detail:** GPL-2.0 license ensures freedom to run, study, change and redistribute the software under specific conditions. ## Choose when ### Choose holodeck if… - License: holodeck is MIT, l2r is GPL-2.0. - Tags unique to holodeck: computer-vision, drones, robotics, simulator. - - When you need to simulate complex robotics scenes in high fidelity that mirror real-world scenarios accurately ### Choose l2r if… - License: l2r is GPL-2.0, holodeck is MIT. - Requirements: 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. - Tags unique to l2r: arrival-simulator, autonomous-driving, deep-learning. - l2r ships Docker support for self-hosted deployment. - When your project includes developing AI systems for autonomous vehicle simulation with a focus on racing environments ## When NOT to use holodeck - - If your project is limited to simpler scenarios where less detailed simulations are acceptable for training reinforcement learning models - - For projects with constraints on computing resources since Unreal Engine can be demanding and this limits its use in resource-constrained environments. ## When NOT to use l2r - 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 ## Common questions ### What is the difference between holodeck and l2r? holodeck: High Fidelity Simulator for Reinforcement Learning and Robotics Research. l2r: Open-source reinforcement learning environment for autonomous racing. See the comparison table for live GitHub stats and shared categories. ### When should I choose holodeck over l2r? Choose holodeck over l2r when License: holodeck is MIT, l2r is GPL-2.0; Tags unique to holodeck: computer-vision, drones, robotics, simulator; - When you need to simulate complex robotics scenes in high fidelity that mirror real-world scenarios accurately. ### When should I choose l2r over holodeck? Choose l2r over holodeck when License: l2r is GPL-2.0, holodeck is MIT; Requirements: 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; Tags unique to l2r: arrival-simulator, autonomous-driving, deep-learning; l2r ships Docker support for self-hosted deployment; When your project includes developing AI systems for autonomous vehicle simulation with a focus on racing environments. ### When should I avoid holodeck? - If your project is limited to simpler scenarios where less detailed simulations are acceptable for training reinforcement learning models - For projects with constraints on computing resources since Unreal Engine can be demanding and this limits its use in resource-constrained environments. ### When should I avoid l2r? 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 ### Is holodeck or l2r more popular on GitHub? holodeck has more GitHub stars (597 vs 178). Stars measure visibility, not whether either tool fits your constraints. ### Are holodeck and l2r open source? Yes - both are open-source projects on GitHub (holodeck: MIT, l2r: GPL-2.0). ### Where can I find alternatives to holodeck or l2r? GraphCanon lists graph-backed alternatives at [holodeck alternatives](/tools/byu-pccl-holodeck/alternatives) and [l2r alternatives](/tools/learn-to-race-l2r/alternatives) ([holodeck markdown twin](/tools/byu-pccl-holodeck/alternatives.md), [l2r markdown twin](/tools/learn-to-race-l2r/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/byu-pccl-holodeck-vs-learn-to-race-l2r.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, holodeck or l2r? holodeck: Dormant. l2r: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for holodeck and l2r? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [holodeck trust report](/tools/byu-pccl-holodeck/trust); [l2r trust report](/tools/learn-to-race-l2r/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=byu-pccl-holodeck`](/api/graphcanon/graph?tool=byu-pccl-holodeck) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_