--- title: "holodeck vs awesome-RLHF" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/byu-pccl-holodeck-vs-opendilab-awesome-rlhf" tools: ["byu-pccl-holodeck", "opendilab-awesome-rlhf"] --- # holodeck vs awesome-RLHF *GraphCanon updated Aug 17, 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 awesome-RLHF if awesome-RLHF is a curated resource list focusing on reinforcement learning with human feedback (RLHF), which is crucial for refining large language models through interactive training methods. [holodeck](https://holodeck.cs.byu.edu) reports 597 GitHub stars, 42 forks, and 52 open issues, last pushed Feb 19, 2022. [awesome-RLHF](https://github.com/opendilab/awesome-RLHF) has 4.4k stars, 258 forks, and 6 open issues, last pushed May 20, 2026. Figures are from public GitHub metadata via [holodeck's repository](https://github.com/BYU-PCCL/holodeck) and [awesome-RLHF's repository](https://github.com/opendilab/awesome-RLHF). | | [holodeck](/tools/byu-pccl-holodeck.md) | [awesome-RLHF](/tools/opendilab-awesome-rlhf.md) | | --- | --- | --- | | Tagline | High Fidelity Simulator for Reinforcement Learning and Robotics Research | A curated list of reinforcement learning with human feedback resources (continually updated) | | Stars | 597 | 4,422 | | Forks | 42 | 258 | | Open issues | 52 | 6 | | Language | 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. | awesome-RLHF is a curated resource list focusing on reinforcement learning with human feedback (RLHF), which is crucial for refining large language models through interactive training methods. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Computer Vision, Model Training | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [holodeck](/tools/byu-pccl-holodeck.md) | [awesome-RLHF](/tools/opendilab-awesome-rlhf.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Steady (60%) | | Days since push | 1623d | 89d | | Open issues (now) | 52 | 6 | | Stars delta | Unknown | +9 (30d) | | Open issues delta | Unknown | 0 (30d) | | Full report | [trust report](/tools/byu-pccl-holodeck/trust.md) | [trust report](/tools/opendilab-awesome-rlhf/trust.md) | ## 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: awesome-RLHF - **Adopt for:** awesome-RLHF is a curated resource list focusing on reinforcement learning with human feedback (RLHF), which is crucial for refining large language models through interactive training methods. ## Choose when ### Choose holodeck if… - License: holodeck is MIT, awesome-RLHF is Apache-2.0. - Tags unique to holodeck: ai, computer-vision, drones, robotics. - Also covers Computer Vision. - - When you need to simulate complex robotics scenes in high fidelity that mirror real-world scenarios accurately ### Choose awesome-RLHF if… - License: awesome-RLHF is Apache-2.0, holodeck is MIT. - Tags unique to awesome-RLHF: deep-learning, depth-reinforcement-learning, human-feedback, large language models. - Also covers Evaluation & Observability. - When you are specifically interested in the resources that pertain to enhancing reinforcement learning algorithms with human feedback for developing advanced AI systems. ## 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 awesome-RLHF - If your focus is exclusively on generic deep-learning or reinforcement-learning resources without the aspect of integrating human feedback into the training process. ## Common questions ### What is the difference between holodeck and awesome-RLHF? holodeck: High Fidelity Simulator for Reinforcement Learning and Robotics Research. awesome-RLHF: A curated list of reinforcement learning with human feedback resources (continually updated). See the comparison table for live GitHub stats and shared categories. ### When should I choose holodeck over awesome-RLHF? Choose holodeck over awesome-RLHF when License: holodeck is MIT, awesome-RLHF is Apache-2.0; Tags unique to holodeck: ai, computer-vision, drones, robotics; Also covers Computer Vision; - When you need to simulate complex robotics scenes in high fidelity that mirror real-world scenarios accurately. ### When should I choose awesome-RLHF over holodeck? Choose awesome-RLHF over holodeck when License: awesome-RLHF is Apache-2.0, holodeck is MIT; Tags unique to awesome-RLHF: deep-learning, depth-reinforcement-learning, human-feedback, large language models; Also covers Evaluation & Observability; When you are specifically interested in the resources that pertain to enhancing reinforcement learning algorithms with human feedback for developing advanced AI systems. ### 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 awesome-RLHF? If your focus is exclusively on generic deep-learning or reinforcement-learning resources without the aspect of integrating human feedback into the training process. ### Is holodeck or awesome-RLHF more popular on GitHub? awesome-RLHF has more GitHub stars (4,422 vs 597). Stars measure visibility, not whether either tool fits your constraints. ### Are holodeck and awesome-RLHF open source? Yes - both are open-source projects on GitHub (holodeck: MIT, awesome-RLHF: Apache-2.0). ### Where can I find alternatives to holodeck or awesome-RLHF? GraphCanon lists graph-backed alternatives at [holodeck alternatives](/tools/byu-pccl-holodeck/alternatives) and [awesome-RLHF alternatives](/tools/opendilab-awesome-rlhf/alternatives) ([holodeck markdown twin](/tools/byu-pccl-holodeck/alternatives.md), [awesome-RLHF markdown twin](/tools/opendilab-awesome-rlhf/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-opendilab-awesome-rlhf.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, holodeck or awesome-RLHF? holodeck: Dormant. awesome-RLHF: Steady. 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 awesome-RLHF? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [holodeck trust report](/tools/byu-pccl-holodeck/trust); [awesome-RLHF trust report](/tools/opendilab-awesome-rlhf/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/_