Comparison
holodeck vs awesome-RLHF
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.
Markdown twin · holodeck alternatives · awesome-RLHF alternatives
GraphCanon updated 4d
vs
Trust & integrity
| Signal | holodeck | awesome-RLHF |
|---|---|---|
| Maintenance | Dormant (1623d since push) As of 3w · github_public_v1 | Steady (89d since push) As of 4d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Organization account As of 4d · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- holodeck
- High Fidelity Simulator for Reinforcement Learning and Robotics Research
- awesome-RLHF
- A curated list of reinforcement learning with human feedback resources (continually updated)
Stars
- holodeck
- 597
- awesome-RLHF
- 4.4k
Forks
- holodeck
- 42
- awesome-RLHF
- 258
Open issues
- holodeck
- 52
- awesome-RLHF
- 6
Language
- holodeck
- Python
- awesome-RLHF
- -
Adopt for
- holodeck
- 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
- 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
- holodeck
- -
- awesome-RLHF
- -
Runtime
- holodeck
- -
- awesome-RLHF
- -
License
- holodeck
- MIT
- awesome-RLHF
- Apache-2.0
Last pushed
- holodeck
- Feb 19, 2022
- awesome-RLHF
- May 20, 2026
Categories
- holodeck
- Computer Vision, Model Training
- awesome-RLHF
- Evaluation & Observability, Model Training
Trust and health
Maintenance
- holodeck
- Dormant (18%)
- awesome-RLHF
- Steady (60%)
Days since push
- holodeck
- 1623d
- awesome-RLHF
- 89d
Open issues (now)
- holodeck
- 52
- awesome-RLHF
- 6
Stars delta
- holodeck
- Unknown
- awesome-RLHF
- +9 (30d)
Open issues delta
- holodeck
- Unknown
- awesome-RLHF
- 0 (30d)
Full report
- holodeck
- Trust report
- awesome-RLHF
- Trust report
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
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.
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (BYU-PCCL/holodeck) · observed Jul 31, 2026
- GitHub forks (BYU-PCCL/holodeck) · observed Jul 31, 2026
- Last push (BYU-PCCL/holodeck) · observed Feb 19, 2022
- License file (MIT) · observed Jul 31, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (opendilab/awesome-RLHF) · observed Aug 17, 2026
- GitHub forks (opendilab/awesome-RLHF) · observed Aug 17, 2026
- Last push (opendilab/awesome-RLHF) · observed May 20, 2026
- License file (Apache-2.0) · observed Aug 17, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: holodeck 597 · awesome-RLHF 4.4k (synced Jul 31, 2026).
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 and awesome-RLHF alternatives (holodeck markdown twin, awesome-RLHF markdown twin), 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 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; awesome-RLHF trust report.